Machine Learning & Synthetic Biology

Machine Learning & Synthetic Biology. Recent publications parsed from OpenAlex.

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  1. Echromatin: Developing a Cell-Free Gene Expression Platform Using Targeted Induced Steric Hinderance within DNA Arrays

    Rory J Majule, Kyvalya Reddy, Oren Fox, Christopher N. Takahashi, Jeff Nivala

  2. Echromatin: Developing a Cell-Free Platform for Electronically Controllable Gene Expression

    Rory J Majule, Kyvalya Reddy, Oren Fox, Christopher N. Takahashi, Jeff Nivala

  3. Molecular fossils of an ancestral code revealed by large-scale analysis of tRNAs

    Ash Nova Royo, Paula Martín-Cabezas, Sergey Ovchinnikov, PAUL R. SCHIMMEL, Lluı́s Ribas de Pouplana

  4. Design of readers for pathological PTM proteoforms in Alzheimer's disease

    Kejia Wu, Tiana Schantz, Lemuel Chang, Prarthna Chabria, Elaina Bashaw, Thejesh Reddy Kalatturu, Doo Yeon Kim, An‐Qi Chen, Nathaniel Corley, Asim K. Bera, Alex Kang, Hannah Nguyen, Johnny Mendoza, Xinting Li, Magnus S. Bauer, Misa Baum, Brian C. Kraemer, Yuyu Song, David A. Baker

  5. Supplementary Data 2. Designed and predicted structures of all designs

    Zachary Wu, Josh Abramson, Thomas Frerix, Alexander E. Chu, Ruijie Zhang, Luca Schulz, Amy E. Danson, Tristan O. C. Kwan, Li Kevin Wenliang, Zi‐Qi Li, Rosalia G. Schneider, Jacob Kelly, Ashok Thillaisundaram, Harshnira Patani, Vinícius Zambaldi, Sukhdeep Singh, David La, Masy Domecillo, Ariane Mora, Julia C. Reisenbauer, Yu Zhang, Eliseo Papa, Akvilė Žemgulytė, Yuhan Wu, Augustin Žídek, Jiaxin Shi, Grace Margand, Naila Assem, Kate Stephen, Charles A. Emrich, Peng Liu, John Jumper, Lucy J. Colwell, Demis Hassabis, Rob Fergus, Frances H. Arnold, Pushmeet Kohli, Jue Wang

  6. Logit Averaging During Training Sets a New State of the Art for Non-Invasive Speech BCI

    Miran Özdogan, Gereon Elvers, Michael M. Bronstein, Ōiwi Parker Jones

  7. Multiplex programming of mappable, molecularly tagged genomic deletions

    Aidan Keith, Riza M. Daza, Jonas Koeppel, Hanna Liao, David S. Lee, David K Moraes, Troy A. McDiarmid, Junhong Choi, Xiaoyi Li, Jean‐Benoît Lalanne, Chengxiang Qiu, Sudarshan Pinglay, Jay Shendure

  8. Distribution-Based Regularization for Reward Fine-Tuning Diffusion and Flow Models

    Romeo Passaro, Jeongjae Lee, Jong Chul Ye, Michael M. Bronstein, Yoshua Bengio, Alexander Tong, Danyal Rehman

  9. De novo design of protein binders targeting the human sweet taste receptor as potential sweet proteins

    Saisai Ding, Yi Zhang

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  10. World of SWECraft: Agent Collaboration at Scale via Simulated Software Worlds

    Shannon Zejiang Shen, Yapei Chang, Qiyao Liang, Benjamin Newman, Jenny Liang, Rulin Shao, Ziqiao Ma, Junjie Oscar Yin, Yung-Sung Chuang, Weijia Shi, Yejin Choi, Luke Zettlemoyer, Pang Wei Koh, Mohit Iyyer, Tongshuang Wu, David Alexander Sontag

  11. Atomic functional site scaffolding in biopolymers with RFdiffusion3

    Jasper Butcher, Rohith Krishna, Raktim Mitra, Seth M. Woodbury, Donghyo Kim, Rafael I. Brent, Yanjing Li, Nathaniel Corley, Andrew Favor, An‐Qi Chen, Paul T. Kim, Jonathan Funk, Gabriela Bastidas Reinoso, Kejia Wu, Aiko Muraishi, Helen E. Eisenach, Saman Salike, Simon V. Mathis, Tuscan Rock Thompson, Antonia Ruder, Enisha Sehgal, Andrew Kubaney, Jie Chen, Yuliya Politanska, Brian Coventry, Odin Zhang, Florence J. Hardy, Bo Qiang, Kieran Didi, Maxwell W. Kazman, Lars L. Schaaf, Frank DiMaio, David A. Baker

  12. Interfaces and Modules: A Scalable Representation for Weight-Space Learning across Architectures

    Michał Kozyra, Michael M. Bronstein

  13. Normalizing Flow Maps

    Francesco M. Ruscio, Timothy James Hitge, Fred Zhangzhi Peng, Danyal Rehman, Zander Blasingame, Nicholas M. Boffi, Michael M. Bronstein, Alexander Tong, Avishek Joey Bose

  14. The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation

    Ian Magnusson, Noah A. Smith, Pang Wei Koh

  15. Computational design of metalloproteases

    An‐Qi Chen, Kejia Wu, Hojae Choi, Preetham Venkatesh, Samuel J. Pellock, Nikita Hanikel, Hojeong Shin, Declan Evans, Kieran Didi, Lars L Schaaf, Cristina Díaz‐Perlas, Brian Coventry, Donghyo Kim, Seth M. Woodbury, Pengfei Ji, Shingo Honda, Asim K. Bera, Hannah Nguyen, Alex Kang, Yanqing Wang, Xinting Li, Stacey Gerben, Lemuel Chang, Xiao Yan, Benjamí Oller‐Salvia, Anthony A. Hyman, Donald Hilvert, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  16. De novo design of cysteine proteases

    Hojae Choi, Magnus S. Bauer, Brian Coventry, Preetham Venkatesh, An‐Qi Chen, Donghyo Kim, Asim K. Bera, Alex Kang, Hannah Nguyen, Shayan Sadre, Justin A. Decarreau, Emily Joyce, Bhanumathi Shankaran, Tuscan Rock Thompson, Gabriel Greenstein, Kieran Didi, Lars L Schaaf, Jacob Merle Gershon, Alexander F. Shida, Gyu Rie Lee, Donald Hilvert, Samuel J. Pellock, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  17. Intrinsic Geometry Matching: learning observation-equivariant process geometry

    Manuel Lecha, Michael M. Bronstein, Iolo Jones

  18. LLM-to-Flow Distillation: Teaching Natural Language Goals to Scientific Generators

    Riccardo De Santi, Federico Di Gennaro, Angelo Gnazzo, Sophia Tang, Pranam Chatterjee, Fanny Yang, İsmail İlkan Ceylan, Michael M. Bronstein, Andreas Krause

  19. High-throughput identification of chromatin-opening transcription factors

    Aidan Keith, Shruti Jain, Riza M. Daza, Chengxiang Qiu, Pierre‐Luc Germain, Jay Shendure, Silvia Domcke

  20. Designing Protein Structures from Natural Language

    Joseph Lee, Yanjing Li, Bo Qiang, Lau G, Allyson Ettinger, Noah A. Smith, Rory Donovan-Maiye, David Baker, Hanna Hajishirzi, Pang Wei Koh

  21. Multimodal Co-Generation With Continuous Bitstream Diffusion

    Georgios Batzolis, Georgios Kontos, Bruno Trentini, Pranam Chatterjee, Micha Livne, Mark Girolami, Luca Ambrogioni

  22. A DNA Typewriter records the cell lineage history of a mouse, from zygote to late organogenesis

    Qi Yu, Haedong Kim, Sophie Seidel, James F. Clark, Beth Martin, Kyle O'Connor, Riza M. Daza, Molly J. Gasperini, Jenny F. Nathans, Maggie Lam, Elena Gamo, Shruthi VijayKumar, Lauren M. Kuo, Jean‐Benoît Lalanne, Kamen P. Simeonov, Marion Pepper, Cole Trapnell, Jesse M. Gray, Junhong Choi, Chengxiang Qiu, Jay Shendure

  23. Femtosecond Three-Dimensional Imaging of Single-Protein with Hard X-ray Laser

    Paulrajpillai Lourdu Xavier, Andrew J. Morgan, Johan Bielecki, A. Samanta, Simon Dold, Chufeng Li, Xinyue Gao, Spencer K. Passmore, Ruslan P. Kurta, Tim B. Berberich, Emiliano De Santis, Wenhui Zhang, E Juncheng, F. Koua, Romain Letrun, Luca Gelisio, O. Turkot, E. V. Sobolev, Lukas V. Haas, Jannik Lübke, Jingxuan He, Madeline B. F. Memovich, Kevin Janson, Stefanie Lenzen, Safi Rafie-Zinedine, Mansi Butola, Oleksandr M. Yefanov, F. Trost, Chan Kim, Tomas Popelar, Tokushi Sato, Yevheniy Ovcharenko, Rebecca Boll, Sergey Usenko, Thomas M. Baumann, A. De Fanis, Björn Senfftleben, M. Porro, Andrea Castoldi, Ibrahym Dourki, Cyril Danilevski, Ekaterina S. Round, Christina Schmidt, Huijong Han, Roberto Alvarez, Ervin Chia, Cornelia Cazey, Carolin Seuring, Ruojie Sha, Maia Azubel, David A. Bushnell, Sébastien Boutet, Sergey Ovchinnikov, Adrian P. Mancuso⋈, Michael Meyer, Richard Bean, Anton Barty, Filipe R. N. C. Maia, Richard A. Kirian, Thomas D. Grant, N. Duane Loh, Carl Caleman, Erik G. Marklund, Nadia Zatsepin, Andrew V. Martin, S. Bajt, Roger D. Kornberg, Jochen Küpper, Henry N. Chapman

  24. Machine learning–assisted directed evolution of plant Rubisco

    Julie Lynn McDonald, Jiacheng Lin, Yunlong Zhao, Brian Hie, Rosemary J. Birch, Mary A. Gehring, Bryan D. Bryson, Spencer Michael Whitney, Matthew Donald Shoulders, Robert H. Wilson

  25. Highly multiplexed mammalian metabolic engineering with a shotgun approach

    Julie Trolle, Sara Sessa, Aleksandra M. Wudzinska, Mark Grivainis, Katerina Rincones, Tori C. Rodrick, Drew R. Jones, David Fenyö, Sudarshan Pinglay, Jef D. Boeke

  26. Complex enzyme active site scaffolding by iteratively detuned catalytic guidance

    Tingli Qian, Weijin Wang, Shilong Gao, Aiko Muraishi, Beibei Zhao, Davi Nakajima An, Fanglei Xue, Max Kazman, Johnny Mendoza, Hannah Nguyen, Asim Bera, Alex Kang, Avery Yang, Wenzhao Dai, Jia Liang, Yujia Wang, Xiongyi Huang, Frank DiMaio, David A. Baker

  27. microsoft/dayhoff: v.0.1.3

    Sarah Alamdari, Kevin Yang

  28. PepRePs: Peptide-Retargeted Phosphatases Designed with Generative Language Models

    Lauren Hong, Tai-Chen Ho, Yi-Shiuan Tseng, Sophia Vincoff, Tong Chen, Thomas Connor Donahue, Matthew P. DeLisa, Po‐Han Chen, Pranam Chatterjee

  29. Global Transport Couplings for Classifier-Free Guided Flows

    Katarina Petrović, Zander Blasingame, Danyal Rehman, İsmail İlkan Ceylan, Michael M. Bronstein, Stephen Y. Zhang, Lazar Atanackovic, Alexander Tong

  30. Designing enzymes for new-to-nature chemistry and non-natural substrates with AlphaProtein Novo

    Zachary Wu, Josh Abramson, Thomas Frerix, Alexander E. Chu, Ruijie K. Zhang, Luca Schulz, Amy E. Danson, Tristan O. C. Kwan, Li Kevin Wenliang, Jacob Kelly, Zi‐Qi Li, Rosalia G. Schneider, Ashok Thillaisundaram, Harshnira Hitesh Patani, Vinícius Zambaldi, Sukhdeep Singh, David La, Masy Domecillo, Ariane Mora, Julia C. Reisenbauer, Yu Zhang, Eliseo Papa, Akvilė Žemgulytė, Yuhan Wu, Augustin Žídek, Jiaxin Shi, Grace Margand, Naila Assem, Kate Stephen, Charles A. Emrich, Peng Liu, Lucy J. Colwell, Demis Hassabis, Rob Fergus, Frances H. Arnold, Pushmeet Kohli, Jue Wang

  31. OAS-nano: the Observed VHH Space Database

    Clare M. C. Gillis, Klara Kropivšek, Ario de Marco, Olga Obrezanova, Erwin De Genst, Rebecca Croasdale-Wood, Charlotte Mary Deane, Matthew I. J. Raybould

  32. A strategy for ionic current analysis of nanopore protein readouts

    Andrew J. Stein, Neda Ghohabi Esfahani, Stuart Akeson, Pooria Daneshvar Kakhaki, Daphne Kontogiorgos-Heintz, Jeff Nivala, Miten Jain

  33. Rubric-Based Optimization for Text-to-Music Generation

    Ping Wang, Guang Yang, SU Shao-rong, Junkai Wu, Pang Wei Koh, Noah A. Smith

  34. FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution

    Hui Chen, Xuan Qi, James Zhao, Zhaopeng Feng, Shilong Liu, Kuang Xu, Pang Wei Koh, Bryan Hooi

  35. A fluctuation test for gastruloid heterogeneity

    Samuel G. Regalado, 承祥 邱, Sanjay V. Kottapalli, Chau Huynh, Riza M. Daza, Aidan Keith, Jihye Park, Beth Martin, Wei Chen, Hanna Liao, Haedong Kim, Xiaoyi Li, Jean‐Benoît Lalanne, Nobuhiko Hamazaki, Silvia Domcke, Junhong Choi, Jay Shendure

  36. SNOOPPI: a sequence-normalized database of on- and off-target protein–protein interactions

    Sophia Vincoff, Pranam Chatterjee

  37. pH-sensitive binder design with Proton-PottsMPNN

    Christian P. Jacobsen, Lukas Deweid, Foster Birnbaum, Line W Gormsen, Thomas Fryer, Ulla Jagd, Andreas Hald, Morten Grønbech Rasch, Timothy P. Jenkins, Sergey Ovchinnikov

  38. pCoMole: Pareto-Constrained Molecule Editing with Discrete Flows

    Tong Chen, Maximilian Holsman, Lin Zhao, Pranam Chatterjee

  39. Gacha Decoding: Eliciting Diverse Generations Through Instruction Following

    Scott Geng, Yufei Zhang, Joseph Lee, Jerry Zheng Li, Marjan Ghazvininejad, Pang Wei Koh

  40. ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research

    Sohyeon Kim, Yoonho Lee, Bo Liu, Dayoon Ko, Rulin Shao, Seungone Kim, Graham Neubig, Pang Wei Koh, Aakanksha Chowdhery, Akari Asai, Omar Khattab, Yejin Choi, Gunhee Kim, Chelsea Finn

  41. Higher-Order Molecular Grammars for Generative and Foundation Models in Chemistry

    Yiming Huang, Yujie Zeng, Vijay Prakash Dwivedi, Simone Foti, Jianmin Wang, Jure Leskovec, Tolga Birdal

  42. WorldAuditBench: Interactive 3D World Auditing with Multimodal Agents

    Ziyan Jiang, Jingbo Yang, Jiabao Ji, Yujian Liu, Qiucheng Wu, Tommi Jaakkola, Yang Zhang, Shiyu Chang

  43. UniEvo-VL: An On-policy Self-Distillation Training Recipe for Multimodal Model Self-improvement

    Fang Wu, Da Xing, Yanjie Huang, Junxi Wang, Ji Wang, Hejia Geng, Guancheng Wan, Bowen Zuo, Xiaomin Li, Shixiang Tang, Xinyu Xiang, Zehong Wang, Shiyi Du, Peng Xia, Shuangjia Zheng, Yining Hong, Li Erran Li, Jure Leskovec, Yejin Choi

  44. Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo

    Luran Wang, Linrui Ma, Hannes Stark, Regina Barzilay

  45. Context Language Models

    Rulin Shao, Shannon Zejiang Shen, Junjie Oscar Yin, Yuetai Li, 王敏衡, Hamish Ivison, Radha Poovendran, Nathan Lambert, Teng Xiao, Mike Lewis, Wen-tau Yih, Luke Zettlemoyer, Pang Wei Koh

  46. Computational design of a versatile, zero-radius proximity labeling enzyme

    Shizhong A. Dai, Peter E. Cavanagh, Albert Qiang, Zijuan Zhang, Qixiang Geng, Maribel Anguiano, Andrew G. Xue, Tsutomu Matsui, Khanh Nguyen, Namrata D. Udeshi, Sam Pellock, Brian Hie, Steven A. Carr, Christina K. Kim, Nathanael S. Gray, Wei Qin, Alice Y. Ting

  47. Deep-learning-based design of an orthogonal self-labeling protein from K-Ras(G12C)

    Jody Mou, Jing Wesley Leong, Sean Gao, Kaia Slaw, Michael McGill, Katherine A. Donovan, Rebecca J. Metivier, Franziska L. Sendker, Qi Kong, Diangen Lin, Azim M. Dharani, Xi Dawn Chen, Kevin M. Haigis, Eric S. Fischer, Fei Chen, Nicholas F. Polizzi

  48. From Surfaces to Volumes: Registered Geometry for Protein Representation Learning

    Siyuan Chen, Cai Zhou, Jinrui Zhang, Zhaokang Liang, Taku Komura, Wojciech Matusik, Stephen Bates, Tommi Jaakkola, Wengong Jin, Peter Yichen Chen, Minghao Guo

  49. Coevolutionary mining of prokaryotic non-coding elements with a genome language model

    David Li, Garyk Brixi, Alexandra Kim, Mateus Bernabe Fiamenghi, Claudia L. Driscoll, Simone A. Evans, Linyi Gao, Natalia Ivanova, Nikos C. Kyrpides, Karl A. Deisseroth, Max E. Wilkinson, Max E. Wilkinson, Brian Hie

  50. De novo design of semisynthetic conduction pores

    Lee Schnaider, A. Katherine Hatstat, Alistair J. Scott, Sophia K. Tan, Richard G. Hambley, William Dawson, Rhys C. Griffiths, Rian C. Kormos, Arthur Alves de Melo, Eric Tse, Nicholas F. Polizzi, Elizabeth Jayne Wallace, Gregory E. Merz, William F. DeGrado

  51. PepRePs: Peptide-Retargeted Phosphatases via Generative Language Models

    Lauren Hong, Tai-Chen Ho, Yi-Shiuan Tseng, Sophia Vincoff, Tong Chen, Connor Donahue, Matthew P. DeLisa, Po‐Han Chen, Pranam Chatterjee

  52. Latent generative search unlocks de novo design of untapped biomolecular interactions at scale

    Kieran Didi, Danny Reidenbach, Matthew Penner, Supriya Ravichandran, Marshall Case, Mike Nichols, Erik Swanson, Alex Reis, Maggie Prescott, Yue Hong Qian, Dongming Qian, J.S. Yang, Weiji Li, Le Li, Daichi Shonai, Sean M. Gay, Bhoomika Basu Mallik, Ho Yeung Chim, Liurong Chen, Miguel Atienza Juanatey, Hubert Klein, D. L. RIEGER, Phillip Schlegel, Anna U. Macintyre, Maxim Secor, Daniele Granata, Sooyoung Cha, Zhonglin Cao, Guoqing Zhou, Tomas Geffner, Xi Chen, Micha Livne, Zuobai Zhang, Tianjing Zhang, Kyle D. Gion, Michael M. Bronstein, Martin Steinegger, Kristine Deibler, Scott H. Soderling, Clara T. Schoeder, Alena Khmelinskaia, Florian Hollfelder, Christian Dallago, Emine Küçükbenli, Arash Vahdat, Pierce Ogden, Karsten Kreis

  53. A Unified 3D Generative Model for Synthesizable Structure-Based Drug Design

    Ilia Igashov, Arne Schneuing, Adrian W. Dobbelstein, Irina Morozova, Rebecca M. Neeser, Kara Zielinski, Luciano A. Abriata, Aaron S. Petruzzella, David Robin Pavel Iosub, Olivia Gampp, Artem Y. Lyubimov, Evgenia Elizarova, Isabella Ferrara, Pedro M. F. Sousa, Ana R. Lemos, Fabio Testori, Pierre A. Miranda Herrera, Laurin Kanis, Joseph Schmidt, Mac Kevin E. Braza, Rommie E. Amaro, Nicolas Thoma, Davide M. Ferraris, Roland Riek, James S. Fraser, Philippe Schwaller, Michael M. Bronstein, Bruno E. Correia

  54. NatiNb: a naive llama VHH dataset

    Klara Kropivšek, Henriette Capel, Clare M. C. Gillis, G. Gordon, Katharina Dost, Matthew I. J. Raybould, Charlotte Mary Deane, Ario de Marco

  55. Resolving the Genomic Context of Clinically Relevant Antibiotic Resistance Genes in Wastewater with Ligation-Mediated PCR

    Megan E. O’Brien, Bradie Ahern, Piper Brase, Sooyeol Kim, Caroline E. M. McCormack, Denise Garcia, Erika K. Keim, Erin Dahl, Matthew Feck, Kelly J. Kauber, Jorge A. Marchand, Rose S. Kantor, Amy J. Pickering, Breanna McArdle, Erica R. Fuhrmeister

  56. Interaction Profiles as a Universal Language for Generative Molecular Design with ShEPhERD–2

    Kento Abeywardane, Kenji Walker, Connor W. Coley

  57. Reframing enzyme function prediction as conditional generation

    William JF Rieger, Sebastian Haeussermann, Luca Herrmann, Zecheng Li, Béla P. Frohn, Manuel Maluenda, Gabriela Lobinska, Sahil Loomba, Julia C. Reisenbauer, Mikael Bodén A, Alexander Tong, Ariane Mora

  58. AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics

    Yuanhao Qu, Xufeng Liu, Xiaotong Wang, Meitong Chen, Xiao Luo, Luna Lyu, Ming Yin, Jingwen Hui, Di Yin, Ravi K. Dinesh, Lin Qiu, Kexin Huang, Hanchen Wang, Shangyuan Tong, Henry C. Cousins, Ricky Feng, Oswaldo Martinez, Junze Zhang, Tanming Chen, Russ Biagio Altman, Jure Leskovec, Aviv Regev, Mengdi Wang, Le Cong

  59. Generative Language Modeling for Antibody CDR Grafting and Alignment-driven De Novo Design

    Ferran Gonzalez Hernandez, Oliver M. Turnbull, Maliha Sultana, Lorena Roldán-Martín, Rohan Kumar, Tom Diethe, Rebecca Croasdale-Wood, Charlotte Mary Deane, Dino Oglić

  60. Data Scarcity and Model Sparsity: Mixtures-of-Experts Overfit More to Repeated Data

    Atindra Jha, Margaret Li, Jure Leskovec, Percy Liang, Luke Zettlemoyer

  61. COPAL: An ensemble of protein-ligand co-folding models enriches preferred ligand predictions for the LuxR-family of quorum sensing receptors

    Davi Nakajima An, Amy L. Schaefer, Szu-Min Chang, Bianxia Bai, Charlotte Mulligan, Yasuhiro Oda, Everett Peter Greenberg, Frank DiMaio

  62. ShEPhERD-2 Supplementary Data

    Kento Abeywardane, Kenji Walker, Connor W. Coley

  63. Multimodal alignment improves generalizability of genomic biomarker prediction in computational pathology

    Ekaterina Redekop, Eric Zimmermann, Ava P. Amini, Alex X Lu, Neil Tenenholtz, James Hall, Lorin A. Crawford, Kristen Severson

  64. RFOptimization: Guiding Design Optimization with All-Atom Structure Prediction

    Odin Zhang, Jiaqi Wang, Tuscan Rock Thompson, Ziyi You, Zihao Song, Frank DiMaio, David A. Baker

  65. Advancing knotted protein design with ESM3: guided generation and topological insights

    Eva Marsalkova, Petr Šimeček

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  66. Forecasting viral evolution from phylogenetic trees

    Ivan Specht, Soyoon Park, Seyone Chithrananda, Claudia L. Driscoll, Garyk Brixi, Julia A. Palacios, Brian Hie

  67. Modeling Reaction Feasibility to Identify New Opportunities for Sigmatropic Rearrangements

    Jordan P. Liles, Nicholas Casetti, Thijs Stuyver, Connor W. Coley

  68. Author response: Squidly harnesses enzyme functional hierarchy and contrastive learning to efficiently predict catalytic residues from sequence

    William JF Rieger, Mikael Bodén A, Frances Arnold, Ariane Mora

  69. Systematic discovery of circular permutations across the protein universe using CIRPIN

    Aiden R. Kolodziej, S. Mazdak Abulnaga, Sergey Ovchinnikov

  70. Proteome-wide crosslinking mass spectrometry reveals novel components of essential complexes in Toxoplasma

    Simon W Butterworth, Ashley L Gin, Shikha Shikha, Isadonna F. Tengganu, John Rush, Tom Duraisingh, Victoria Sodeinde, Leandro Lemgruber, Fabian Schulte, Ke Hu, Lilach Sheiner, Sergey Ovchinnikov, Sebastian Lourido

  71. Efficient Test-Time Adaptation through Human-AI Interaction

    Zora Zhiruo Wang, Apurva Gandhi, Rulin Shao, Aspen Chen, Jonas Weylin Mueller, Zhiqi Liang, Jett Chen, Michael Ryan, Qianou Ma, Luxi He, Zhoujun Cheng, Andre He, Seungone Kim, Jiayi Geng, Mingqian Zheng, Weiwei Sun, Zheyuan Zhang, Xinran Zhao, Yike Wang, Abe Hou, Liwei Jiang, Pang Wei Koh, Diyi Yang, Graham Neubig, Daniel Fried

  72. 5- t Bu-Thalidomide Is a Selective Molecular Glue Degrader of the Transcription Factor FLT3 Interacting Zinc Finger 1 (FIZ1)

    Patrick J. Brennan, Ruiyang Guo, Alban Ordureau, Sarah E. Singleton, Miranda Li, Fatma Albayrak Halaç, Connor H. Chang, Lorena Yu-Liao, Charlotte Mary Deane, Kendall N. Houk, Michael M. Hann, Lewis L. Brayshaw, Heeseon An, Stuart J. Conway

  73. Fragment-Grounded Neural Simulation of Electron Ionization Mass Spectra at Library Scale

    Magdalena Lederbauer, Runzhong Wang, Connor W. Coley

  74. Mapping the combinatorial coding between olfactory receptors and perception with deep learning

    Seyone Chithrananda, Judith Amores, Kevin Yang

  75. CollabLLM: From Passive Responders to Active Collaborators (Extended Abstract)

    Shirley Wu, Michel Galley, Baolin Peng, Hao Cheng, G. L. Li, Yao Dou, Weixin Cai, James Zou, Jure Leskovec, Jianfeng Gao

  76. Knowledge Distillation During Mid-Training Favors Reasoning over Factual Recall

    Jacqueline He, Howard Yen, Shuyue Stella Li, Margaret Li, Hanqing Zeng, Yinglong Xia, Benyu Zhang, Zhuokai Zhao, Qiang Zhang, Pang Wei Koh, Luke Zettlemoyer, Wen-tau Yih

  77. Structural T‐Cell Receptor Analysis in the Age of Machine Learning

    Nele P. Quast, Matthew I. J. Raybould, Charlotte Mary Deane

  78. Fragment-Grounded Neural Simulation of Electron Ionization Mass Spectra at Library Scale – Supplementary Data

    Magdalena Lederbauer, Runzhong Wang, Connor W. Coley

  79. Predicting cellular responses to perturbation across diverse contexts with State

    Abhinav Adduri, Dhruv Gautam, Beatrice Bevilacqua, Mohsen Naghipourfar, Alishba Imran, Rohan Shah, Noam Teyssier, Rishi Verma, Christopher Carpenter, Basak Eraslan, Francis Chalissery, Rajesh Ilango, Vishvak Subramanyam, Chiara Ricci-Tam, Sanjay Nagaraj, Aidan F. Winters, Mingze Dong, Stefanie Fellinger, Adam Krejčí, Tilmann Bürckstümmer, Sravya Tirukkovular, Jeremy Sullivan, Brian S. Plosky, Nicholas D. Youngblut, Jure Leskovec, Luke A. Gilbert, Silvana Konermann, Patrick D. Hsu, Alexander Dobin, Dave P. Burke, Hani Goodarzi, Yusuf Roohani

  80. Programmable protein degraders enable selective knockdown of pathogenic β-catenin subpopulations in vitro and in vivo

    Tianzheng Ye, Azmain Alamgir, Cara M. Robertus, Darianna Colina, Connor Monticello, Thomas Connor Donahue, Lauren Hong, Sophia Vincoff, Shrey Goel, Peter Fekkes, Luis Miguel Camargo, Kieu Lam, James Heyes, David A. Putnam, Christopher A. Alabi, Pranam Chatterjee, Matthew P. DeLisa

  81. Ultrafast Proton-Coupled Electron Transfer Reactions at Unit Quantum Yield

    Rui Liu, Zachary X. W. Widel, Jiaqi Zhu, Jarrett P. Mansergh, Aubrey R. Caplin, Samuel I. Mann, Ian J. Bakanas, Nicholas F. Polizzi, Peng Zhang, William F. DeGrado, David N. Beratan, Michael J. Therien

  82. The first OpenBind release: An open experimental structure–affinity dataset and benchmark for structure-based AI

    Jochem Nelen, Omeir Khan, E. Charles Adams, Jasmin Cara Aschenbrenner, Warren Athol Thompson, Ali Ebrahim, Eda Çapkın, Cédric Vallée, OpenBind, Elizabeth J. Shotton, Ed Griffen, John D. Chodera, Charlotte Mary Deane, F. von Delft, Mohammed AlQuraishi, Fergus Imrie

  83. A Mammalian High-Throughput Screen for AI-Designed Peptide-Guided Protein Degraders

    Lin Zhao, Anuoluwapo A Mattix, Aastha Pal, Tong Chen, Sophia Vincoff, Lauren Hong, Diana Renteria, Sunetra Sase, Adeline L. Vanderver, Daniel Matson, Pranam Chatterjee

  84. Hypothalamic Cell Type Evolution across Vertebrates

    Yunming Wu, Prateek R Kalakuntla, Billie C. Goolsby, Yanay Rosen, Alyssa Hayashi, Guang Yang, Chew Chai, Heidi M. Tate, Tom Hindmarsh Sten, Jessica P. Nowicki, Lindsay L. Sailer, Jun Hyuk Song, Jaeyoon Lee, Robert C. Jones, Dong‐Wook Kim, David Kabelik, Alexander G. Ophir, Hongkui Zeng, Jure Leskovec, Stephen R. Quake, Lauren A. O’Connell, Bo Wang, Liqun Luo

  85. De Novo Design of a Protein Binder to Probe Gas Channel and Enhance the Oxygen Tolerance of [NiFe]‐Hydrogenase

    Xuan Sun, Qi xiao, Nan Zhang, Wenjin Li, Hang Luo, Yanxin Gao, Leyan Zhang, Leyan Zhang, Yilin Fan, Peiyu Jiang, Yucen Huang, Geng Wu, Liyun Zhang, Liyun Zhang

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  86. Adaptive model-guided protein evolution with sparse data optimizes compact eukaryotic genome editors

    Shijie Wan, Jackson Gold, Pranay Vure, Casey S. Mogilevsky, Ananya Talikoti, Tianrong Chen, Aman Gupta, Trisha Biswas, Zheng You, Vir Acharya, Pranam Chatterjee, Xiao Wang, Xue Gao

  87. Quantifying replication fidelity of unnatural base pairs using nanopore sequencing

    Nicholas A. Kaplan, Jayson Sumabat, Jane McKelvey, Jeantine E. Lunshof, Jorge A. Marchand

  88. Reviewer #2 (Public review): Protein generation with evolutionary diffusion: sequence is all you need

    Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Neil Tenenholtz, Robert Strome, Alan M Moses, Alex X Lu, Nicolò Fusi, Ava P. Amini, Kevin K Yang

  89. Reviewer #1 (Public review): Protein generation with evolutionary diffusion: sequence is all you need

    Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Neil Tenenholtz, Robert Strome, Alan M Moses, Alex X Lu, Nicolò Fusi, Ava P. Amini, Kevin K Yang

  90. Supplementary Data for Modeling Reaction Feasibility to Identify New Opportunities for Sigmatropic Rearrangements

    Nicholas Casetti, Jordan P. Liles, Thijs Stuyver, Connor W. Coley

  91. QuantumPioneer: Scalable Generation of Quantum Chemical Data for Solution-Phase Hydrogen Transfer Reactions

    Haoyang Wu, Jonathan Wenyuan Zheng, Hao‐Wei Pang, Yu-Chi Kao, Akshat Shirish Zalte, Charlles R. A. Abreu, Emad Al Ibrahim, Sayandeep Biswas, Chansup Byun, Jackson W. Burns, Chuangchuang Cao, Yunsie Chung, Xiaorui Dong, Anna C. Doner, Jeremy Kepner, Bonhyeok Koo, Shih‐Cheng Li, Angiras Menon, Lauren Milechin, Nathan Morgan, Kariana Andrea Moreno-Sader, Kevin Spiekermann, Florence H. Vermeire, Bryan Wang, Markus Kraft, Connor W. Coley, William H. Green

  92. Perturb-ME: Scalable mechanism discovery from phenotype-enriched genome-wide screens

    Hanchen Wang, Jiacheng Gu, Chris J. Frangieh, Michael S. Cuoco, Maryann Zhao, Arindam Sett, Tom Beyer, Kuan Pang, Etzard Stolte, Zihan Xu, Jure Leskovec, Orit Rozenblatt–Rosen, Vivek Natarajan, Kathryn Geiger-Schuller, Pratiksha I. Thakore, Aviv Regev

  93. PolyDegLib

    Joules Provenzano, Runzhong Wang, Connor W. Coley, Desirée L. Plata

  94. Advancing Open and Reproducible Relational Learning: RelArena-$α$, TabPFN-Rel and RPI

    Adrian Hayler, Klemens Flöge, Alan Arazi, Rishabh Ranjan, Jure Leskovec, Felix Birkel, Brendan Roof, Anurag Garg, Kristina Collins, Lydia Sidhoum, Jonas Kübler, Siyuan Guo, Oscar Key, Jan Hendrik Metzen, Rylee Grace, David Salinas, Arthur Cahu, Simon Bing, Benjamin Jäger, Tuana Celik, Mihir Manium, Vitor Monteiro, Jake Robertson, Jerry Chen, Eliott Kalfon, Tomás Pereda, Lilly Charlotte Wehrhahn, Dominik Safaric, Tobias Schroeder, Georg Grab, Diana Kriuchkova, Clara Cornu, Philipp Singer, Nick Erickson, Vahid Balazadeh, Marie Salmon, Simone Alessi, Kursat Kaya, Philipp Jund, Léo Grinsztajn, Yann LeCun, Bernhard Schölkopf, Madelon Hulsebos, Lennart Purucker, Sauraj Gambhir, Frank Hutter, Noah Hollmann

  95. Present and future of safe biological AI

    Brian Hie

  96. Aligning protein-generative models to experimental fitness with ProteinDPO

    Talal Widatalla, Ashir A. Borah, S. B. King, Claudia L. Driscoll, Rafael Rafailov, Brian Hie

    Curated by Papers for Protein Design Using Deep Learning · 5.6 RL-based · 5.Function to Sequence

  97. Protein language models and the long tail of functional diversity

    Ria Vinod, Samir Char, Ava P. Amini, Lorin A. Crawford, Kevin Yang

  98. EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology

    Pia Francesca Rissom, Vít Škrhák, Paulo Yanez Sarmiento, Jordan F. Safer, Connor W. Coley, Bernhard Y. Renard, Henrike Heyne, Sumaiya Iqbal

  99. Fast retrieval of structurally similar antibodies from large sequence databases with AbSLang

    Eric Ji Da Wang, Fabian C. Spoendlin, Alexander Greenshields‐Watson, Christopher R. Taylor, Charlotte Mary Deane

  100. Learning millisecond protein dynamics from what is missing in NMR spectra

    Hannah K. Wayment-Steele, Gina El Nesr, Ramith Hettiarachchi, Adedolapo M. Ojoawo, Hasindu Kariyawasam, Sergey Ovchinnikov, Dorothee Kern

  101. Generative design of bacteriophages with genome language models

    S. B. King, Claudia L. Driscoll, David B. Li, Daniel Guo, Aditi T. Merchant, Garyk Brixi, Max E. Wilkinson, Brian Hie

  102. CrediBench: Web-Scale Network Data for Credibility Prediction

    Emma Kondrup, Sebastian Sabry, Hussein Abdallah, Zachary Yang, Jiaqi Xiong, Kellin Pelrine, James J. Zhou, Zhijin Guo, Michael M. Bronstein, Jean‐François Godbout, Reihaneh Rabbany K, Shenyang Huang

  103. Characterizing the Assembly and Functional Properties of Gene-Length Mixed DNA Monolayers on Electrodes for Cell-Free Expression

    Rory J Majule, Kyvalya Reddy, Samrutha Babu, Oren A. Fox, Jeff Nivala, Christopher N. Takahashi

  104. De novo design of small-molecule–induced conformational change

    Jeffrey Chang, Nicholas F. Polizzi

  105. Agentic Laboratories of the Future: Towards World Models for Scientific Discovery

    Fu Kelsey, Luna Lyu, Shuzhen Li, Suozhi Huang, Suheng Xu, Joy Zheng, Xuefeng Liu, Shilong Liu, Greg Barbone, Ying Liu, Linxi Jim Fan, Yuke Zhu, Matthew Davis, Kaiyi Jiang, Sherry Yang, Xu Kuang, Yuan Luo, Francesca Dominici, Christina N. Curtis, Xiaojie Qiu, Alberto Abadie, Caroline Uhler, Mary X. Tang, Joseph C. Wu, Ju Li, Jared Toettcher, José L. Avalos‬, Rebecca B. Rojansky, Huimin Zhao, John P. A. Ioannidis, Barry Rand, Emma K. Lundberg, Andrew S. Rosen, Zhuang Liu, Shana Kelley, Chenyan Xiong, Barbara Elizabeth Engelhardt, Thomas Jude Montine, Christina V. Theodoris, Katherine Snowden Pollard, Fuxin Li, Xueyue Zhang, Tianyi Peng, Dmitri Basov, Xiang Qian, Eric Xing, Ali Yazdani, Jure Leskovec, Nigam Haresh Shah, Zhenan Bao, Pietro Perona, Sanfeng Wu, Clifford P. Brangwynne, Le Cong, Mengdi Wang

  106. AlphaFold3 predictions of 24 de novo designed lidded exatecan-binding proteins

    Chang, Jeffrey, Nicholas F. Polizzi

  107. Reconstructing whole-organism cell phylogenies with resolved ancestral transcriptional states

    Zhan Liu, Shanjun Deng, Shanjun Deng, Mengdong Zhang, Boliang Liu, Huimin Xiang, Zhiyun Chen, Ao Zhang, Jay Shendure, Xionglei He

  108. De novo design of semisynthetic conduction pores - supplementary scripts

    Lee Schnaider, William F. DeGrado, Samuel H. Schneider, Nicholas F. Polizzi

  109. A Generative Neuro‐Symbolic AI for Protein Sequence Design

    Marianne Defresne, Delphine Dessaux, Samuel Buchet, Lucie Barthe, Liza Ammar Khodja, Bessam Azizi, Valentin Durante, Gianluca Cioci, Simon de Givry, Alain Roussel, Luis Fernando Garcia-Alles, Thomas Schiex, Sophie Barbe

    Curated by Papers for Protein Design Using Deep Learning · 4.1 MLP-based · 4.Scaffold to Sequence

  110. The Human Bindome: A Proteome-scale Atlas of Designed Binder Candidates

    Julius Wenckstern, Anna M. Díaz-Rovira, Julia Kuhn, Arvid Ban, Rahma Hamdani, Roser Pruano-Milla, Evgenia Elizarova, Sandrine Georgeon, Kaiden Thompson, Matthias Hinterndorfer, Devanarayanan Siva Sankar, Maximilian Dünnebacke, David Desscan, Sreenath Sasidharan Nair, Marcelo Querino Lima Afonso, Jennifer Fleming, Sameer S Velankar, Andrea Ablasser, Paola Picotti, Georg E. Winter, Mikko Taipale, Bruno E. Correia

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  111. Integrating chemical structures as treatments improves representations of microscopy images for morphological profiling

    Yemin Yu, Emre Hayir, Neil Tenenholtz, Lester W. Mackey, Ying Wei, David Alvarez-Melis, Ava P. Amini, Alex Xijie Lu

  112. Discovery of a phenazine–thiol conjugase from sparse data using genome-informed machine learning

    Xiaoyu Shan, Inês B. Trindade, Nathaniel R. Glasser, Korbinian O. Thalhammer, Matthew Scurria, Ariane Mora, Stuart J. Conway, Dianne K. Newman

  113. Full raw sequencing dataset for "Quantifying replication fidelity of unnatural base pairs using nanopore sequencing"

    Nicholas A. Kaplan, Jayson Sumabat, Jane McKelvey, Jeantine E. Lunshof, Jorge A. Marchand

  114. Locus-Scale Massively Parallel Reporter Assays

    Abby V. McGee, Carina G Biar, Beth Martin, Tony Li, Jean‐Benoît Lalanne, Haedong Kim, Jay Shendure

  115. Expanding Flow Maps

    Sophia Tang, Pranam Chatterjee

  116. Programmable design of synthetic plant immune receptors for pathogen protein recognition

    Haocheng Zhu, Dandan Jiang, Qiao Zhang, Kang Zhang, Kevin Tianmeng Zhao, Jin‐Long Qiu, Caixia Gao

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  117. Programming protein shape as an explicit design layer via CAD blueprint-guided diffusion

    Yilun Qi, Guoxun Zhang, Klaus Yserentant, Sonya Lee, Artem Y. Lyubimov, Kyrellos E. Ibrahim, Giuseppe Cimicata, James S. Fraser, Bo Huang, Tanja Kortemme

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  118. Antigen-specific Antibody Multi-modal Foundation Model for Functional Antibody Design

    Xiaoliang Shi, Zichen Wang, Runze Ma, Zhongyue Zhang, Shuangjia Zheng

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  119. Expanding the scope of protein language modeling to protein-protein interactions with MSA Pairformer

    Yo Akiyama, Zhidian Zhang, Olivia Tang, Seongeun Kim, Milot Mirdita, Martin Steinegger, Sergey Ovchinnikov

  120. ABOPD: Antibody CDR Design via On-Policy Distillation

    Zhuo Yang, Jiaying He, Jiaqing Xie, Daolang Wang, Xipeng Qiu, Yuxin Wang, Tianfan Fu, Beilun Wang

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  121. De Novo Design of Protein Switches with Diffusion-Based Ensemble Sampling

    Alireza Omidi, Jiajun He, Jennifer M. Bui, Joerg Gsponer, Saifuddin Syed

    Curated by Papers for Protein Design Using Deep Learning · 3. Function to Scaffold · 3.4 Diffusion-based

  122. Massively parallel characterization and predictive modelling of neuronal regulatory variation

    Kilian Salomon, Chengyu Deng, Pyaree Mohan Dash, Theofilos Chalkiadakis, Qunrui Li, Ziwei Chen, Nicholas F. Page, Mustafa Helal, Sebastian Röner, Anshul Bharat Kundaje, Claudia Langenberg, Maik Pietzner, Jay Shendure, Max Schubach, Nadav Ahituv, Martin Kircher

  123. PeptiVerse: A unified platform for therapeutic peptide property prediction

    Yinuo Zhang, Sophia Tang, Tong Chen, Elizabeth Mahood, Sophia Vincoff, Pranam Chatterjee

  124. RetroAgent: Harnessing LLMs to Search Over Structured Memory for Agentic Retrosynthesis Planning

    Yanqiao Zhu, Jingru Gan, Xiaoqi Sun, Fang Sun, Y Shi, Md Tauhidul Islam, Chao Shang, Wenhao Gao, Connor W. Coley, Yizhou Sun, Wei Wang

  125. De novo design of orthogonal far-red, orange, and green fluorophore-binding proteins for multiplexed imaging

    Long Tran, Steffen Klein, David Juergens, Shajesh Sharma, Justin A. Decarreau, Gyu Rie Lee, Yujia Wang, Wei Chen, Asim K. Bera, Alex Kang, Jonathan Woods, Emily Joyce, Dionne K. Vafeados, Nicole Roullier, Xinting Li, Bingxu Liu, Bo Yang, Edin Muratspahić, Timothy A. Brown, Jonathan B. Grimm, Ronak H. Patel, Luke D. Lavis, Julia Mahamid, Linna An, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  126. AI-driven discovery of synergistic drug combinations against pancreatic cancer

    Regina Barzilay, Carleen A. Klumpp-Thomas, Tommi Jaakkola, Mohsen Pourmousa, Andrew Thieme, Zina Itkin, Sam Michael, Wengong Jin, Alexey V. Zakharov, Alexander Tropsha, Kelli Wilson, Travis Maxfield, Sankalp Jain, Cleber Camilo Melo-Filho, Marc Ferrer, Eugene N. Muratov, Joshua E. Hochuli, Noel Southall, Elena S. Barnaeva

  127. De novo design of ligand binding and sensing with a physics based generative approach

    Yanzhe Zhang, Yitao Ke, Rui Zhi, Qihan Jin, Yiqin Feng, Chentong Wang, Minchao Fang, Jinyang Liao, Dachuan Chen, Jiao Liu, Longxing Cao

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  128. Chemical Shift Prediction Beyond the Electronic Structure Limit

    Keir Adams, Corin Wagen, John Wolford, Marcus H. Sak, Josep Saurí, Zhitao Feng, Abhijeet S. Bhadauria, Michael A. Bailey, Jonah S. Downs, Sophia Z. Li, Amelia I. Liu, Tess Smidt, Robert S. Paton, Richard Y. Liu, Connor W. Coley, Eugene E. Kwan

  129. De novo design of selective kinase modulators

    Magnus S. Bauer, Saurav Kumar, Mia S. Donald-Paladino, Dongyang Li, Kody A. Klupt, Matthias Glögl, Ana María Fernández‐Escamilla, Thomas Schlichthaerle, Edin Muratspahić, Xinru Wang, Ludwig Schmiderer, Sebastian Kenny, Brian Coventry, Bulat Faezov, Wei Chen, Alex Shida, Gyu Rie Lee, Yang Hsia, Ryan D. Kibler, Michael B. Elowitz, Daniel Lietha, Behnam Nabet, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  130. Enhancing machine learning‐based binder design with high‐throughput screening: A comparison of mRNA and yeast display technologies

    Zhiyuan Yao, McGuire Metts, Avery K. Huber, J M Li, Tomoaki Kinjo, Henry Dieckhaus, Amrita Nallathambi, Albert A. Bowers, Brian A. Kuhlman

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  131. Variable-Length Generative Protein Design via Generalized Poisson Flow

    Chaoran Cheng, Zhanghan Ni, Yanru Qu, Yuxin Chen, Ruihan Guo, Jiajun Fan, Ge Liu

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.8 Flow-based

  132. Vilya-1: An all-atom foundation model for macrocycle structure prediction and design

    Vilya Research, Pascal Sturmfels, Milad Salem, Naozumi Hiranuma, Stephen Rettie, Xiaoliang Pan, Benjamin D. Sellers, Adam P. Moyer, Patrick J. Salveson, Ivan Anishchanka

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  133. Autonomous biomedical research with an artificial intelligence agent

    Kexin Huang, Serena Zhang, Hanchen Wang, Yuanhao Qu, Yingzhou Lu, Ryan Li, Yusuf Roohani, Lin Qiu, Shiyi Cao, G. L. Li, Junze Zhang, Di Yin, Rick P. Wierenga, Deniz Kavi, Liu S, Tianwei She, Shruti Marwaha, Jennefer N. Carter, Xin Zhou, Matthew Thomas Wheeler, Jonathan Adam Bernstein, Mengdi Wang, Peng He, Jingtian Zhou, Michael P. Snyder, Le Cong, Aviv Regev, Jure Leskovec

  134. Characterising AlphaFold 3’s ability to predict T cell antigen specificity

    Benjamin McMaster, Ali El Moselhy, Ilija Ilievski, Christopher J. Thorpe, Nicole L. La Gupta, Jamie Rossjohn, Charlotte Mary Deane, Hashem Koohy

  135. A De Novo CO 2 Reductase Featuring a Cysteine-Ligated Cobalt Porphyrin Cofactor

    Emily Radley, Alessia Andrews, Indrek Kalvet, Yunling Deng, Elizabeth L. Bell, Colin Levy, Mary Ortmayer, Derren J. Heyes, Clare F. Megarity, Reyes Núñez‐Franco, Amy E. Hutton, Yi Lu, David A. Baker, Anthony P. Green

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  136. IgGM2: An All-Atom Foundation Model for Adaptive Immune Receptor Design

    J J Ma, Fandi Wu, Lin Yao, J Gao, Rubo Wang, Qifeng Li, Nianzu Yang, Songlin Jiang, Dawei Huang, Xiaoyong Pan, Yiheng Zhu, Tingjun Hou, Jianhua Yao, Junchi Yan

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.9 AlphaFold-based

  137. Universal cell embedding provides a foundation model for cell biology

    Yanay Rosen, Yusuf Roohani, Ayush Agrawal, Leon Samotorcan, Stephen R. Quake, Jure Leskovec

  138. Computational design of blue melanin by motif‐scaffolding a pentapeptide‐repeat protein

    Di Sheng Lee, Bomi Park, Sergio Salgado, James Dolgin, David Lee Kaplan

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  139. Predicted TCR:pMHC Structures

    Benjamin McMaster, Ali El Moselhy, Ilija Ilievski, Christopher J. Thorpe, Nicole L. La Gruta, Jamie Rossjohn, Charlotte Mary Deane, Hashem Koohy

  140. Rethinking Benchmarks and Models for Enzyme Specificity Prediction

    Elizabeth H. Mahood, Natália Komorníková, Tomáš Pluskal, Pranam Chatterjee

  141. A generalizable interface-seeded framework for de novo design of functional oligomers

    Ho Yeung Chim, Mubarak Olanrewaju Idris, Dominic Rieger, Phillip Schlegel, Nicolas Manuel Goldbach, Miguel Atienza Juanatey, Bhoomika Basu Mallik, Stephen Buckley, Samrat Basak, Sandrine Georgeon, Kelvin Lau, Florence Pojer, Leonard Kaysser, Philip Tinnefeld, Clara T. Schoeder, Bruno E. Correia, Alena Khmelinskaia

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  142. Linker-Length Landscape Mapping Enables Coupling of Diverse Synthetic Chemically Induced Dimerization Systems to Molecular Readouts

    Yuxin Pan, Shoukai Kang, Davi Nakajima An, Yixuan Yu, Frank DiMaio, Liangcai Gu

  143. Membrane protein solubilization and structure determination using de novo–designed proteins

    Ljubica Mihaljević, David E. Kim, Pooja Bandawane, Helen E. Eisenach, Andrew J. Borst, Alexis Courbet, Connor Weidle, Kenneth D. Carr, Everton Burlamarque Bettin, Qiushi Liu, Aldo T. Trejos, Sagardip Majumder, Surabhi Kokane, Alexander Stevens, Edin Muratspahić, Thomas Schlichthaerle, Marc Expòsit, Xinting Li, Mila Lamb, A G Murray, Rashmi Ravichandran, Elizabeth C. Williams, Shuyuan Hu, Lynda M. Stuart, Linda Grillová, Nicholas Robert Thomson, Michael Landreh, Pengxiang Chang, Lorenzo Giacani, Melissa J. Caimano, Kelly L. Hawley, Neil P. King, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  144. SF-Cluster: Frustration-Guided MSA Subsampling for Alternative Protein Conformation Recovery

    Hanqun Cao, Gao Z, Chunbin Gu, Ge Liu, Pheng Ann Heng, Pranam Chatterjee

  145. OpenGerminal: an open-source implementation of the Germinal antibody design pipeline

    Bing Han, Sheng Li

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  146. GLACIER: Rethinking Mass Spectrum Prediction as an Object Detection Problem

    Rui-Xi Wang, Runzhong Wang, Connor W. Coley

  147. BoltzProt-1: Towards Efficient De Novo Binder Design with Good Developability

    Talip Uçar, Jack Bates, Yunguan Fu, Wenxian Shi, Hannes Stark, Demitri Nava, Luca Cavalleri, Jeremy Wohlwend, Gabriele Corso, Saro Passaro

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  148. ShuklaGroup/esmdynamic: esmdynamic

    Diego E. Kleiman, Tom Sercu, Nikita Smetanin, Naail Kashif-Khan, Roshan Rao, Joshua Meier, jasoniliu, Chloe Hsu, Yinying Yao, Zeming Lin, Jacob Buswell Roberts, Alessandro Diana, Alex Morehead, Brian Hie, Walid Ahmad, Yunan Luo, brett koonce, cutecutecat, E Jinq Wong, Kira Mourão, S M Bonner

  149. Zero-shot design of drug-binding proteins via neural iterative selection−expansion

    Benjamin N. Fry, Kaia Slaw, Nicholas F. Polizzi

  150. Pathway-Aware Template-Based Retrosynthesis

    Jason J. Zhang, Seung Kyun Ha, Jihye Roh, Zhengkai Tu, Pritha Verma, Connor W. Coley, Klavs F. Jensen

  151. Integrating Machine-learning and Ultra-high-throughput Screening for Enzyme spaces exploration

    Yitao Ke, Yanzhe Zhang, Minchao Fang, Jingyang Zhao, Hao Zhu, Zehui Xu, Longxing Cao

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  152. ComplexDesign: sequence-hallucination design of protein binders bridging multiple proteins

    J Q Xu, Milong Ren, Ning Qi, Xinru Zhang, Zaikai He, Chungong Yu, Dongbo Bu

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  153. Computational Redesign of an Antifreeze Protein Using Deep Learning

    Cianna N. Calia, Arthur J. Altunc, Rosemary J. Eufemio, Benjamin O. Alvarado, Jake D. Huynh, Eunkeu Oh, Michael D. Burkart, Konrad Meister, Francesco Paesani

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  154. Efficient generation of epitope-targeted antibodies with Germinal

    Luis Santiago Mille, Claudia L. Driscoll, John N. Wang, Haoyu Dai, Talal Widatalla, Jim L. Zhang, Xiaowei Zhang, Bing Rao, Liang Feng, Brian Hie, Xiaojing J. Gao

  155. A high-level programming language for generative biology with Proto

    Aditi T. Merchant, Daniel Guo, Ben T Viggiano, Lucas Brennan-Almaraz, Evelyn Hur, Tina Mai, Peter Yin, Samuel H. King, Euan Angus Ashley, Brian Hie

  156. Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction

    Fang Wu, Weihao Xuan, Jure Leskovec, Yejin Choi, Li Erran Li

  157. SAbDab2: The structural antibody database in the age of machine learning

    Henriette Capel, Odysseas Vavourakis, Ben H. Williams, Christopher R. Taylor, Charlotte Mary Deane

  158. Ovo, an open-source ecosystem for de novo protein design

    David Příhoda, Marco Ancona, Tereza Calounova, Adam Kral, Lukas Polak, Hugo Hrbáň, Nicholas J. Dickens, Danny A. Bitton

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  159. Molecular basis of mitochondrial leucine transport by human Sideroflexin 1

    Fiona Fitzpatrick, Daniel T.D. Jones, Hannah Aguirre, Benjamin N. Fry, Elena T. Kaemmerer, S. Tan, Luise E. Veelken, Wen Han Tong, Samuel Block, S. Ali Aghvami, Edmund R.S. Kunji, Nicholas F. Polizzi, Stephen C. Blacklow, Nora Kory

  160. MassSpecGym in the Wild: Uncovering and Correcting Evaluation Pitfalls in AI-Driven Molecule Discovery

    Hongxuan Liu, Roman Bushuiev, Ivy Lightheart, Mrunali Manjrekar, Anton Bushuiev, Magdalena Lederbauer, Filip Jozefov, Yinkai Wang, Soha Hassoun, Josef Šivic, James Taylor, Runzhong Wang, David Healey, Tomáš Pluskal, Connor W. Coley

  161. Membrane Protein Design: From Reprogramming Functions to AI-Guided De Novo Design Approaches

    Jefferson Roberts, Patrick Barth

    Curated by Papers for Protein Design Using Deep Learning · 1. Reviews · 1.1 De novo protein design

  162. Improved Stability and Brightness Following Iterative Redesign of a De Novo Biliprotein

    Felix S. Morey-Burrows, Tingxiang Yang, Woody Ahern, David A. Baker, Andrew Hitchcock, Graham J. Leggett, Jenny Clark, Aneika C. Leney, Christopher Neil Hunter

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  163. Spokes: Optimizing for Diverse Pretraining Data Selection

    Clarence Lee, Yejin Choi, Luke Zettlemoyer, Pang Wei Koh, Hai Leong Chieu

  164. Analysis of monoclonal antibodies against the malaria invasion complex protein RIPR reveals the structural basis for synergistic antibody protection

    Barnabas G. Williams, Jordan R. Barrett, Josefin Bartholdson Scott, Cassandra A. Rigby, Matteo Cagiada, Doris Quinkert, Kirsty McHugh, Anna Gesine Huhn, Sean A. Burnap, Camille Gourjault, Francesca Byrne, Sai Sundar Rajan Raghavan, Ana Rodrigues, Laura Bergamaschi, Beatrice Balzarotti, Simon J. Watson, Noah Miller, Lloyd D. W. King, Francesca R. Donnellan, Camilla A. Gladstone, Jemima Paterson, Stefania Scalabrino, Sarah E. Silk, Jo Salkeld, Angela M. Minassian, Katherine Skinner, Weston B. Struwe, Charlotte Mary Deane, Stephen T. Reece, Andrew B. Ward, Simon J. Draper

  165. Riemannian Metric Matching for Scalable Geometric Modeling of Distributions

    Jacob Bamberger, Adam Gosztolai, Pierre Vandergheynst, Michael M. Bronstein, Jones, Iolo

  166. Emyx: Fast and efficient all-atom protein generation

    Williams, Nicholas J., Ward Haddadin, Matteo Paolo Ferla, Constantin Schneider, Nicholas B. Woodall, Ruby Sedgwick, Christian Degnbol Madsen, Andrew L. Hopkins, Douglas E. V. Pires, Edward O. Pyzer‐Knapp

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.8 Flow-based

  167. Why machine learning fails at mass spectrometry for small molecules

    Ling Min Serena Khoo, Regina Barzilay

  168. A2D2: Fine-Tuning Any-Length Discrete Diffusion for Adaptive Decoding

    Sophia Tang, Y C Zhu, Molei Tao, Pranam Chatterjee

  169. Deep learning based design of buried hydrogen bond networks with HBDesigner

    Henry Dieckhaus, Brock T. Harvey, Tomiris Mulikova, Jessica T Horenstein, Nathan I. Nicely, Nicholas Z. Randolph, Brian A. Kuhlman

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  170. Controlling metal-carbonate phase, form, and function through de novo protein design

    Paul S. Kwon, Xinqi Li, Le Tracy Yu, Harley Pyles, Todd H. Lewis, Connor Weidle, Andrew J. Borst, Catherine Bodinger, Alex Kang, Hannah Nguyen, Johnny Mendoza, Kenneth D. Carr, Brian Coventry, Yang Hsia, Zsombor Molnár, Dongsheng Li, Bo Zhang, Brandi Michelle Cossairt, Shuai Zhang, Asim K. Bera, James J. De Yoreo, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.4 CNN-based

  171. SciPhy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data

    Sophie Seidel, Antoine Zwaans, Samuel G. Regalado, Junhong Choi, Jay Shendure, Tanja Stadler

  172. Generative modeling with structured diffusion models

    Minkai Xu, Stefano Ermon, Jure Leskovec, Lei Li, Stanford University. School of Engineering., Stanford University. Computer Science Department.

  173. M*: A Modular, Extensible, Serving System for Multimodal Models

    Atindra Jha, Naomi Sagan, Keisuke Kamahori, Irmak Sivgin, Rohan Sanda, Steven Gao, Mark Horowitz, Luke Zettlemoyer, Olivia Hsu, Jure Leskovec, Baris Kasikci, Stephanie Wang

  174. Promera: a unified model for biomolecular structure prediction, filtering, and design

    Bowen Jing, Mihir Bafna, Daniel J. Diaz, Adam R. Klivans, Bonnie Berger

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  175. Evaluating the role of pretraining dataset size and diversity on single-cell foundation model performance

    Alan DenAdel, Madeline Hughes, Akshaya Thoutam, Anay Gupta, Andrew W. Navia, Nicolò Fusi, Srivatsan Raghavan, Peter S. Winter, Ava P. Amini, Lorin A. Crawford

  176. MSA Pairformer analysis data

    Yo Akiyama, Zhidian Zhang, Olivia Tang, Seongeun Kim, Milot Mirdita, Martin Steinegger, Sergey Ovchinnikov

  177. Author Correction: De novo design of quasisymmetric two-component protein cages

    Shunzhi Wang, Ying Xie, David Chmielewski, Connor Weidle, Tong Shu, Green Ahn, Ryan D. Kibler, Cindy Hernandez, Wei Chen, David Camilo Duran, ANN CARR, Asim K. Bera, Sangmin Lee, Justin A. Decarreau, Alex Kang, Evans Brackenbrough, Emily Joyce, Kejia Wu, Andrew J. Borst, Andrew Favor, Buwei Huang, Frank DiMaio, Liam J. Holt, David A. Baker

  178. Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models

    A. Gupta, Polina Barabanshchikova, Vikas Garg, Samuel Kaski A, Tommi Jaakkola

  179. ProtGPT3: an Open-source family of Promptable and Aligned Protein Language Models

    Michele Garibbo, Gerard Boxo Corominas, Filippo Stocco, R. Illanes, Lasse Middendorf, Noelia Ferruz

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  180. Few-step Cofolding with All-Atom Flow Maps

    Gianluca Scarpellini, Ron Shprints, Peter Holderrieth, Juno Nam, Pranav Murugan, Rafael Gómez‐Bombarelli, Tommi Jaakkola, Maruan Al-Shedivat, Nicholas Matthew Boffi, Avishek Joey Bose

  181. Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules

    Riccardo De Santi, Bruce Lee, C. Jensen, Kimon Protopapas, Sophia Tang, Cheng-Hao Liu, Pranam Chatterjee, Yisong Yue, Andreas Krause

  182. Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge

    Juntong Shi, Brian L. Trippe, Jure Leskovec, Stefano Ermon, Minkai Xu

  183. Generative design of programmable asymmetric β-barrel nanopores

    Annika Philomin, Ria Sonigra, Sagardip Majumder, Hsien-Jui Lin, Yanjing Li, Fanglei Xue, Ryan D. Kibler, Claire Baldus, Eva Trapido, Amelie Medeiros, Brian Coventry, Asim K. Bera, Alex Kang, Johnny Mendoza, Manish Kumar, Yi Yang, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  184. Why a synthetic human genome is still worth building

    Sudarshan Pinglay

  185. AlloGen: Conformation-Selective Binder Generation with Differential State Scoring

    Hanqun Cao, Zachary Quinn, Aastha Pal, Sumi Kimura, Jingjie Zhang, Pheng Ann Heng, Pranam Chatterjee

    Curated by Papers for Protein Design Using Deep Learning · 3. Function to Scaffold · 3.4 Diffusion-based

  186. Artificial Intelligence and Chemical Engineering: An Editorial Preface

    Venkat Venkatasubramanian, Connor W. Coley

  187. Reply to: Limited evidence of AI superiority in seasonal influenza vaccine strain selection

    Wenxian Shi, Jeremy Wohlwend, Menghua Wu, Regina Barzilay

  188. Gigabase-scale deletion scanning of the human genome

    Jonas Koeppel, Aidan Keith, Samantha R. Sgrizzi, PeiXi Chen, Riza M. Daza, Faaiz Quaisar, Eleftheria Anastasia, Zihao Song, Jay Shendure, Sudarshan Pinglay

  189. Why Are DMD Students Lazy? Understanding the Copying Behavior in Few-Step Distillation

    Shucheng Li, Iolo Jones, Alexander Tong, Michael M. Bronstein

  190. Structure of the PhiX174 bacteriophage

    D.F. Li, S.H. King, C.L. Driscoll, Max E. Wilkinson, Brian Hie

  191. Structure of the Evo-Phi36 bacteriophage

    D.F. Li, S.H. King, C.L. Driscoll, Max E. Wilkinson, Brian Hie

  192. AgentPLM: Agentic Protein Language Models with Reasoning-Augmented Decoding for Protein Sequence Design

    Sahil Rahman, Maxx Richard Rahman

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  193. Systematic incorporation of non-natural base pairs for increased toehold-mediated strand displacement kinetics in random DNA pools — NAR 2026 submission

    Tiernan Kennedy, T Mayer, Friedrich Simmel, Chris Thachuk

  194. Just Add Structure: Protein Language Models Combined with Structural Equivariance Excel at Protein Tasks

    Qurat-ul-ain, Carlos Outeiral, Matteo Cagiada, Yee Whye Teh, Charlotte Mary Deane

  195. FlowER - Mechanistic datasets and model checkpoint (May 29 2026)

    Mun Hong Fong, Joonyoung F. Joung, Timothy M. Pinkhassik, Connor W. Coley

  196. Graph Neural Networks Are Not Continuous Across Graph Resolutions

    Christian Koke, Yuesong Shen, Abhishek Saroha, Marvin Eisenberger, Bastian Rieck, Michael M. Bronstein, Daniel Cremers

  197. mRNAutilus: Multi-Objective-Guided Discrete Generation of mRNA with Optimized Therapeutic Properties

    Sawan Patel, Sophia Tang, Yesol Kim, Yinuo Zhang, Divya Srijay, Ping-Jung Lin, Shambhavi Shubham, Fengmei Pi, Cedric Wu, Sherwood Yao, Pranam Chatterjee

  198. Enabling federated discovery leveraging agentic AI systems: The Cancer AI Alliance and DataVoyager.

    Simone E. Dekker, Stephen M. Salerno, Bodhisattwa Prasad Majumder, Reece Adamson, Rory Donovan-Maiye, Pang Wei Koh, Lei Deng, Keith D. Eaton, Srinivasan Yegnasubramanian, Kenneth L. Kehl, Justin Jee, Brian M. Bot, Jeff T. Leek, Cancer AI Alliance (CAIA)

  199. Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets

    Ashima Khanna, Dominik G. Grimm

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.5 Sampling-algorithms

  200. SurfDesign: Effective Protein Design on Molecular Surfaces

    Fang Wu, Shuting Jin, Xiangru Tang, Mark Gerstein, Xiangxiang Zeng, Yejin Choi, Jure Leskovec, Jinbo Xu

    Curated by Papers for Protein Design Using Deep Learning · 4.7 Transformer-based · 4.Scaffold to Sequence

  201. JobBench: Aligning Agent Work With Human Will

    Yuetai Li, Yichen Feng, Zhangchen Xu, Zixian Ma, Kaiyuan Zheng, Fengqing Jiang, Xinghua Sun, Rulin Shao, Zichen Chen, Yue Huang, Xinyang Han, Brian Lee, Kayla Xu, Shenglai Zeng, Hang Hua, Xiangliang Zhang, Basel Alomair, Ranjay Krishna, Luke Zettlemoyer, Pang Wei Koh, Bhaskar Ramasubramanian, Luyao Niu, Xiang Yue, Radha Poovendran

  202. AI-Guided De Novo Design of a Caffeine-Induced Protein Dissociation System

    Tatsuki Nonomura, Brendan McKee, Anna Price, Mingguang Cui, Zaynah Yousuf, Faith Tran, Lian He, Tianlu Wang, Yubin Zhou

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  203. Found in Conversation: LLMs Teach Themselves to Close the Multi-Turn Gap

    Tianlang Chen, Shirley Wu, Jure Leskovec

  204. Diffusion Domain Expansion: Learning to Coordinate Pre-trained Diffusion Models

    Egor Lifar, Semyon Savkin, Timur Garipov, Shangyuan Tong, Tommi Jaakkola

  205. GeoCycler: Reward-Aligned 3D Diffusion for Constraint-Conditioned Cyclic Peptide Design

    Jingjie Zhang, Hanqun Cao, Haosen Shi, Mutian He, Yu Wang, Zijun Gao, Fang Wu, Xiaojun Yao, Chang‐Yu Hsieh, Sinno Jialin Pan, Pranam Chatterjee, Chunbin Gu, Pheng‐Ann Heng

  206. ANARCII enables alignment-free antigen receptor numbering using a generalised language model

    Alexander Greenshields‐Watson, Parth Agarwal, Sarah A. Robinson, Ben H. Williams, G. Gordon, Henriette Capel, Yushi Li, Fabian C. Spoendlin, Broncio Aguilar-Sanjuan, Fergus Boyles, Charlotte Mary Deane

  207. DNAS-Bench: Deterministic Nucleic Acid Screener Benchmarking

    Henry C. Wong, Tadayoshi Kohno, Jeff Nivala

  208. Accurate protein stability prediction for small domains using mega-scale experiments

    Yehlin Cho, Kotaro Tsuboyama, Theodore J. Litberg, Michelle Jung, Adunoluwa Obisesan, Wang, Qian, 1972-, Claire M. Phoumyvong, Jane Thibeault, Sergey Ovchinnikov, Gabriel J. Rocklin

  209. De Novo Design of Miniature and Efficient Metallo-Ketoreductases

    Y Xu, Yunhao Li, Hangwen Zheng, Elliot S. Delfosse, Gao Y, David A. Baker, Pengfei Ji

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  210. Raw BLI and SPR data

    Xiaojing J. Gao, Claudia L. Driscoll, Luis Santiago Mille, Xiaojing J. Gao, Brian Hie, Haoyu Dai

  211. MUX: Continuous Reasoning via Multiplexed Tokens

    Ayhan Suleymanzade, Halil Alperen Gozeten, Michael M. Bronstein, İsmail İlkan Ceylan, Jinwoo Kim

  212. Learning over Positive and Negative Edges with Contrastive Message Passing

    Peter Pao-Huang, Charilaos I. Kanatsoulis, Michael D. Bereket, Jure Leskovec

  213. Harnessing AtomisticSkills for Agentic Atomistic Research

    Bowen Deng, Bohan Li, Matthew Cox, Hoje Chun, Juno Nam, Artur Lyssenko, Sathya Edamadaka, Jurğis Ruža, Xiaochen Du, Nofit Segal, Jesus Diaz Sanchez, Mingrou Xie, Ty Perez, Yu Yao, Miguel Steiner, Sauradeep Majumdar, Charles B. Musgrave, Anirban Chandra, Abhirup Patra, Detlef Hohl, Connor W. Coley, Ju Li, Rafael Gómez‐Bombarelli

  214. Bio-BLIP: A Multimodal Architecture for Transferable Reasoning in Genomic Variant Interpretation

    Anvita Gupta, Alejandro Buendia, Anshul Bharat Kundaje, Jure Leskovec

  215. De novo design of transmembrane accessory subunits for fold stabilization and expansion

    Sebastian Jojoa-Cruz, Saba Kanwal, Nolan P Jacob, Weiyi Tang, Mio Murakoso, Minghao Zhang, Jiayi Li, Masy Domecillo, Nicholas F. Polizzi, John R. Yates, Huong T. Kratochvil, Fábio Pereira Gomes, Heedeok Hong, Marco Mravic

  216. QuantumPioneer Dataset

    Haoyang Wu, Jonathan Wenyuan Zheng, Hao‐Wei Pang, Yu-Chi Kao, Akshat Shirish Zalte, Charlles R. A. Abreu, Emad Al Ibrahim, Sayandeep Biswas, Chansup Byun, Jackson W. Burns, Chuangchuang Cao, Yunsie Chung, Xiaorui Dong, Anna C. Doner, Jeremy Kepner, Bonhyeok Koo, Shih‐Cheng Li, Angiras Menon, Lauren Milechin, Nathan Morgan, Kariana Andrea Moreno-Sader, Kevin Spiekermann, Florence H. Vermeire, Bryan Wang, Markus Kraft, Connor W. Coley, William H. Green

  217. Entropy Across the Bridge: Conditional-Marginal Discretization for Flow and Schrödinger Samplers

    Bruno Trentini, Dejan Dejan Stančević, Michael M. Bronstein, Alexander Tong, Luca Ambrogioni

  218. BiomniBench: Process-level Evaluation of LLM Agents for Real-world Biomedical Research

    Yuanhao Qu, Yingzhou Lu, Xinming Tu, Serena Zhang, Tianwei She, Alexander Glenn Shaw, Jou‐Ho Shih, Bingqing Zhao, Minjie Shen, Haochen Yang, Jielin Yan, Rongchuan Zhang, Xinze Wu, Tingting Li, Bin Zhou, Ning Wang, Adam Ma, Le Cong, Xiaobo Hu, Yuan Jiang, Jiayun Dong, Tao Peng, Jure Leskovec, Kexin Huang

  219. Redesign selective protein binders using contrastive decoding

    Ziwei Xie, Jinbo Xu

    Curated by Papers for Protein Design Using Deep Learning · 4.7 Transformer-based · 4.Scaffold to Sequence

  220. Deep Reasoning in General Purpose Agents via Structured Meta-Cognition

    Dean Light, Michael Theologitis, Kshitish Ghate, Shuyue Stella Li, Benjamin Newman, Chirag Shah, Aylin Caliskan, Pang Wei Koh, Dan Mircea Suciu, Yulia Tsvetkov

  221. TD3B: Transition-Directed Discrete Diffusion for Allosteric Binder Generation

    Hanqun Cao, Aastha Pal, Sophia Tang, Yinuo Zhang, Jingjie Zhang, Pheng Ann Heng, Pranam Chatterjee

    Curated by Papers for Protein Design Using Deep Learning · 4.9 Diffusion-based · 4.Scaffold to Sequence

  222. Generative models for antimicrobial peptide design: auto-encoders and beyond

    Lukas Beierle, Julian M. Hahnfeld, Alexander Goesmann, Reihaneh Mostolizadeh, Franz Cemič

    Curated by Papers for Protein Design Using Deep Learning · 1. Reviews · 1.3 Peptide design

  223. QuADMET-Former: Pre-training on Quantum Mechanical Properties Improves ADMET Prediction

    Arun Raja, Hongtao Zhao, Christian Tyrchan, Eva Nittinger, Michael M. Bronstein, Charlotte M. Deane, Garrett M. Morris

  224. SAbDab2 Machine Learning Dataset

    Henriette Capel, Odysseas Vavourakis, Ben H. Williams, Christopher R. Taylor, Charlotte Mary Deane

  225. Transforming the cytokine literature into a resource for experimental analysis and discovery

    Lukas Oesinghaus, Molly Park, Rulin Shao, Pang Wei Koh, Georg Seelig

  226. Training Transformers for KV Cache Compressibility

    Yoav Gelberg, Yam Eitan, Michael M. Bronstein, Yarin Gal, Haggai Maron

  227. SPARC: A mechanism-aware spatial representation from routine histology predicts cancer survival and therapy response

    Aziz Ayed, Gabrielle Cohn, Noe Bertramo, Genevieve Marie Boland, Justin Gainor, Ömer H. Yilmaz, Regina Barzilay

  228. Reverse engineering the fatally cross-reactive A3A TCR to decouple potency and specificity

    Julia V McCarthy, A. Christina Heroven, King Ifashe, Martin Fellermeyer, Dan Hudson, Raul Cioaca, Max N Quastel, Yunkai Yang, Matteo Cagiada, Simeon D. Dragonov, Christopher J. Thorpe, Adán Pinto-Fernández, Alexander Greenshields‐Watson, Gillespie, Charlotte Mary Deane, Ricardo A. Fernandes

  229. EvoLM: Self-Evolving Language Models through Co-Evolved Discriminative Rubrics

    Shuyue Stella Li, Rui Xin, Teng Xiao, Yike Wang, Rulin Shao, Zoey Hao, Melanie Sclar, Sewoong Oh, Faeze Brahman, Pang Wei Koh, Yulia Tsvetkov

  230. A-CODE: Fully Atomic Protein Co-Design with Unified Multimodal Diffusion

    Chaoran Cheng, Jiaqi Guan, Milong Ren, Chengyue Gong, Cong Liu, Xinshi Chen, Ge Liu, Wenzhi Xiao

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  231. Fast and Ultra-Capable Protein Design: Advancing the Frontier Through Atomistic SE(3)-Equivariance with Genie 3

    Yi‐Cheng Lin, Minji Lee, Aakarsh Vermani, Ellena Jiang, Sebastiaan De Cooman, Matěj Špeťko, Mohammed AlQuraishi

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  232. Emergence of specific binding and catalysis from a designed generalist binding protein

    Yuda Chen, Sagar Bhattacharya, Lena Bergmann, G.J. Correy, Sophia K. Tan, Kaipeng Hou, J.T. Biel, Lei Lu, Ian J. Bakanas, Jason E. Gestwicki, Alexander N. Volkov, Ivan V. Korendovych, Nicholas F. Polizzi, James S. Fraser, William F. DeGrado

  233. NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training

    Haokai Zhao, Da Xing, Hanqun Cao, Tinson Xu, Xinyu Xiang, Yanchao Li, Xiangru Tang, Hongbin Lin, Zehong Wang, Kuan Pang, Peng Xia, Molei Tao, Li Erran Li, Aditya Joshi, Jure Leskovec, Fang Wu

  234. Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

    Fang Wu, Weihao Xuan, Heli Qi, Hanqun Cao, Heng-Jui Chang, Zeqi Zhou, Haokai Zhao, Ma Jian, Carl Ma, Yu‐Chi Cheng, Kuan Pang, Xiangru Tang, Zehong Wang, Guanlue Li, Hanchen Wang, Kejun Ying, Pan Lu, Chiho Im, Seungju Han, Peng Xia, Tinson Xu, Yinxi Li, Deyao Zhu, Pheng-Ann Heng, Naoto Yokoya, Masashi Sugiyama, Li Erran Li, Jure Leskovec, Yejin Choi

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  235. Abstract 2231 Using neural networks to design proteins that bind and sense small-molecule drugs

    Nicholas F. Polizzi

  236. Artificial intelligence aided design of peptides with custom secondary structure motifs and reduced amino acid alphabets

    Sean M Brown, Ashley B. Cohen, Scott N. Dean

    Curated by Papers for Protein Design Using Deep Learning · 4.3 LSTM-based · 4.Scaffold to Sequence

  237. Toward life with a 19–amino acid alphabet through generative artificial intelligence design

    Liyuan Liu, Charlotte Rochereau, Simon Kozlov, Guillaume Urtecho, Xiaonan Liu, Jiahui Zhao, Jasmine Wang, Yiming Huang, Yiming Qu, Zetian Zhang, Tomasz Blazejewski, Avi Swartz, Sergey Ovchinnikov, Harris H. Wang

  238. A biologically annotated neural network for proteomic discovery in Parkinson’s disease

    Avish Vijayaraghavan, Lorin A. Crawford, Saboo Krishnakant, Ava P. Amini, Ashley Mae Conard, Abby L. Olsen, Lana M. Chahine, Kristen Severson

  239. TxConformal: Controlling False Discoveries in AI-Driven Therapeutic Discovery

    Ying Jin, Kexin Huang, Nathaniel Diamant, Kerry R. Buchholz, Steven T. Rutherford, Nicholas Skelton, Tommaso Biancalani, Gabriele Scalia, Jure Leskovec, Emmanuel J. Candès

  240. Author response for "A Machine Learning-Based Workflow for Transaminase Selection"

    Alexander J. Rago, Priyanka Raghavan, Lisandra Santiago-Capeles, Ruijie Zhang, ying wang, Connor W. Coley

  241. Towards A Generative Protein Evolution Machine with DPLM-Evo

    Xinyou Wang, Liang Hong, Jiasheng Ye, Zaixiang Zheng, Yu Li, Shujian Huang, Quanquan Gu

    Curated by Papers for Protein Design Using Deep Learning · 5.12 Diffusion-based · 5.Function to Sequence

  242. Computational design of an ultrapotent deltacoronavirus miniprotein inhibitor

    Nathan G Avery, Courtney N. Yoshiyama, Ashley L Taylor, Young‐Jun Park, Daniel E. Asarnow, Lisa Perruzza, Jack T Brown, Davide Corti, Fabio Benigni, Tyler N. Starr, David Veesler

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  243. Technical and biological sources of noise confound multiplexed enhancer AAV screening

    Avery C. Hunker, John K. Mich, Naz Taskin, Amy Torkelson, Trangthanh Cardenas, Jean‐Benoît Lalanne, Joseph T. Mahoney, Darren Bertagnolli, Anish Bhaswanth Chakka, Rushil Chakrabarty, Nicholas P. Donadio, Rebecca Ferrer, Molly J. Gasperini, Jeff Goldy, Junitta Guzman, Kelly Jin, Shannon Khem, Rana Kutsal, Refugio A. Martinez, Dakota Newman, Nick Pena, Christine Rimorin, Dana Rocha, Nadiya V. Shapovalova, Michael Tieu, Natalie Weed, Thomas Zhou, Rebecca Hodge, Shenqin Yao, Jay Shendure, Kimberly A. Smith, Ed S. Lein, Bosiljka Tasic, Boaz P. Levi, Jonathan T. Ting

  244. Pool-packaged AAV libraries exhibit extensive length-dependent and homology-dependent chimerism

    Jean‐Benoît Lalanne, Chau Huynh, John K. Mich, Avery C. Hunker, Troy A. McDiarmid, Haedong Kim, Boaz P. Levi, Jonathan T. Ting, Jay Shendure

  245. Optics-free reconstruction of shapes, images and volumes with DNA barcode proximity graphs

    Hanna Liao, Sanjay V. Kottapalli, Yuqi Huang, Matthew Chaw, Jase Gehring, Olivia Waltner, Melissa Phung-Rojas, Riza M. Daza, Frederick A. Matsen IV, Cole Trapnell, Jay Shendure, Sanjay R Srivatsan

  246. Reviewer #1 (Public review): Optics-free reconstruction of shapes, images and volumes with DNA barcode proximity graphs

    Hanna Liao, Sanjay V. Kottapalli, Yuqi Huang, Matthew Chaw, Jase Gehring, Olivia Waltner, Melissa Phung-Rojas, Riza M. Daza, Frederick A. Matsen IV, Cole Trapnell, Jay Shendure, Sanjay R Srivatsan

  247. Author response: Optics-free reconstruction of shapes, images and volumes with DNA barcode proximity graphs

    Hanna Liao, Sanjay V. Kottapalli, Yuqi Huang, Matthew Chaw, Jase Gehring, Olivia Waltner, Melissa Phung-Rojas, Riza M. Daza, Frederick A. Matsen IV, Cole Trapnell, Jay Shendure, Sanjay R Srivatsan

  248. TcrDesign: de novo design of epitope-specific full-length T cell receptors

    Kaixuan Diao, Jing Chen, Xiangyu Zhao, Tao Wu, Die Qiu, W Wang, Haopeng Wang, Xue-Song Liu

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  249. Designed Minibinders Rewire Receptor Signaling to Enable Functional Human Myogenic Reprogramming

    Riya Keshri, Zachary Foreman, Philip Barrett, Alexander J. Robinson, Gabriela Reyes, Ashish Phal, Aditya Krishnakumar, Ethan Narog, Melodie Chiu, Shruti Jain, Xinru Wang, David J. Lee, Marc Expòsit, Mohamad H. Abedi, Alec S.T. Smith, Sanjay R Srivatsan, Jay Shendure, Julie Mathieu, David L. Mack, David A. Baker, David A. Baker

  250. Generative design of sequence specific DNA binding proteins

    Enisha Sehgal, Yuliya Politanska, Raktim Mitra, Paul T Kim, Nayim González-Rodríguez, Tushar Warrier, Andrew Kubaney, Akira Morishita, Riley N. Quijano, Jasper Butcher, Rohith Krishna, Robert J. Pecoraro, Brian J. Belmont, Nicole Roullier, Inna Goreshnik, Dionne K. Vafeados, Paul S. Kwon, Rachana Ramarao, Jussi Taipale, Cameron J. Glasscock, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  251. Are Current AI Virtual Cell Models Useful for Scientific Discovery?

    Michael D. Bereket, Jure Leskovec

  252. The proteomic landscape and temporal dynamics of human and mouse gastruloid development

    Riddhiman K. Garge, Valerie Lynch, Rose Fields, Silvia Casadei, Sabrina Best, Jeremy Stone, Matthew W. Snyder, Connor A. Kubo, Arata Wakimoto, Zukai Liu, Christopher D. McGann, Jay Shendure, Lea M. Starita, Nobuhiko Hamazaki, Devin K. Schweppe

  253. Using experimental results of protein design to guide biomolecular energy-function development

    Hugh K. Haddox, Gabriel J. Rocklin, Francis C. Motta, Devin Strickland, Samer F. Halabiya, Cameron Cordray, Hahnbeom Park, Eric Klavins, David A. Baker, Frank DiMaio

  254. De novo masking domains that gate TNF-family ligand assembly and activity

    Shota Kudo, Brian A. Kuhlman

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  255. Hydrolysis Reaction Rate Prediction Using Machine Learning: WaterDRoP

    Amélie C. Lemay, Connor W. Coley, Desirée L. Plata

  256. Multi-scale dissection, compaction and derivatization of mammalian developmental enhancers

    Jean‐Benoît Lalanne, Tony Li, Emma A.N. Kajiwara, Chau Huynh, Tiffany V. Do, Beth K. Martin, Samuel G. Regalado, Jay Shendure

  257. Retracing and rewriting the evolutionary trajectories of mammalian developmental enhancers

    Tony Li, Jean‐Benoît Lalanne, Emma A.N. Kajiwara, Shruti Jain, Xiaoyi Li, Tiffany V. Do, Beth K. Martin, Samuel G. Regalado, Riza M. Daza, Jay Shendure

  258. AbFlow: End-to-end Paratope-Centric Antibody Design by Interaction Enhanced Flow Matching

    Wenda Wang, Yang Zhang, Zhewei Wei, Wenbing Huang

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.8 Flow-based

  259. Closing the loop: Experimentally validated methods in artificial intelligence–driven protein design

    Clayton W. Kosonocky, Sarah Alamdari, Kevin Yang, Ava P. Amini

  260. RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design

    Meghana Kshirsagar, Allen Nie, Ching-An Cheng, Fanglei Xue, Rahul Dodhia, Juan Lavista Ferres, Kevin K. Yang, Frank DiMaio

  261. The Synthetic Epitope Atlas: High-Throughput Design and Validation of De Novo Antibody-Antigen Complexes

    Nicholas Altieri, Joseph L. Harman, David Noble, Natasha Seelam Murakowska, Alexander Eng, Kerry L. McGowan, Davis Goodnight, Lucian DiPeso, Colleen Shikany, Emily Engelhart, Leah J. Homad, Miranda C. Lahman, Shyam Gandhi, Mackenzie Goodwin, Kendrick Herbst, Charles Lin, Margot McMurray, Juliana Barrett, Aditya A. Agarwal, James Harrang, Ryan Emerson, Randolph M. Lopez, David A. Younger, Adrian W. Lange

    Curated by Papers for Protein Design Using Deep Learning · 4.9 Diffusion-based · 4.Scaffold to Sequence

  262. FRIGID: Scaling Diffusion-Based Molecular Generation from Mass Spectra at Training and Inference Time

    Montgomery Bohde, Hongxuan Liu, Mrunali Manjrekar, Magdalena Lederbauer, Shuiwang Ji, Runzhong Wang, Connor W. Coley

  263. Sampling antibody conformational ensembles with ABodyBuilder4-STEROIDS

    Fabian C. Spoendlin, Matteo Cagiada, King Ifashe, Odysseas Vavourakis, Charlotte Mary Deane

  264. mRNAutilus Training Data (Pretraining + Classifiers)

    Smit Patel, Pranam Chatterjee, Sherwood Yao, Sophia Tang

  265. Generative design of intrinsically disordered proteins based on conditioned protein language models: Data is the limit

    Laure Carriere, Alexandre Huyghe, Mátyás Pajkos, Pau Bernadó, Juan Cortés

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  266. Evolutionary conditioning enables guided generation of functionally diverse enhancers

    Andrew Duncan, Micaela Elisa Consens, Lorin A. Crawford, Jennifer A. Mitchell, Alan M Moses, Kevin Yang, Alex X Lu

  267. A Hybrid Physics-Deep Learning Framework for Combinatorial De Novo Design of Small-Molecule Binding Proteins

    Connor V. Galvin, Amy B. Guo, Maple N. Chen, Isabella Alfonso, Dominic Waldo Grisingher, Simon Kretschmer, Divya Kranthi, Mark J. S. Kelly, Tanja Kortemme

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  268. Perturbing the energy landscape for improved packing during computational protein design

    Samer F. Halabiya, Jermel R. Griffin, Hugh K. Haddox, Devin Strickland, David F. Thieker, Thomas Szyperski, Jack B. Maguire, Brian Coventry, Surya V. S. R. K. Pulavarti, Matthew C. Cummins, Brian A. Kuhlman, David A. Baker, Eric Klavins, Frank DiMaio

  269. KumoRFM-2: Scaling Foundation Models for Relational Learning

    Valter Hudovernik, Federico Elizondo Lopez, Vid Kocijan, Akihiro Nitta, Jan Eric Lenssen, Jure Leskovec, Matthias Fey

  270. tRNA-deacylase-directed discovery of biosynthetic pathways

    Douglas C. Millar, Yu Zhou, Jorge A. Marchand, Helena Roberts-Mataric, Tavis Chen, Calvin X. Hu, Jason Fang, Shannon Millar, Elizabeth A. Stone, Jeffrey G. Pelton, Max B. Sosa, Michelle C. Y. Chang

  271. A Mechanistic Analysis of Looped Reasoning Language Models

    Hugh Blayney, Álvaro Arroyo, Johan Obando-Ceron, Pablo Samuel Castro, Aaron C. Courville, Michael M. Bronstein, Xiaowen Dong

  272. Steering generative models for protein design: Aligning and conditioning strategies

    Filippo Stocco, Michele Garibbo, Noelia Ferruz

    Curated by Papers for Protein Design Using Deep Learning · 1. Reviews · 1.1 De novo protein design

  273. Generative design of intrinsically disordered protein regions with IDiom

    Jason Liu, Sebastian Ibarraran, Frank Hu, Abigail Park, Alexander R. Dunn, Grant M. Rotskoff

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  274. iNOS modulates inflammatory responses in an NO-independent manner through direct interaction with IRG1 in mitochondria

    Marina Diotallevi, Carlos Outeiral, Priyanka Patel, Gareth S. D. Purvis, Surawee Chuaiphichai, Thomas Nicol, Faseeha Ayaz, Daniel Allen Nissley, Ganna O. Krasnoselska, Svenja Hester, John H. McVey, Román Fischer, Benedikt Mathias Kessler, Charlotte Mary Deane, Raymond J. Owens, Keith M. Channon, Mark J. Crabtree

  275. AI-Designed Peptides as Tools for Biochemistry

    Lauren Hong, Sophia Vincoff, Pranam Chatterjee

  276. Sequence Display enables large-scale sequence–activity datasets for rapid protein evolution

    Linqi Cheng, Xinzhe Zheng, Shiyu Jiang, Yu Hu, Y. N. Liu, Kaiqiang Yang, Jinyan Rui, Haoxue Ding, Mengxi Zhang, Teng Yuan, Qianglan Lu, Haoxin Leo Ye, C. Li, Yiming Guo, Zuotong Tian, Anna Qin, Boyang Zhou, Kevin Yang, Xiongyi Huang, Han Xiao

  277. CytED and test datasets for pyCytED

    Lukas Oesinghaus, Molly Park, Rulin Shao, Pang Wei Koh, Georg Seelig

  278. Sampling antibody conformational ensembles with ABodyBuilder4-STEROIDS Dataset

    King Ifashe, Fabian C. Spoendlin, Matteo Cagiada, Charlotte Mary Deane

  279. Evolutionary transfer learning enables organism-wide inference of mammalian enhancer landscapes

    Chengxiang Qiu, Riza M. Daza, Ian C. Welsh, Rupali P Patwardhan, Beth K. Martin, Tony W. Li, Shizhao Yang, Camiel C. A. Mannens, Seppe De Winter, Niklas Kempynck, Megan L. Taylor, Olivia Fulton, Truc-Mai Le, Diana R. O’Day, Jean‐Benoît Lalanne, Silvia Domcke, Stephen A. Murray, Stein Aerts, Cole Trapnell, Jay Shendure

  280. How Well Do Agentic Skills Work in the Wild: Benchmarking LLM Skill Usage in Realistic Settings

    Yujian Liu, Jiabao Ji, Li An, Tommi Jaakkola, Yang Zhang, Shiyu Chang

  281. General Multimodal Protein Design Enables DNA-Encoding of Chemistry

    Jarrid Rector-Brooks, Théophile Lambert, Marta Skreta, Daniel Roth, Yueming Long, Zi-Qi Li, Xi Zhang, Miruna Cretu, Francesca-Zhoufan Li, Tanvi Ganapathy, Emily Jin, Avishek Joey Bose, Jason Yang, Kirill Neklyudov, Yoshua Bengio, Alexander Tong, Frances H. Arnold, Chenghao Liu

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  282. A Rapid and Universal Pipeline for High-Resolution GPCR Structure Determination through In Silico Construct Optimization and de novo Protein Design

    Asato Kojima, Kouki Kawakami, Naoya Kobayashi, Kazuhiro Kobayashi, Toshiki E. Matsui, Kohei Uemoto, Yuzhong Gu, Masahiro Fukuda, Hideaki E. KATO

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  283. High-PepBinder: A pLM-Guided Latent Diffusion Framework for Affinity-Aware Target-Specific Peptide Design

    Qingyi Mao, Silong Zhai, Sen Cao, Renjie Zhu, Wen Xu, Chengyun Zhang, Zhu Ning, Jingjing Guo, Hongliang Duan

    Curated by Papers for Protein Design Using Deep Learning · 5.12 Diffusion-based · 5.Function to Sequence

  284. Abstract 5484: Designing ATP-gated proteins for tumor selective drug delivery.

    Angela Mei, Azim M. Dharani, Jody Mou, Benjamin N. Fry, Nicholas F. Polizzi

  285. AI-based methods for simulating, sampling, and predicting protein ensembles

    Bowen Jing, Bonnie Berger, Tommi Jaakkola

  286. Tackling the complexity of cancer with generative models

    Ashley Mae Conard, Madeline Hughes, James Hall, Neil Tenenholtz, Eric Zimmermann, Lorin A. Crawford, Ava P. Amini, Kristen Severson

  287. Protein FID: improved evaluation of protein structure generative models

    Felix Faltings, Hannes Stark, Tommi Jaakkola, Regina Barzilay

  288. Universal Cell Embeddings provides a Foundation Model for Cell Biology

    Yanay Rosen, Yusuf Roohani, Ayush Agrawal, Leon Samotorcan, Tabula Sapiens Consortium, Stephen R. Quake, Jure Leskovec

  289. Online Reasoning Calibration: Test-Time Training Enables Generalizable Conformal LLM Reasoning

    Cai Zhou, Zekai Wang, Menghua Wu, Qianyu Julie Zhu, Feng Shi, Chenyu Wang, Ashia Wilson, Tommi Jaakkola, Stephen Bates

  290. Rational discovery of therapeutic PAK1 allosteric activators

    Yu Hui He, James S.H. Bae, Elzbieta Nowak, Carlos Outeiral, Daniel Allen Nissley, Anthony Tumber, G. Berridge, E. Salah, Yi Wang, Wenqi He, Hongyuan Zhang, Tangting Chen, Samuel E. Tusk, Sebastian Mathea, Ying-Jie Wang, Alexander Grassam-Rowe, Philipp Kukura, Christopher John Schofield, Darragh Patrick O'Brien, Andrea Pierangelini, Grant C. Churchill, Thomas Lanyon‐Hogg, Yunbo Ke, Chao Xu, Tao Ye, Hugh C Watkins, Liming Ying, Andreas Koschinski, R John Solaro, X Tan, Jani Reddy Bolla, Xin Wang, Stefan Knapp, Charlotte Mary Deane, Manuela Zaccolo, Marcin Nowotny, Ming Lei

  291. Scaling Atomistic Protein Binder Design with Generative Pretraining and Test-Time Compute

    Kieran Didi, Zuobai Zhang, Guoqing Zhou, Danny Reidenbach, Zhonglin Cao, Sooyoung Cha, Tomas Geffner, Christian Dallago, Jian Tang, Michael M. Bronstein, Martin Steinegger, Emine Küçükbenli, Arash Vahdat, Karsten Kreis

  292. Symmetric Self-play Online Preference Optimization for Protein Inverse Folding

    Wenwu Zeng, Xiaodong Li, H. Xuanpengfan Zou, Yutao Dou, Xiongjun Zhao, Shaoliang Peng

    Curated by Papers for Protein Design Using Deep Learning · 3. Function to Scaffold · 3.5 RL-based

  293. GYDE: A collaborative drug discovery platform for AI-powered protein design and engineering

    Thomas A. Down, Mateusz Warowny, April Walker, Luigi D’Ascenzo, Donald Lee, Zhenru Zhou, Shengya Cao, Travis W. Bainbridge, John M. Nicoludis, Seth F. Harris, Kiran Mukhyala

    Curated by Papers for Protein Design Using Deep Learning · 7. Other tasks · 7.4 Framework

  294. Description of a collaborative sperm whale birth and shifts in coda vocal styles during key events

    Yaniv Aluma, Zethra Baron, Ricardo Barrett, Courtney Baumgartner, Gašper Beguš, Sushmita Bhattacharya, Michael M. Bronstein, Shlomi Dahan, Oscar Davis, Sarah de Haas, Jullan Defoe, Joseph DelPreto, Roberto Dessì, Roee Diamant, John Gatesy, Kevin George, Shane Gero, Darren Gibbons, Dean Gibbons, Stephanie Gil, Shafi Goldwasser, David F. Gruber, Odel Harve, Alyssa M. Hernandez, Mapal Ishay, Ninad Jadhav, Lakshyana KC, Aidan Kenny, Antonio Leitao, Maxime Lucas, Alaa Maalouf, Peter Malkin, Yaly Mevorach, Stefano Pagani, Orr Paradise, Giovanni Petri, Simone Poetto, Emanuele Rossi, Daniela L. Rus, Michael Salino-Hugg, Andrea Santoro, Pratyusha Sharma, Dan Tchernov, Antonio B. Torralba, Pernille Tønnesen, Daniel M. Vogt, Robert J. Wood

  295. Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability

    Ana F. Rodrigues, Lucas Ferraz, Laura Balbi, Pedro Giesteira Cotovio, Cátia Pesquita

    Curated by Papers for Protein Design Using Deep Learning · 7. Other tasks · 7.1 Effects of mutation & Fitness Landscape

  296. Remembrance of inflammations past

    Chengxiang Qiu, Jay Shendure

  297. Collaborative AI agents

    Shirley Yingxin Wu, Jure Leskovec, James Zou, Diyi Yang, Stanford University. School of Engineering., Stanford University. Computer Science Department.

  298. Polymerized Short Sequences as a Template for Protein Folding and Evolution

    Tianyi Jin, Jacob Sass, Wenhao Gao, Aiden A. Hurst, Connor W. Coley, Alfredo Alexander‐Katz

  299. ProteinMCP : An agentic AI framework for autonomous protein engineering

    Xiaopeng Xu, Chenjie Feng, Chao Zha, Wenjia He, Maolin He, Bin Xiao, X.-J. Gao

    Curated by Papers for Protein Design Using Deep Learning · 7. Other tasks · 7.4 Framework

  300. CombinGym: a benchmark platform for machine learning-assisted design of combinatorial protein variants

    Yongcan Chen, Lihao Fu, Xuchao Lu, Wenzhuo Li, Yuan Gao, Yibo Wang, Zhicheng Ruan, Tong Si

    Curated by Papers for Protein Design Using Deep Learning · 7. Other tasks · 7.1 Effects of mutation & Fitness Landscape

  301. Can Graph Foundation Models Generalize Over Architecture?

    Benjamin Gutteridge, Michael M. Bronstein, Xiaowen Dong

  302. AssayMatch: Learning To Select Data for Molecular Activity Models

    Vincent Fan, Regina Barzilay

  303. Reinforcement-guided generative protein language models enable de novo design of highly diverse AAV capsids

    Lucas Ferraz, Ana F. Rodrigues, Pedro Giesteira Cotovio, Mafalda Ventura, Gabriela Silva, Ana Sofia Coroadinha, Miguel Machuqueiro, Cátia Pesquita

    Curated by Papers for Protein Design Using Deep Learning · 5.6 RL-based · 5.Function to Sequence

  304. Computational design of functional random heteropolymers through atomistic simulations

    Tianyi Jin, Collin S. Lung, Ting Xu, Connor W. Coley, Alfredo Alexander‐Katz

  305. AI-enabled protein design facilitates future plant research and crop breeding

    Yuxuan Lou, Tianhao Wu, Fan Xia, Anwen Zhao, Xiangfeng Wang

    Curated by Papers for Protein Design Using Deep Learning · 1. Reviews · 1.1 De novo protein design

  306. Drugs and where to find them : multi-scale artificial intelligence methods for identifying ligand-binding pockets and opioid-related social media posts

    Kristy A. Carpenter, Russ Biagio Altman, Polly Morrell Fordyce, Jure Leskovec, Ying Lü, Stanford University. School of Medicine., Stanford University. Department of Biomedical Informatics.

  307. Improved vector toolkit for genome writing in mammalian cells

    Kelly Barriball, Brianna Berrios, Camila Coelho, Sudarshan Pinglay, Yu Zhao, Noor Chalhoub, Tiffany Tsou, John T. Atwater, Jef D. Boeke, Weimin Zhang, Ran Brosh

  308. Companion data for CytED construction software

    Lukas Oesinghaus, Molly Park, Rulin Shao, Pang Wei Koh, Georg Seelig

  309. Advancing Protein Design via Multi-Agent Reinforcement Learning with Pareto-Based Collaborative Optimization

    Mingming Zhu, Jiahua Rao, Xiaoyu Chen, Qianmu Yuan, Yuedong Yang

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.5 Sampling-algorithms

  310. Real-world causal inference for healthcare and public policy

    Hamed Nilforoshan, Jure Leskovec, Emma Brunskill, Stefan Wager, Stanford University. School of Engineering., Stanford University. Computer Science Department.

  311. De Novo Multi‐Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning

    Xiaojuan Li, Haifan Gong, Yue Wang, Yinuo Zhao, Lei Li, Peijing Bao, Qingzhou Kong, Jialu Fu, Boyao Wan, Yumeng Zhang, Jinghui Zhang, Jiekun Ni, Zhongxue Han, Xueping Nan, Kunping Ju, Longfei Sun, Yuerui Ma, Huijun Chang, Mengqi Zheng, Yanbo Yu, Xiaoyun Yang, Xiuli Zuo, Haina Wang, Yanqing Li

    Curated by Papers for Protein Design Using Deep Learning · 5.2 VAE-based · 5.Function to Sequence

  312. Higher-Level Strategies for Computer-Aided Retrosynthesis

    Jihye Roh, Joonyoung F. Joung, Kevin Yu, Zhengkai Tu, G. Logan Bartholomew, Omar A. Santiago-Reyes, Mun Hong Fong, Richmond Sarpong, Sarah E. Reisman, Connor W. Coley

  313. Functional reorganization of motor cortex connectivity during learning

    Kayvon Daie, Kyle Aitken, Márton Rózsa, Matthew S. Bull, Peter C. Humphreys, Ziheng Christina Wang, Lucas W. Kinsey, Mohit Kulkarni, Kimberly Stachenfeld, Maria K. Eckstein, Zeb L. Kurth-Nelson, Claudia Clopath A, Timothy Lillicrap, Matthew Botvinick, Matthew D. Golub, Ştefan Mihalaş, Karel Svoboda

  314. An Energy Landscape Approach to Miniaturizing Enzymes using Protein Language Model Embeddings

    Jakub Lála, Harsh Agrawal, Fanfei Dong, Jude Wells, Stefano Angioletti‐Uberti

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.4 LLM-based

  315. Miniprotein inhibitors of the Staphylococcus aureus efflux transporter NorA

    Priyanka Mishra, Adam M. Chazin-Gray, Gaëlle Lamon, David E. Kim, David A. Baker, Nathaniel J. Traaseth

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  316. Genome modelling and design across all domains of life with Evo 2

    Garyk Brixi, Matthew G. Durrant, Jerome Ku, Mohsen Naghipourfar, Michael Poli, Gwanggyu Sun, Greg Brockman, Daniel Chang, Alison Fanton, Gabriel A. Gonzalez, Samuel H. King, David B. Li, Aditi T. Merchant, Eric Nguyen, Chiara Ricci-Tam, David W. Romero, Jonathan C. Schmok, Ali Taghibakhshi, Anton Vorontsov, Brandon Yang, Myra Deng, Liv Gorton, Nam‐Ky Nguyen, Nicholas K. Wang, Michael T. Pearce, Elana P. Simon, Etowah Adams, Zachary J. Amador, Euan Angus Ashley, Stephen A. Baccus, Haoyu Dai, Steven Dillmann, Stefano Ermon, Daniel Guo, Michael H. Herschl, Rajesh Ilango, Ken Janik, Amy X. Lu, Reshma Mehta, Mohammad R. K. Mofrad, Madelena Y. Ng, Jaspreet Pannu, Christopher Ré, John St. John, Jeremy Sullivan, Joseph Tey, Ben T Viggiano, Kevin Zhu, Gregory J. Zynda, Daniel Balsam, Patrick Collison, Anthony B. Costa, Tina Hernandez‐Boussard, Eric Ho, Mingyu Liu, Thomas McGrath, Kimberly Powell, Sudarshan Pinglay, Dave P. Burke, Hani Goodarzi, Patrick D. Hsu, Brian Hie

  317. Grand Challenges for Predictive Modeling in Small Molecule Drug Discovery

    Connor W. Coley, Pankaj Daga, Marco De Vivo, Willem Jespers, Ashutosh S. Jogalekar, S Roy Kimura, Lucien Koenekoop, Anne‐Grete Märtson, Timothy R. Newhouse, Soumya Ray, Riccardo Sabatini, David C Thompson, Woody Sherman

  318. Co-designing sequence and structure of functional de novo enzymes with EnzyGen2

    Zhenqiao Song, Huichong Liu, Yunlong Zhao, Yang Yang, Lei Li

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.6 Transformer-based

  319. Deep learning-guided evolutionary optimization for protein design

    Erik Hartman, Di Tang, Johan A. Malmström

    Curated by Papers for Protein Design Using Deep Learning · 7. Other tasks · 7.1 Effects of mutation & Fitness Landscape

  320. PanCanBench: A Comprehensive Benchmark for Evaluating Large Language Models in Pancreatic Oncology

    Yimin Zhao, Sheela Damle, Simone E. Dekker, Scott Geng, Karly Williams Silva, Jesse J. Hubbard, Manuel Fernández, Fatima Zelada-Arenas, Alejandra Alvarez, Brianne Flores, Alexis Rodriguez, Stephen M. Salerno, Carrie Wright, Zihao Wang, Pang Wei Koh, Jeffrey T. Leek

  321. Branched Schrödinger Bridge Matching.

    Sophia Tang, Yinuo Zhang, Alexander Tong, Pranam Chatterjee

  322. Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles

    Zhanghan Ni, Yanjing Li, Zeju Qiu, Bernhard Schölkopf, Hongyu Guo, Weiyang Liu, Shengchao Liu

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.8 Flow-based

  323. AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2

    Artem Gazizov, Anna Lian, Casper A. Goverde, Jody Mou, Sergey Ovchinnikov, Nicholas F. Polizzi

  324. A False Sense of Privacy: Evaluating Textual Data Sanitization Beyond Surface-level Privacy Leakage

    Rui Xin, Niloofar Mireshghallah, Shuyue Stella Li, Michael Duan, Hyunwoo Kim, Yejin Choi, Yulia Tsvetkov, Sewoong Oh, Pang Wei Koh

  325. Bridging the gap: aligning clinical decision support regulation with clinical practice in the era of artificial intelligence

    Maëlle-Marie Corso, Paul T Kim, Brenna T. Loufek, Leslie R. Lamb, Regina Barzilay

  326. Scalable nonparametric clustering with unified marker gene selection for single-cell RNA-seq data

    Chibuikem Chukwudalu Nwizu, Madeline Hughes, Michelle L. Ramseier, Andrew W. Navia, Alex K. Shalek, Nicolò Fusi, Srivatsan Raghavan, Peter S. Winter, Ava P. Amini, Lorin A. Crawford

  327. BioPipelines: Accessible Computational Protein and Ligand Design for Chemical Biologists

    Gianluca Quargnali, Pablo Rivera‐Fuentes

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  328. Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms

    Yanlin Mi, Arpit Shukla, Mark Tangney, Sabin Tabirca, Venkata V. B. Yallapragada

    Curated by Papers for Protein Design Using Deep Learning · 1. Reviews · 1.1 De novo protein design

  329. Generative design and validation of therapeutic peptides for glioblastoma based on a potential target ATP5A

    Hao Qian, Pu You, Lin Zeng, Jingyuan Zhou, Dengdeng Huang, Kaicheng Li, Shikui Tu, Lei Xu

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.8 Flow-based

  330. FLIP2: Expanding Protein Fitness Landscape Benchmarks for Real-World Machine Learning Applications

    Kieran Didi, Sarah Alamdari, Alex Xijie Lu, Bruce J. Wittmann, Kadina E. Johnston, Ava P. Amini, Ali Madani, Maya Czeneszew, Christian Dallago, Kevin K. Yang

  331. Beyond alignment: synergistic integration is required for multimodal cell foundation models

    Till Richter, Eric Zimmermann, James Hall, Fabian Joachim Theis, Srivatsan Raghavan, Peter S. Winter, Ava P. Amini, Lorin A. Crawford

  332. Zatom-1: Towards a Multimodal Foundation Model for 3D Molecules and Materials

    Alex Morehead, Miruna Cretu, Antonia Panescu, Rishabh Anand, Maurice Weiler, Tynan J. Perez, Samuel M. Blau, Steven Farrell, W. Bhimji, Anubhav Jain, Hrushikesh P. Sahasrabuddhe, Pietro Lio, Tommi Jaakkola, Rafael Gómez‐Bombarelli, Rex Ying, N. Benjamin Erichson, Michael W. Mahoney

  333. A Chemoselective and Stereodivergent Platform of Heme‐Nitrene Transferases to Access Chiral Aryl‐β‐Amino Esters and An Investigation of the Sequence‐Activity Landscape

    Julia C. Reisenbauer, Hayden M. Carder, Ariane Mora, Ana Jaramillo, Frances H. Arnold

  334. The Next Generation of Protein Sequencing and Analysis Methods

    K. C. Wong, Mattias Tolhurst, Oren A. Fox, Safwan Diwan, Nicholas Bogard, Jeff Nivala

  335. PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment

    Akshay Subramanian, Elton Pan, Juno Nam, Maurice Weiler, Shuhui Qu, Cheol Woo Park, Tommi Jaakkola, Elsa Olivetti, Rafael Gómez‐Bombarelli

  336. 2025 Ginkgo Datapoints Antibody Developability Competition outcomes: limited model performance and a call for data standardization

    Lood van Niekerk, Joshua Moller, Seth Ritter, Porfirio Quintero-Cadena, Rich Cohen, Georgia Channing, Michael Chungyoun, Laura M. Rand, Alexander Smith, Aanal Bhatt, Yolaine Pierre, Blake Harris, Xiang Ao, Lucía Daniela Grippo, Maximilian Schwenk, Adam Rosenbaum, Olga Allen, Nimra Asi, Jiang Zhu, Aviral Singh, Daksh Sammi, Rushikesh Jadhav, Antonín Dušek, Shyam Chandra, Valentin Badea, Nels Thorsteinson, Nathaniel Blalock, Jeonghyeon Kim, Oliver M. Turnbull, Ameya Kulkarni, Vivek Kohar, Netsanet Gebremedhin, Charlotte Mary Deane, Peter M. Tessier, Ammar Arsiwala

  337. Reprogramming CAR T-Cells with designed bioPROTACs

    Vivek S. Peche, Sebastian Kenny, Tae Gun Kang, Brian Coventry, Tian Mi, Inna Goreshnik, Mariana Garcia Sanchez, Reid Martin, Macey Smith, Dionne K. Vafeados, Rahul S. Kathayat, Yu Kaiwen, Zuo-Fei Yuan, Long Wu, Anthony A. High, Andrew N. Nemecek, Elizabeth Wickman, Adeleye Oluwatosin Adeshakin, Francesca Ferrara, Robert E. Throm, Taosheng Chen, Ben Youngblood, David A. Baker, Stephen M Gottschalk

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  338. LICHEN enables light-chain immunoglobulin sequence generation conditioned on the heavy chain and experimental needs

    Henriette Capel, Isaac Ellmen, Chris J. Murray, Giulia Mignone, Megan Black, Brendan Clarke, Conor Breen, Sean Tierney, Patrick Dougan, Richard J. Buick, Alexander Greenshields‐Watson, Charlotte Mary Deane

  339. A multiplex, prime editing framework for identifying drug resistance variants at scale

    Florence M.C. Abadie, Chase C. Suiter, Nahum T. Smith, Riza M. Daza, Mary C Rominger, Phoebe C. R. Parrish, Troy A. McDiarmid, Jean‐Benoît Lalanne, Beth Martin, Diego Calderon, Amira Ellison, Sitapriya Moorthi, Jay Shendure, Lea M. Starita

  340. Rapid directed evolution guided by protein language models and epistatic interactions

    Vincent Q. Tran, Matthew Nemeth, Liam J. Bartie, Sita S. Chandrasekaran, Alison Fanton, Hyungseok C. Moon, Brian Hie, Silvana Konermann, Patrick D. Hsu

  341. Moving the antibody: Molecular dynamics for molecular mechanisms and developability

    Matteo Cagiada, Charlotte Mary Deane

  342. Revised estimates of the types and durations of long Covid symptoms based on claims records from 245 Million US patients

    Hamed Nilforoshan, Julia Reisler, Erfan Jahanparast, Michael Moor, Steven N. Goodman, Stefan Wager, Jure Leskovec

  343. ACM TechBrief: Buy versus Build an LLM

    Joan Lu, Ziwei Xu, William Chandra Tjhi, Junnan Li, Antoine Bosselut, Pang Wei Koh, Mohan Kankanhalli

  344. ProtFlow: Flow Matching-based Protein Sequence Design with Comprehensive Protein Semantic Distribution Learning and High-quality Generation

    Zitai Kong, Yiheng Zhu, Yinlong Xu, Mingze Yin, Tingjun Hou, Jian Wu, Hongxia Xu, Chang‐Yu Hsieh

    Curated by Papers for Protein Design Using Deep Learning · 5.7 Flow-based · 5.Function to Sequence

  345. Hierarchical latent representations reveal protein organization for functional discovery and design

    Zhengyang Guo, Zi Wang, Shimin Wang, Yongping Chai, Kaiming Xu, Ming Li, Wei Li, Guangshuo Ou

    Curated by Papers for Protein Design Using Deep Learning · 7. Other tasks · 7.1 Effects of mutation & Fitness Landscape

  346. Rethinking Diffusion Models with Symmetries through Canonicalization with Applications to Molecular Graph Generation

    Cai Zhou, Zijie Chen, Zian Li, Jike Wang, Kaiyi Jiang, Li, Pan, Rose Yu, Muhan Zhang, Stephen Bates, Tommi Jaakkola

  347. Cold-Start Personalization via Training-Free Priors from Structured World Models

    Avinandan Bose, Shuyue Stella Li, Faeze Brahman, Pang Wei Koh, Simon S. Du, Yulia Tsvetkov, Maryam Fazel, Lin Xiao, Asli Celikyilmaz

  348. MacroGuide: Topological Guidance for Macrocycle Generation

    Alicja W. Maksymiuk, Alexandre Duplessis, Michael M. Bronstein, Alexander Tong, Fernanda Santos Duarte, İsmail İlkan Ceylan

  349. ALMo: Interactive Aim-Limit-Defined, Multi-Objective System for Personalized High-Dose-Rate Brachytherapy Treatment Planning and Visualization for Cervical Cancer

    Edward Chen, Natalie Dullerud, Pang Wei Koh, Thomas R Niedermayr, Elizabeth A. Kidd, Sanmi Koyejo, Carlos Guestrin

  350. RelBench v2: A Large-Scale Benchmark and Repository for Relational Data

    Justin Gu, Rishabh Ranjan, Charilaos I. Kanatsoulis, Haiming Tang, Martin Jurkovic, Valter Hudovernik, Mark Znidar, Pranshu Chaturvedi, Parth Shroff, Li, Fengyu, Jure Leskovec

  351. Buy versus Build an LLM: A Decision Framework for Governments

    Jiahao Lu, Ziwei Xu, William Tjhi, Junnan Li, Antoine Bosselut, Pang Wei Koh, Mohan Kankanhalli

  352. A geometric foundation model for enzyme retrieval with evolutionary insights

    Yong Liu, Chenqing Hua, Menglong Xu, Tao Zeng, Jiahua Rao, Zhongyue Zhang, Rongjie Wu, Jing‐Ke Weng, Connor W. Coley, Shuangjia Zheng

  353. Data and Model for Quantifying the Failure Modes of Current One-step Retrosynthesis Models

    Suong B. A. Tran, Jihye Roh, Connor W. Coley

  354. Categorical Flow Maps

    Daan Roos, Oscar Davis, Floor Eijkelboom, Michael M. Bronstein, Max Welling, İsmail İlkan Ceylan, Luca Ambrogioni, Jan-Willem van de Meent

  355. Lysine Targeting Group‐Transfer Chimeras for Proximity Induction

    Sameek Singh, Kien Tran, Endri Karaj, Basudeb Mondal, Wenzhi Tian, Surached Siriwongsup, Shaimaa H. Sindi, Uttam Dhawa, Kaushik Pal, Veronika M. Shoba, Sunny Shi, Anna Lian, Jody Mou, Myeonghoon Han, Prashant R. Singh, Nicholas F. Polizzi, Amit Choudhary

  356. Multichannel genomic recording of biological information with ENGRAM

    Jenny F. Nathans, Troy A. McDiarmid, Wei Chen, Jay Shendure

  357. Predictive Query Language: A Domain-Specific Language for Predictive Modeling on Relational Databases

    Vid Kocijan, Jinu Sunil, Jan Eric Lenssen, Viman Deb, Xinwei Xe, Federico Reyes Gomez, Matthias Fey, Jure Leskovec

  358. Trainable subnetworks reveal insights into structure knowledge organization in protein language models

    Ria Vinod, Ava P. Amini, Lorin A. Crawford, Kevin Yang

  359. Author Correction: Random access and semantic search in DNA data storage enabled by Cas9 and machine-guided design

    Carina E. Imburgia, Lee Organick, Karen Huilin Zhang, Nicolás Cardozo, Jeff McBride, Callista Bee, Delaney Wilde, Gwendolin Roote, Sofia Jorgensen, David Ward, Charlie Anderson, Karin Strauß, Luís Ceze, Jeff Nivala

  360. Generative structural elucidation from mass spectra as an iterative optimization problem

    Mrunali Manjrekar, Runzhong Wang, Samuel Goldman, Jenna C. Fromer, Connor W. Coley

  361. HumanLM: Simulating Users with State Alignment Beats Response Imitation

    Wu, Shirley, Evelyn Choi, Arpandeep Khatua, Zhanghan Wang, Joy He-Yueya, Tharindu Cyril Weerasooriya, Wei Wei, Diyi Yang, Jure Leskovec, James Zou

  362. Auxiliary datasets for Generative structural elucidation from mass spectra as a iterative optimization problem

    Mrunali Manjrekar, Runzhong Wang, Samuel Goldman, Jenna C. Fromer, Connor W. Coley

  363. Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model

    Jacqueline He, Jonathan Hayase, Wen-tau Yih, Sewoong Oh, Luke Zettlemoyer, Pang Wei Koh

  364. SaDiT: Efficient Protein Backbone Design via Latent Structural Tokenization and Diffusion Transformers

    Mo, Shentong, Lanqing Li

    Curated by Papers for Protein Design Using Deep Learning · 3. Function to Scaffold · 3.4 Diffusion-based

  365. Energy Landscape and Stability in Random Heteropolymers: Somewhere Between Protein Folding and Plastic Miscibility

    Tianyi Jin, Connor W. Coley, Alfredo Alexander‐Katz

  366. Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps

    Peter Holderrieth, Douglas Chen, Luca Eyring, Ishin Shah, Giri Anantharaman, Yutong He, Zeynep Akata, Tommi Jaakkola, Nicholas M. Boffi, Max Simchowitz

  367. Mesostructured Water Enhances Stability of ProteinMPNN-Designed Ubiquitin-Fold Proteins

    Lu-Yi Chen, Wei-Lin Lu, Tanvi Pathania, I-Hsuan Chu, Meng‐Ru Ho, Wei-Chen Chuang, Yuan‐Chao Lou, Ta I Hung, Yohei Miyanoiri, Chia‐en A. Chang, Kuen‐Phon Wu

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  368. Synthesizing scientific literature with retrieval-augmented language models

    Akari Asai, Jacqueline He, Rulin Shao, Weijia Shi, Amanpreet Singh, Joseph Chee Chang, Kyle Lo, Luca Soldaini, Sergey Feldman, Mike D’Arcy, David Wadden, Matt Latzke, Jenna Sparks, Jena D. Hwang, Varsha Kishore, Minyang Tian, Pan Ji, Shengyan Liu, Hao Tong, Bohao Wu, Yanyu Xiong, Luke Zettlemoyer, Graham Neubig, Daniel S. Weld, Doug Downey, Wen-tau Yih, Pang Wei Koh, Hannaneh Hajishirzi

  369. Turbulence teaches equivariance to neural networks

    Ryley McConkey, Julia Balla, Bailey, Jeremiah, Ali Backour, E. Hofgard, Tommi Jaakkola, Abigail S. Bodner, Tess Smidt

  370. Reliable and Responsible Foundation Models: A Comprehensive Survey

    Xinyu Yang, Junlin Han, Rishi Bommasani, Jinqi Luo, Wenjie Qu, Wangchunshu Zhou, Adel Bibi, Xiyao Wang, Jaehong Yoon, Elias Stengel-Eskin, Shengbang Tong, Lingfeng Shen, Rafael Rafailov, Runjia Li, Zhaoyang Wang, Yiyang Zhou, Chenhang Cui, Yu Wang, Wenhao Zheng, Huichi Zhou, Jindong Gu, Zhaorun Chen, Peng Xia, Tony Lee, Thomas P. Zollo, Vikash Sehwag, Jixuan Leng, Jiuhai Chen, Yuxin Wen, Huan Zhang, Zhun Deng, Linjun Zhang, Pavel Izmailov, Pang Wei Koh, Yulia Tsvetkov, Andrew Wilson, Jiaheng Zhang, James Zou, Cihang Xie, Hao Wang, Philip H.S. Torr, Julian McAuley, David Alvarez-Melis, Florian Tramèr, Kaidi Xu, Suman Jana, Chris Callison-Burch, René Vidal, Filippos Kokkinos, Mohit Bansal, Beidi Chen, Huaxiu Yao

  371. Protein Autoregressive Modeling via Multiscale Structure Generation

    Yanru Qu, Cheng-Yen Hsieh, Zaixiang Zheng, Ge Liu, Quanquan Gu

    Curated by Papers for Protein Design Using Deep Learning · 3. Function to Scaffold · 3.8 Autoregressive

  372. Computational design of blue melanin with peptide motif scaffolding

    Di Sheng Lee, Bomi Park, Sergio Salgado, James Dolgin, David Lee Kaplan

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  373. PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models

    Vignesh Kothapalli, Ranjan, Rishabh, Valter Hudovernik, Vijay Prakash Dwivedi, Johannes Hoffart, Carlos Guestrin, Jure Leskovec

  374. Privasis: Synthesizing the Largest "Public" Private Dataset from Scratch

    Hyunwoo Kim, Niloofar Mireshghallah, Michael Duan, Rui Xin, Shuyue Stella Li, Jaehun Jung, David Acuna, Qi Pang, Hanshen Xiao, G. Edward Suh, Sewoong Oh, Yulia Tsvetkov, Pang Wei Koh, Yejin Choi

  375. Generative AI for Enzyme Design and Biocatalysis

    Lasse Middendorf, Noelia Ferruz

    Curated by Papers for Protein Design Using Deep Learning · 1. Reviews · 1.5 Enzyme design

  376. FragmentFlow: Scalable Transition State Generation for Large Molecules

    Ron Shprints, Peter Holderrieth, Juno Nam, Rafael Gómez‐Bombarelli, Tommi Jaakkola

  377. Generative AI for drug design : methods and applications

    Kyle Swanson, Anshul Bharat Kundaje, James Zou, Jure Leskovec, Stanford University. School of Engineering., Stanford University. Computer Science Department.

  378. Publisher Correction: Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning

    Simon Hanassab, Joshua Southern, Ayomide Victoria Olabode, Ivan Laponogov, Michael M. Bronstein, Alexander N. Comninos, Thomas Heinis, Ali Abbara, Chioma N. Izzi‐Engbeaya, Kirill Veselkov, Waljit Singh Dhillo

  379. A unified pipeline for discovering previously unknown enzyme activities

    Ariane Mora, Julia C. Reisenbauer, Helen Schmid, Ikumi Miyazaki, Yueming Long, Jason Yang, Ryen L. O’Meara, Frances H. Arnold

  380. BPS2026 – AF2chi: Predicting protein side-chain rotamer distributions with AlphaFold2

    Matteo Cagiada, Emil Thomasen, Sergey Ovchinnikov, Charlotte Mary Deane, Kresten Lindorff‐Larsen

  381. Characterising nanobody developability to improve therapeutic design using the Therapeutic Nanobody Profiler

    Gemma L. Gordon, João Henrique Diniz Brandão Gervásio, Colby Souders, Charlotte Mary Deane

  382. Improving atlas-scale single-cell annotation models with hierarchical cross-entropy loss

    Sebastiano Cultrera di Montesano, Davide D’Ascenzo, Srivatsan Raghavan, Ava P. Amini, Peter S. Winter, Lorin A. Crawford

  383. Principles of in situ protein sequencing: expansion microscopy-adapted Edman degradation and amino acid recognition

    Camille Marie Mitchell, Sara Z. Tavana, Joanne Peng, Hao Wang, Jiuhan Shi, Chi Zhang, Lilia Evgeniou, Masy Domecillo, Shiwei Wang, Daniel Estandian, Alexi G. Choueiri, E. Wong, Sarah Dohadwala, Isken Kenzhebaev, Tay Won Shin, Nicholas F. Polizzi, Laura L. Kiessling, Edward S. Boyden

  384. Nab-paclitaxel fused with the de novo designed receptor binder exhibits enhanced tumor targeting and therapeutic efficacy

    Yuanying Qian, Weikang Yan, Fan Xu, Yali Liu, Fabao Chen, Yue Lu, Zihan Zhang, Ao Gu, Ruobing Yu, Zhen Fang, Yang Yu, Maolan Li, Longxing Cao, Yingbin Liu, Yongning He

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  385. Minimal-Action Discrete Schrödinger Bridge Matching for Peptide Sequence Design

    Shrey Goel, Pranam Chatterjee

  386. ProDCARL: Reinforcement Learning-Aligned Diffusion Models for De Novo Antimicrobial Peptide Design

    Fang Sheng, Mohammad Noaeen, Zahra Shakeri

    Curated by Papers for Protein Design Using Deep Learning · 4.12 RL-based · 4.Scaffold to Sequence

  387. Improving Diffusion Language Model Decoding through Joint Search in Generation Order and Token Space

    Yi Shen, Tianjian Feng, Jiaqi Han, Wen Wang, Tianlang Chen, Chunhua Shen, Jure Leskovec, Stefano Ermon

  388. ProChoreo: de novo Binder Design from Conformational Ensembles with Generative Deep Learning

    Saisai Ding, Yi Zhang

    Curated by Papers for Protein Design Using Deep Learning · 5.10 Autoregressive-models · 5.Function to Sequence

  389. TCRAD: An End-to-End Framework for Antigen-Targeted T Cell Receptor Design

    Chenao Li, Yaochi Guo, Xin Guan, Hui Chen, Yong Zhang, Pengyuan Yang, Jizhong Lou

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  390. De novo protein–ligand design including protein flexibility and conformational adaptation

    Jakob Agamia, Martin Zacharias

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  391. Predicting epistasis across proteins by structural logic

    Michelle Tang, Gareth A. Cromie, Anowarul Kabir, Martin S. Timour, Julee Ashmead, Russell S. Lo, Nathaniel Corley, Frank DiMaio, Hiroki Morizono, Ljubica Caldovic, Nicholas Ah Mew, Andrea Lynne Gropman, Amarda Shehu, Aimée M. Dudley

  392. AutoBinder Agent: An MCP-Based Agent for End-to-End Protein Binder Design

    Fukang Ge, Jiarui Zhu, Linjie Zhang, Haowen Xiao, Xiangcheng Bao, Fangnan Xie, Danyang Chen, Yanrui Lu, Yuting Wang, Ziqian Guan, Gu, Lin, Jinhao Bi, Yingying Zhu

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.4 CNN-based

  393. Origin-1: a generative AI platform for de novo antibody design against novel epitopes

    Simon Levine, Jonathan Edward King, Jacob Stern, David Grayson, Raymond Wang, Rui Yin, Umberto Lupo, Paulina Kulytė, Ryan M. Brand, Tristan Bertin, Robert Pfingsten, Jovan Cejovic, Chelsea Chung, Breanna K. Luton, Andrew Hagemann, Robel Gemechu Haile, Elliot Medina, Pankaj Panwar, Oleksii Dubrovskyi, Chase LaCombe, Zahra Anderson, Derrik Mildh, Scott Benjamin, Joe Kaiser, Joseph Ferron, Marta Sarrico, Alexandria C. Kershner, Apurva Mishra, Kai Ejan, Emily K Marsh, Paul Bringas, Phetsamay Vilaychack, Kyra Chapman, Jacob Ripley, Muttappa Gowda, Kathryn M Collins, Cailen M. McCloskey, Jeremiah S. Joseph, Rylee Ripley, Shaheed A. Abdulhaqq, David Spencer, Tiffany DeVine, Audree Feltner, Michael Guerin, Jeffrey Goby, Jesse Hendricks, Danielle Castillo, Sean McClain, Douglas Ganini, Derek Shpiel, James Mategko, Eder Cruz Garcia, Masoud Zabet‐Moghaddam, John M. Sutton, Zheyuan Guo, Sean M. West, Janani S. Iyer, Amir Shanehsazzadeh

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  394. Templating and confining calcium phosphate mineralization within designed protein assemblies

    Le Tracy Yu, Xinqi Li, Harley Pyles, Anjali P. Patni, Andrew J. Borst, Neville P. Bethel, Paul S. Kwon, Connor Weidle, Ryan D. Kibler, Kenneth D. Carr, Yulai Liu, Dongsheng Li, Stanislav Moroz, David A. Baker, Shuai Zhang, James J. DeYoreo, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  395. The ADAPT learning cancer treatment system: ARPA-H’s initiative to revolutionize cancer therapy

    Andrea H. Bild, Michelle Cook Sangar, Jasmine A. McQuerry, Trey E. Ideker, Scott Kopetz, Lisa Carey, Aritro Nath, Daniel Marcus, Allison Regier, Naim Rashid, Regina Barzilay, Eric P. Winer, Ravi Salgia, Jyoti Dhar Malhotra, Andrew J. Gentles, Kenneth H. Buetow, Faisal Mahmood, David W. Markman, James A. Eddy, Michelle Afkhami, Kristin D. Alfaro-Munoz, Salvador Alonso, Kate Alpert, Jesús Anampa, Vineet Bafna, Marija Balić, Justin Micah Balko, Regina Barzilay, Stephen Bates, Tanios Bekaii‐Saab, Vincent Bernard, Andrea H. Bild, Genevieve Marie Boland, Mitesh J. Borad, Kenneth H. Buetow, Loren Buhle, Robyn Burns, Timothy Lewis Cannon, Lisa Carey, Luca F. Castelnovo, Ruizhe Chen, Mary E. Cianfrocca, Amy Sanders Clark, Ryan Bruce Corcoran, Annie Delores, Nicholas S. Duesbery, Georges El Fakhri, Ahmed M. Elkhanany, Stephanie Forrest, Paul Henry Frankel, Ignacio Fuentes, Justin F. Gainor, Andrew J. Gentles, Manju George, Olivier Gevaert, Karthik Giridhar, Jason I. Griffiths, Benjamin Haibe‐Kains, Stanley R. Hamilton, Nicole Herrera, Susan Galloway Hilsenbeck, Frederick Howard, David S. Hsu, Stephanie Huang, Laura A. Huppert, Trey E. Ideker, Claudine J. Isaacs, Leila V. Kiefer, Erin Kelly, Edward J. Kim, Scott Kopetz, Nevan J. Krogan, Ian E. Krop, Manfred Laubichler, Long Le, Adrian V. Lee, Heewook Lee, Su‐In Lee, Xiaochen Li, Christopher Hanyoung Lieu, NU Lin, Faisal Mahmood, Anirban Maitra, Carlo C. Maley, Jyoti Dhar Malhotra, David Managadze, Daniel Marcus, Maurie Markman, Dipen Maheshbhai Maru, Jasmine McQuerry, David G. Menter, Wells A. Messersmith, Mark Messervy, Jonathan D. Mizrahi, Van Karlyle Morris, Remya Nair, Rita Nanda, Aritro Nath, Tia Newgen, Kimmie Ng

  396. Surface-based Molecular Design with Multi-modal Flow Matching

    Fang Wu, Zhengyuan Zhou, Shuting Jin, Xiangxiang Zeng, Jure Leskovec, Jinbo Xu

  397. AptaBLE: A Deep Learning Platform for Aptamer Generation and Analysis

    Sawan Patel, Keith Fraser, Dhanush Gandavadi, Abhisek Dwivedy, Xing Wang, Fred Zhangzhi Peng, Pranam Chatterjee, Sherwood Yao

  398. Deep learning guided design of protease substrates

    Carmen Martin-Alonso, Sarah Alamdari, Tahoura S. Samad, Kevin Yang, Sangeeta N. Bhatia, Ava Amini

  399. Learning Inter-Atomic Potentials without Explicit Equivariance

    Association for Artificial Intelligence 2026, Samuel M. Blau, Michael M. Bronstein, Ahmed A. Elhag, Alex Morehead, Garrett M. Morris, Arun Raja

  400. De novo design of metalloproteases for targeted amyloid-β cleavage

    Yannan Qu, Chentong Wang, Hongli Zhu, Yanjun Wang, Longxing Cao

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  401. Squidly: Enzyme Catalytic Residue Prediction Harnessing a Biology-Informed Contrastive Learning Framework

    William JF Rieger, Mikael Bodén A, Frances H. Arnold, Ariane Mora

  402. Pox-AbDab: the orthopoxvirus antibody database

    Henriette Capel, Eric Ji Da Wang, Ben H. Williams, Charlotte Mary Deane, Matthew I. J. Raybould

  403. Author response: Squidly: Enzyme Catalytic Residue Prediction Harnessing a Biology-Informed Contrastive Learning Framework

    William JF Rieger, Mikael Bodén A, Frances H. Arnold, Ariane Mora

  404. Reviewer #2 (Public review): Squidly: Enzyme Catalytic Residue Prediction Harnessing a Biology-Informed Contrastive Learning Framework

    William JF Rieger, Mikael Bodén A, Frances H. Arnold, Ariane Mora

  405. Reviewer #1 (Public review): Squidly: Enzyme Catalytic Residue Prediction Harnessing a Biology-Informed Contrastive Learning Framework

    William JF Rieger, Mikael Bodén A, Frances H. Arnold, Ariane Mora

  406. Mammalian genome writing: Unlocking new length scales for genome engineering

    Sudarshan Pinglay, John T. Atwater, Ran Brosh, Jay Shendure, Matthew T. Maurano, Jef D. Boeke

  407. Black-box data: a new paradigm for biomedicine in the AI era

    Luca Naef, Michael M. Bronstein

  408. An overview of reaction outcome prediction with physics-based and data-driven methods

    Joonyoung F. Joung, Nicholas Casetti, Priyanka Raghavan, Connor W. Coley

  409. Protein engineering: status report

    Hui‐wang Ai, Frances H. Arnold, Doug Barrick, Hagan Bayley, Alfie-Louise R. Brownless, Robert E. Campbell, Roger Castells‐Graells, Pimchai Chaiyen, Roberto A. Chica, Lauren O. Chisholm, Daniel Christ, Guo-Chao Chu, Michael Chungyoun, Vincent G. H. Eijsink, Michael B. Elowitz, Douglas J. Fansher, Rudi Fasan, Gabriel Foley, Michelle S. Frei, Andreas Gagsteiger, Ruiyan Gao, Elizabeth M. J. Gillam, Jeffrey J. Gray, Luis F. Guerra, Birte Höcker, Zhiyang Jin, Shina Caroline Lynn Kamerlin, Nobuyasu Koga, Roland E. Kontermann, Brian A. Kuhlman, O Langlois, Dongyang Li, Roberto Dias Lins, Chang C. Liu, Lu Liu, Diego López Barreiro, Peilong Lu, Sohum Mehta, Ikumi Miyazaki, Manuel M. Müller, Serge Muyldermans, Naoto Nemoto, Jeff Nivala, James K. Nuñez, Izaiah J. Ornelas, Joelle N. Pelletier, Andreas Plückthun, Owen T. Porth, Kridsadakorn Prakinee, Yujia Qing, Julia C. Reisenbauer, Sandy Schmidt, Oliver Seifert, Mikhail G. Shapiro, Rachna Sharma, Nilmani Singh, Benjamin J. Smith, Jamie B. Spangler, Rie Koga, Takuya Terai, Henrik Terholsen, Mattias Tolhurst, Katherine W. Tripp, Vanessa Tanja Trossmann, Chandra L. Tucker, Onur Turak, James A. Van Deventer, Anikó Várnai, Isabelle F. T. Viana, Amy M. Weeks, Karl Dane Wittrup, K. C. Wong, Chenggang Xi, Todd O. Yeates, Andy Hsien-Wei Yeh, Jin Zhang, Tiger Zhang, Huimin Zhao, Fang Zheng, Jingyi Zhu, Sacha Zinn

  410. Quantifying the failure modes of current one-step retrosynthesis models

    Suong B. A. Tran, Jihye Roh, Connor W. Coley

  411. On the Impact of Sample Size in Reconstructing Noisy Graph Signals: A Theoretical Characterisation

    Baskaran Sripathmanathan, Xiaowen Dong, Michael M. Bronstein

  412. Ten-Year Latent Trajectories of Global Function and Their Associations with Social Relationship Factors: A Traumatic Brain Injury Model Systems Study

    Evan D. Feigel, Michael J. Mastrodicasa, Charlotte B. Luster, Brenna Finegan, David Guo, Chris W. Lewis, Maëlle-Marie Troadec, Ben Olsen, Ignacio Fuentes, Joseph T. Giacino, Regina Barzilay, Daniel Hamed Daneshvar

  413. A machine learning-based workflow for transaminase selection

    Alexander J. Rago, Priyanka Raghavan, Lisandra Santiago-Capeles, Ruijie Zhang, Y Wang, Connor W. Coley

  414. AI & the Discovery of Molecules through Autonomous Laboratories

    Connor W. Coley

  415. Data - Generating Hybrid Proteins with the MSA-Transformer

    Sanjana Tule, Samuel J. Davis, Ivan Koludarov, Ariane Mora, Mikael Bodén A

  416. Distinct Five-Year Latent Class Trajectories and Predictors of Functional Cognitive Independence Following Traumatic Brain Injury: A TBI Model Systems Study

    Evan D. Feigel, Charlotte B. Luster, Chris W. Lewis, Brenna Finegan, Michael J. Mastrodicasa, Maëlle-Marie Troadec, David Guo, Ben Olsen, Ignacio Fuentes, Joseph T. Giacino, Regina Barzilay, Daniel Hamed Daneshvar

  417. Syndirella: synthesis-directed fragment elaboration enables extensive and cost-effective binding site exploration beyond catalog compounds

    Kate K. Fieseler, Max Winokan, Joseph Morrone, Charlotte Mary Deane, Frank von Delft, Warren Athol Thompson

  418. De novo design of small molecule–regulated protein oligomers

    Qihan Jin, Yukai Wang, Dachuan Chen, Jinyang Liao, Zhanyuan Cui, Yuxuan Fan, An‐Ping Zeng, Mingqi Xie, Longxing Cao

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  419. Computational design of class II MHC binding peptide with sequence-based evolution information

    Ying Cao, Yuqing Li, Weitong Ren, Wenfei Li, Ming Yang

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  420. CyclicMPNN: stable cyclic peptide sequence generation

    Andrew C. Powers, Yanapat Janthana, Parisa Hosseinzadeh

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  421. De Novo Design of Protein Nanopores: From Minimal Peptides to AI-Driven Design

    Anastassia A. Vorobieva, Rituparna Samanta, Toon Van Thillo

    Curated by Papers for Protein Design Using Deep Learning · 1. Reviews · 1.1 De novo protein design

  422. Discriminator-Guided Inverse Folding for Multi-Property Protein Design

    Yuchuan Zheng, Chuyi Liu, Zhaoming Liu, Mao Su, Chenyu Tang, Xiang Zheng, Hao Zhang, Jingyuan Li

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  423. HFGuidedDesign: de novo design of cyclic peptide binders via structure-guided discrete diffusion

    Haomeng Hu, Renjie Zhu, Ning Zhu, Chengyun Zhang, Tianfeng Shang, Chongyang Li, Jingjing Guo, Xudong Wang, Hongliang Duan

    Curated by Papers for Protein Design Using Deep Learning · 5.12 Diffusion-based · 5.Function to Sequence

  424. Curiosity-driven de novo protein design via polymerized amyloids

    Tianyi Jin, Jacob Sass, Wenhao Gao, Connor W. Coley, Alfredo Alexander‐Katz

  425. SeedProteo: Accurate De Novo All-Atom Design of Protein Binders

    Qu Wei, Ma Yiming, Ye Fei, Lu Chan, Zhou Yi, Zhang Kexin, Wang Lan, Gui Minrui, Gu Quanquan

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  426. End-to-End Test-Time Training for Long Context

    Arnuv Tandon, Karan Dalal, Xinhao Li, Daniel Koceja, Marcel Rød, Sam Buchanan, Xiaolong Wang, Jure Leskovec, Sanmi Koyejo, Tatsunori Hashimoto, Carlos Guestrin, Jed McCaleb, Yejin Choi, Yu Sun

  427. Chemprop v2: An Efficient, Modular Machine Learning Package for Chemical Property Prediction

    David Graff, Nathan K. Morgan, Jackson W. Burns, Anna C. Doner, Brian Li, Shih‐Cheng Li, Joel Manu, Angiras Menon, Hao‐Wei Pang, Haoyang Wu, Akshat Shirish Zalte, Jonathan Wenyuan Zheng, Connor W. Coley, William H. Green, Kevin P. Greenman

  428. Stable de novo protein design via joint conformational landscape and sequence optimization

    Yehlin Cho, Justas Dauparas, Kotaro Tsuboyama, Gabriel J. Rocklin, Sergey Ovchinnikov

  429. Structure–activity relationships can be directly extracted from high-throughput crystallographic evaluation of fragment elaborations in crude reaction mixtures

    Harold Grosjean, Kate K. Fieseler, Ruben Sanchez-Garcia, Warren Athol Thompson, Charlotte Mary Deane, F. von Delft, Philip Charles Biggin

  430. Experimental analyses of RFdiffusion designed Miniproteins for binding to SARS-CoV-2 nucleocapsid protein

    Zeenat Khakerwala, Ashwani Kumar, Sujay S. Gaikwad, T. Barik, Shweta Singh, Gagan D. Gupta, Ravindra D. Makde

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  431. De novo design of protein binders that target DELE1 to inhibit the mitochondrial stress response

    Rui Yang, Kaiyuan Zheng, McGuire Metts, Yiluo Wang, Danyan YIN, Kevin P. Li, Agnieszka A. Prazmowska, David F. Kashatus, Brian A. Kuhlman, Jie Yang

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  432. Protein design and RNA design: Perspectives

    Xi Chen, Xu Dai, Peilong Lu

    Curated by Papers for Protein Design Using Deep Learning · 1. Reviews · 1.1 De novo protein design

  433. De novo design of protein nanoparticles with integrated functional motifs

    Cyrus M. Haas, Sanela Rankovic, Hanul K Lewis, Kenneth D. Carr, Connor Weidle, Sophie S Gerdes, L. Nuss, Felicitas Ruiz, Syed Moiz, Maggie Fiorelli, Emily Grey, Jackson McGowan, N. Pavan Kumar, Adrian Creanga, Alex Kang, Hannah Nguyen, Yanqing Wang, Banumathi Sankaran, Annie M. Dosey, Rashmi Ravichandran, Asim K. Bera, Elizabeth M. Leaf, Cole A. DeForest, Masaru Kanekiyo, Andrew J. Borst, Neil P. King

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  434. LASErMPNN Training Dataset (REDUCE Protonated PDB) [PDB File Format], Chunk 1/2

    Benjamin N. Fry, Nicholas F. Polizzi

  435. LASErMPNN Training Dataset (REDUCE Protonated PDB) [PDB File Format], Chunk 2/2

    Benjamin N. Fry, Nicholas F. Polizzi

  436. Structure-Aware Antibody Design with Affinity-Optimized Inverse Folding

    Xinyan Zhao, Tang Yi-Ching, Rivaaj Monsia, Victor Cantu, Ashwin Kumar Ramesh, Xiaozhong Liu, Zhiqiang An, Xiaoqian Jiang, Yejin Kim

    Curated by Papers for Protein Design Using Deep Learning · 4.7 Transformer-based · 4.Scaffold to Sequence

  437. Assessing the Chemical Intelligence of Large Language Models

    Nicholas T. Runcie, Charlotte Mary Deane, Fergus Imrie

  438. FragmentScope - exploring the fragment space with learned surface representations

    Evgenia Elizarova, Irina Morozova, Ilia Igashov, Olivia Gampp, David Robin Pavel Iosub, Arne Schneuing, Kelvin Lau, Davide M. Ferraris, Florence Pojer, Roland Riek, Michael M. Bronstein, Bruno E. Correia

  439. Author response for "Structure-Activity-Relationships can be directly extracted from high-throughput crystallographic evaluation of fragment elaborations in crude reaction mixtures"

    Harold Grosjean, Kate K. Fieseler, Ruben Sanchez-Garcia, Warren Athol Thompson, Charlotte Mary Deane, F. von Delft, Philip Charles Biggin

  440. Structural basis for synergistic antibody protection against the essential malaria invasion complex protein RIPR

    Barnabas G. Williams, Jordan R. Barrett, Josefin Bartholdson Scott, Cassandra A. Rigby, Matteo Cagiada, Doris Quinkert, Kirsty McHugh, Anna Gesine Huhn, Sean Anthony Burnap, Camille Gourjault, Francesca Byrne, Sai Sundar Rajan Raghavan, Ana Rodrigues, Laura Bergamaschi, Beatrice Balzarotti, Simon J. Watson, Noah Miller, Lloyd D. W. King, Francesca R. Donnellan, Camilla A. Gladstone, Jemima Paterson, Stefania Scalabrino, Sarah E. Silk, Jo Salkeld, Angela M. Minassian, Katherine Skinner, Weston B. Struwe, Charlotte Mary Deane, Stephen T. Reece, Andrew B. Ward, Simon J. Draper

  441. De novo design of protein competitors for small molecule immunosensing

    Yosta de Stigter, Tallie Godschalk, Maarten Merkx

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  442. Olmo 3

    Team OLMo, Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, Graham, David, David Heineman, Dirk Groeneveld, Faeze Brahman, Finbarr Timbers, Hamish Ivison, Jacob Morrison, Jake Poznanski, Kyle Lo, Luca Soldaini, Matt Jordan, Mayee F. Chen, Michael Noukhovitch, Nathan Lambert, Walsh, Pete, Pradeep Dasigi, Robert Berry, Saumya Malik, Saurabh Anil Shah, Scott Geng, Shane Arora, Shashank Rai Gupta, Anderson, Taira, Xiao, Teng, Murray, Tyler, Tyler Romero, Victoria Graf, Akari Asai, Akshita Bhagia, Alexander Wettig, Alisa Liu, Aman Rangapur, Chloe Anastasiades, Huang, Costa, Dustin Schwenk, Harsh K. Trivedi, Ian Magnusson, Jaron Lochner, Jiacheng Liu, Lester James V. Miranda, Maarten Sap, Morgan, Malia, Schmitz, Michael, Michal Guerquin, Wilson, Michael, Regan Huff, Ronan Joseph Le Bras, Rui Xin, Rulin Shao, Sam Skjonsberg, Shannon Zejiang Shen, Shuyue Stella Li, Tucker Wilde, Valentina Pyatkin, Merrill, Will, Chang, Yapei, Yuling Gu, Zhiyuan Zeng, Ashish Sabharwal, Luke Zettlemoyer, Pang Wei Koh, Farhadi, Ali, Noah A. Smith, Hannaneh Hajishirzi

  443. On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals

    Baskaran Sripathmanathan, Xiaowen Dong, Michael M. Bronstein

  444. Sequence and structural determinants of efficacious de novo chimeric antigen receptors

    Arthur Chow, Hoyin Chu, Ruofan Li, Benan N. Nalbant, Abdul Vehab Dozic, Laura C. Kida, Caleb A. Lareau

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  445. Self Distillation Fine-Tuning of Protein Language Models Improves Versatility in Protein Design

    Amin Tavakoli, Murugan, Raswanth, Ozan Gökdemir, Arvind Ramanathan, Frances H. Arnold, Animashree Anandkumar

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  446. Protein Diffusion Models as Statistical Potentials

    James P. Roney, Chenxi Ou, Sergey Ovchinnikov

  447. Author response: Evidence of social learning across symbolic cultural barriers in sperm whales

    Antonio Leitao, Maxime Lucas, Simone Poetto, Taylor A. Hersh, Shane Gero, David F. Gruber, Michael M. Bronstein, Giovanni Petri

  448. Enzyme Engineering Database (EnzEngDB): a platform for sharing and interpreting sequence–function relationships across protein engineering campaigns

    Yueming Long, Fatemeh Abbasinejad, Francesca-Zhoufan Li, Pierre Reinprecht, Bruce J. Wittmann, Jennifer L. Kennemur, Hayden M. Carder, Jason Yang, Théophile Lambert, Ryen L. O’Meara, Lukas Radtke, Ziyang Qin, Sabine Brinkmann‐Chen, Frances H. Arnold, Ariane Mora

  449. Learning constrained static equilibrium for thrust network inverse form-finding via physics-informed geometric deep learning on CW complexes

    Kam-Ming Mark Tam, Nathan Collin Brown, Michael M. Bronstein, Tom Van Mele, Philippe Block

  450. Mechanistic Interpretability of Antibody Language Models Using SAEs

    Haque, Rebonto, Oliver M. Turnbull, Anisha Parsan, Nithin Parsan, John Jeongseok Yang, Anna L. Beukenhorst, Charlotte Mary Deane

  451. Designing novel solenoid proteins with in silico evolution

    Daniella Pretorius, Georgi I. Nikov, Kono Washio, Steve-William Florent, Henry N. Taunt, Sergey Ovchinnikov, James W. Murray

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  452. Atom-level enzyme active site scaffolding using RFdiffusion2

    Woody Ahern, Jason Yim, Doug Tischer, Saman Salike, Seth M. Woodbury, Donghyo Kim, Indrek Kalvet, Yakov Kipnis, Brian Coventry, Han Altae-Tran, Magnus S. Bauer, Regina Barzilay, Tommi Jaakkola, Rohith Krishna, David A. Baker

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.3 RoseTTAFold-based

  453. A foundation model for microbial growth dynamics

    Zachary A. Holmes, Irida Shyti, Alexandra L. Hoffman, Katherine E. Duncker, Helena R. Ma, Zhengqing Zhou, Dongheon Lee, Rohan Maddamsetti, Kyeri Kim, Emrah Şimşek, Grayson S. Hamrick, Hye-In Son, César A. Villalobos, Jia Grace Lu, Yuanchi Ha, Ashwini R. Shende, Zhixiang Yao, Sizhe Liu, Daniel Mark Shapiro, Kseniia Kholina, Harris M. Davis, Yasa Baig, Feilun Wu, Shangying Wang, Xiran Wang, Pranam Chatterjee, Michael Lynch, Allison J. Lopatkin, Lawrence A. David, Emma J. Chory, Lingchong You

  454. Development of Plasma Protein Classification Models for Alzheimer’s Disease Using Multiple Machine Learning Approaches

    Amy Tsurumi, Catherine M. Cahill, Andy Liu, Pranam Chatterjee, Sudeshna Das, Ami Kobayashi

  455. From algorithms to systems: integrating computation into drug discovery

    Anthony R Bradley, Adrian Rossall, Garry Pairaudeau, Charlotte Mary Deane

  456. The unique architecture of umbrella toxins permits a two-tiered molecular bet-hedging strategy for interbacterial antagonism

    Qinqin Zhao, J. Vlach, Young‐Jun Park, Yongjun Tan, Savannah K Bertolli, Pooja Srinivas, Pinyu Liao, Connor R. Fitzpatrick, Jeffery L. Dangl, Parastoo Azadi, Frank DiMaio, Snow Brook Peterson, Dapeng Zhang, David Veesler, Joseph D. Mougous

  457. Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training

    Felix Teufel, Aaron W. Kollasch, Yining Huang, Ole Winther, Kevin Yang, Pascal Notin, Debora S. Marks

  458. Self-Improving VLM Judges Without Human Annotations

    Inna Wanyin Lin, Yushi Hu, Shuyue Stella Li, Scott Geng, Pang Wei Koh, Luke Zettlemoyer, Tim Althoff, Marjan Ghazvininejad

  459. Modeling the structure-conditioned sequence landscape for large-scale protein design with TriFlow

    Harish Srinivasan, Rongqing Yuan, Jing Zhang, Qian Cong, Jian Zhou

    Curated by Papers for Protein Design Using Deep Learning · 5.7 Flow-based · 5.Function to Sequence

  460. Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning

    Simon Hanassab, Joshua Southern, Ayomide Victoria Olabode, Ivan Laponogov, Michael M. Bronstein, Alexander N. Comninos, Thomas Heinis, Ali Abbara, Chioma N. Izzi‐Engbeaya, Kirill Veselkov, Waljit Singh Dhillo

  461. Lymphodepleting chemotherapy potentiates neoantigen-directed T cell therapy by enhancing antigen presentation

    Shira Sagie, Tomer Babu, Chen Weller, Claire Tabachnik, Ido Livneh, Nele P. Quast, Nofar Gumpert, Alina S. Shomuradova, Matthew I. J. Raybould, Ronen Levy, Dmitry B. Malko, Adi Alfia, Talal Ben Lulu, Michal Alon, Franco Herrera, Mikhail A. Kutuzov, Mirie Zerbib, Polina Greenberg, Talya Wasserman-Bartov, Gil Benedek, Yishai Levin, Chani Stossel, Iris Kamer, Talia Golan, Roni Oren, Merav D. Shmueli, Osnat Bartok, Jair Bar, Jonathan E. Cohen, Omer Dushek, Charlotte Mary Deane, Yardena Samuels

  462. Efficient and Programmable Exploration of Synthesizable Chemical Space

    Shitong Luo, Connor W. Coley

  463. Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation

    Fiona Y. Wang, Di Sheng Lee, David Lee Kaplan, Markus J. Buehler

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  464. PULSAR: a Foundation Model for Multi-scale and Multicellular Biology

    Kuan Pang, Yanay Rosen, Kasia Kedzierska, Ziyuan He, Abhejit Rajagopal, Claire E. Gustafson, Grace H. Huynh, Jure Leskovec

  465. BoltzGen: Toward Universal Binder Design

    Hannes Stark, Felix Faltings, MinGyu Choi, Yuxin Xie, Eunsu Hur, Timothy J. O’Donnell, Anton Bushuiev, Talip Uçar, Saro Passaro, Weian Mao, Mateo Reveiz, Roman Bushuiev, Tally Portnoi, Tomáš Pluskal, Josef Šivic, Karsten Kreis, Arash Vahdat, Shamayeeta Ray, Jonathan T. Goldstein, Andrew Savinov, Jacob A. Hambalek, Anshika Gupta, Diego A. Taquiri-Diaz, Yaotian Zhang, Samuel J. Snyder, A. Katherine Hatstat, Angelika Arada, Nam Hyeong Kim, Haoyu Fan, Ethel Tackie‐Yarboi, Gene‐Wei Li, Lee Schnaider, Chang C. Liu, Gene‐Wei Li, Denes Hnisz, David M. Sabatini, William F. DeGrado, Jeremy Wohlwend, Gabriele Corso, Regina Barzilay, Tommi Jaakkola

  466. Flow Map Distillation Without Data

    Tong, Shangyuan, Ma, Nanye, Xie, Saining, Tommi Jaakkola

  467. DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research

    Shao, Rulin, Asai, Akari, Shen, Shannon Zejiang, Ivison, Hamish, Kishore, Varsha, Zhuo, Jingming, Xinran Zhao, Park, Molly, Samuel G. Finlayson, David Alexander Sontag, Murray, Tyler, Min, Sewon, Dasigi, Pradeep, Soldaini, Luca, Brahman, Faeze, Yih, Wen-tau, Tongshuang Wu, Luke Zettlemoyer, Kim, Yoon, Hajishirzi, Hannaneh, Pang Wei Koh

  468. Beyond Protein Language Models: An Agentic LLM Framework for Mechanistic Enzyme Design

    Jacob, Bruno, Agarwal, Khushbu, Baer, Marcel, Rice, Peter, Raugei, Simone

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  469. General-Purpose Large Language Models, such as DeepSeek V3.2, Have Evolved Protein Design Capabilities

    Jiawei Li, Xinxiu Dong

    Curated by Papers for Protein Design Using Deep Learning · 5.4 Transformer-based · 5.Function to Sequence

  470. Torsion-Space Diffusion for Protein Backbone Generation with Geometric Refinement

    Singh, Lakshaditya, Shelke, Adwait, Agrawal, Divyansh

    Curated by Papers for Protein Design Using Deep Learning · 3. Function to Scaffold · 3.4 Diffusion-based

  471. Structure-informed models for ionic current prediction in nanopore sequencing of expanded dna alphabets

    Ashley P. Stephenson, Jayson Sumabat, Hinako Kawabe, Sidharth Lakshmanan, Hyo Joong Kim, Yubing Li, Jorge A. Marchand, Jeff Nivala

  472. Generating Hybrid Proteins with the MSA-Transformer

    Sanjana Tule, Samuel Davis, Ivan Koludarov, Ariane Mora, Mikael Bodén A

  473. Targeting peptide–MHC complexes with designed T cell receptors and antibodies

    Amir Motmaen, Kevin M. Jude, Nan Wang, Anastasia A. Minervina, David Feldman, Mauriz A. Lichtenstein, Abishai Ebenezer, Colin Correnti, Paul Glyndwr Thomas, K. Christopher García, David A. Baker, Philip H Bradley

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence

  474. CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design

    Zijun Gao, Mutian He, Sun, Shijia, Hanqun Cao, Jingjie Zhang, Zihao Luo, Xiaorui Wang, Yao, Xiaojun, Chang‐Yu Hsieh, Chunbin Gu, Pheng‐Ann Heng

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.9 AlphaFold-based

  475. Semantic design of functional de novo genes from a genomic language model

    Aditi T. Merchant, Samuel H. King, Eric Nguyen, Brian Hie

  476. sokrypton/af2bind: final version for publication

    Artem G, Sergey Ovchinnikov

  477. CIRPIN: Learning Circular Permutation-Invariant Representations to Uncover Putative Protein Homologs

    Aiden R. Kolodziej, S. Mazdak Abulnaga, Sergey Ovchinnikov

  478. Hit or Miss: Understanding Emergence and Absence of Homo-oligomeric Contacts in Protein Language Models

    Zhidian Zhang, Yo Akiyama, Yehlin Cho, Samarth Jajoo, Sergey Ovchinnikov

  479. Editing stem cell genomes at scale to measure variant effects in diverse cell and genetic contexts

    Shawn Fayer, Riddhiman K. Garge, Melissa Hopkins, Clayton E. Friedman, Abby V. McGee, Joshua Rico, Rachel L. Powell, Evan McDermot, Nahum T. Smith, Sriram Pendyala, Marcy E. Richardson, Erica D. Smith, B. Monica Bowen, Rebecca Resnick, Pankhuri Gupta, Andrew B. Stergachis, Casey A. Gifford, Sudarshan Pinglay, Kai‐Chun Yang, Douglas M. Fowler, Lea M. Starita

  480. Transferable Transcriptional Topic Modeling Traces Medulloblastoma Subtypes to Distinct Cerebellar Developmental States

    Ashmitha Rajendran, Parthiv Haldipur, Sonali Arora, Kaustubh Grama, Saanvi S. Subramanian, Leyre Merino Galan, David Johnson, Kimberly A. Aldinger, Jay Shendure, Kathleen J. Millen, John H. Gennari, Siobhan S. Pattwell

  481. GDH in complex with compound G2, processed with cryoPARES

    Rubén Sánchez-García, Berndt A, Apelbaum A, Reeks J, Williams PA, Carl R. Poelking, Charlotte Mary Deane, Michael Saur

  482. Harnessing protein-folding algorithms to drug intrinsically disordered epitopes

    Jakub Lála, Stefano Angioletti‐Uberti

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.4 LLM-based

  483. Uncovering the flexibility of CDR loops in antibodies and TCRs through large-scale molecular dynamics

    Matteo Cagiada, Fabian C. Spoendlin, King Ifashe, Charlotte Mary Deane

  484. Controllable protein design with particle-based Feynman-Kac steering

    Erik Hartman, Jonas Wallin, Johan A. Malmström, Jimmy Olsson

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.5 Sampling-algorithms · 3. Function to Scaffold · 3.4 Diffusion-based

  485. A parts list of promoters and gRNA scaffolds for mammalian genome engineering and molecular recording

    Troy A. McDiarmid, Megan L. Taylor, Wei Chen, Florence M. Chardon, Junhong Choi, Hanna Liao, Xiaoyi Li, Haedong Kim, Jean‐Benoît Lalanne, Tony Yi‐Wei Li, Jenny F. Nathans, Beth Martin, Jordan Knuth, Alessandro Luis Venega Coradini, Jesse M. Gray, Sudarshan Pinglay, Jay Shendure

  486. Random Heteropolymers Enable Nonspecific Protein Binding and Loop-Mediated Stabilization

    Tianyi Jin, Akorfa Dagadu, Connor W. Coley, Alfredo Alexander‐Katz

  487. Inferring residue level hydrogen deuterium exchange with ReX

    Oliver M. Crook, Nathan Gittens, Chun‐wa Chung, Charlotte Mary Deane

  488. RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments

    Zhiyuan Zeng, Hamish Ivison, Wang, Yiping, Lifan Yuan, Shuyue Stella Li, Zhuorui Ye, Siting Li, Jacqueline He, Runlong Zhou, Tong Chen, Chenyang Zhao, Yulia Tsvetkov, Simon S. Du, Natasha Jaques, Hao Peng, Pang Wei Koh, Hannaneh Hajishirzi

  489. Automating discovery with AI

    Kexin Huang, Jure Leskovec, Emmanuel J. Candès, Anshul Bharat Kundaje, Stanford University. School of Engineering., Stanford University. Computer Science Department.

  490. Entangled Schrödinger Bridge Matching

    Tang, Sophia, Zhang, Yinuo, Pranam Chatterjee

  491. HalluDesign: Protein Optimization and de novo Design via Iterative Structure Hallucination and Sequence Design

    Minchao Fang, Chentong Wang, Jungang Shi, Fengbai Lian, Qihan Jin, Zhe Wang, Yanzhe Zhang, Peipei Chen, Zhanyuan Cui, Yanjun Wang, Ze Zhang, Yitao Ke, Qingzheng Han, Longxing Cao

    Curated by Papers for Protein Design Using Deep Learning · 2. Model-based design · 2.1.1 trRosetta-based

  492. Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model

    Li, Guanlue, Xufeng Zhao, Fang Wu, Laue, Sören

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  493. Peptide2Mol: A Diffusion Model for Generating Small Molecules as Peptide Mimics for Targeted Protein Binding

    He, Xinheng, Zhang, Yijia, Lin, Haowei, Peng, Xingang, Kong, Xiangzhe, Li, Mingyu, Ma, Jianzhu

    Curated by Papers for Protein Design Using Deep Learning · 6. Function to Structure · 6.2 Diffusion-based

  494. Large-scale discovery of neural enhancers for cis-regulation therapies

    Troy A. McDiarmid, Nicholas F. Page, Florence M. Chardon, Riza M. Daza, George T Chen, Michael Kosicki, Lucas M. James, Hannah C. Nourie, Dianne Laboy Cintrón, Arthur S. Lee, Paula A Vij, Diego Calderon, Jean‐Benoît Lalanne, Beth Martin, Kyle D. Fink, Michael E. Talkowski, Alysson Renato Muotri, Benjamin D. Philpot, Len A. Pennacchio, Daniel H. Geschwind, Stephan Sanders, Nadav Ahituv, Jay Shendure

  495. Adaptive resampling for improved machine learning in imbalanced single-cell datasets

    Zeinab Navidi, Akshaya Thoutam, Madeline Hughes, Srivatsan Raghavan, Peter S. Winter, Lorin A. Crawford, Ava P. Amini

  496. GDH in complex with ADP, processed with cryoPARES

    Rubén Sánchez-García, Berndt A, Apelbaum A, Reeks J, Williams PA, Carl R. Poelking, Charlotte Mary Deane, Michael Saur

  497. GDH in complex with compound G1, processed with cryoPARES

    Rubén Sánchez-García, Berndt A, Apelbaum A, Reeks J, Williams PA, Carl R. Poelking, Charlotte Mary Deane, Michael Saur

  498. Modeling protein–small molecule conformational ensembles with PLACER

    Ivan V. Anishchenko, Yakov Kipnis, Indrek Kalvet, Guangfeng Zhou, Rohith Krishna, Samuel J. Pellock, Anna Lauko, Gyu Rie Lee, Linna An, Justas Dauparas, Frank DiMaio, David A. Baker

  499. A Roadmap for Predictive Human Immunology

    Khan, Aly A., Perera, Jason, James Zou, Löıc A. Royer, Alan R. Lowe, Carr, Ambrose, Karaletsos, Theofanis, Brennan, Patricia, Parsa, Roham, Clark, Marcus R., Joseph L. DeRisi, Jay Shendure, Schmid, Sandra L., Fraser, Scott E., Califano, Andrea, Kelley, Shana O.

  500. Conformational regulation of two essential activators of bacterial cell elongation

    Morgan S. A. Gilman, Irina Shlosman, Daniel D. Samé Guerra, Masy Domecillo, Elayne M. Fivenson, Claire Bourett, Thomas G. Bernhardt, Nicholas F. Polizzi, Joseph J. Loparo, Andrew C. Kruse

  501. Identification of Capture Phases in Nanopore Protein Sequencing Data Using a Deep Learning Model

    Martin, Annabelle, Daphne Kontogiorgos-Heintz, Jeff Nivala

  502. Interim Results of the CMML scrna-seq atlas: GMP skewing is independently associated with overall survival in CMML.

    Amy F. McLemore, Surendra Neupane, Zena Komrokji, Maria E. Balasis, Traci L. Kruer, Umesh Padia, Sean Murphy, Aline Renneville, Nathalie M Droin, Dorothée Selimoglu‐Buet, Miguel Ángel González Martínez, Chaomei H. Zhang, Tania Mesa, Sean Yoder, David Andrew Sallman, Andrew Tucker Kuykendall, Christopher B. Ryder, Rami S. Komrokji, Mrinal M. Patnaik, Meghan C. Ferrall‐Fairbanks, Regina Barzilay, Éric Solary, Eric Padron

  503. DL4Proteins Jupyter Notebooks Teach how to use Artificial Intelligence for Biomolecular Structure Prediction and Design

    Chungyoun, Michael, Au, Gabe, Carpentier, Britnie, Puvada, Sreevarsha, Thomas, Courtney, Gray, Jeffrey J.

    Curated by Papers for Protein Design Using Deep Learning · 1. Reviews · 1.1 De novo protein design

  504. Orthogonal IMiD-Degron Pairs Induce Selective Protein Degradation in Cells

    Patrick J. Brennan, Rebecca E. Saunders, Mary Spanou, Sarah E. Singleton, M. Teresa Serafini, Liang Sun, Guillaume Heger, Agnieszka Konopacka, Ryan D. D. Beveridge, C. Cameron Taylor, Peter DePaola, Laurie Gordon, Shenaz B. Bunally, Aurore Saudemont, Andrew B. Benowitz, Carlos Martinez-Fleites, Danielle L. Schmitt, Robert D. Damoiseaux, Markus A. Queisser, Heeseon An, Charlotte Mary Deane, Michael M. Hann, Lewis L. Brayshaw, Stuart J. Conway

  505. HarnessLLM: Automatic Testing Harness Generation via Reinforcement Learning

    Yujian Liu, Jiabao Ji, Yang Zhang, Wenbo Guo, Tommi Jaakkola, Shiyu Chang

  506. Bridging Graph Neural Networks and Large Language Models: A Survey and Unified Perspective

    Álvaro Arroyo, Federico Barbero, Hugh Blayney, Michael M. Bronstein, Xiaowen Dong, Pietro Liò, Razvan Pascanu, Pierre Vandergheynst

  507. Single-cell, multi-region profiling of the macaque brain across the lifespan

    Wei Yang, Kirsty Watkins, Alex R. DeCasien, Mary B. O’Neill, Martin O. Bohlen, Diana R. O’Day, Madeleine Duran, Chengxiang Qiu, A. N. Meleshko, Anh Vo, Melia Menke, Diego Calderon, Jérôme Sallet, James P. Higham, Melween I. Martínez, Cole Trapnell, Lea M. Starita, Michael J. Montague, Michael L. Platt, Kenneth L. Chiou, Jay Shendure, Noah Snyder‐Mackler

  508. LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature

    Magdalena Lederbauer, Siddharth Betala, Valerie Gentzke, Anamaria Leonescu, Amine Sehaba, Faris Flaifil, Ayush Jain, Alfonso Amayuelas, Nikhil Yelamarthy, Li, Xiyao, Germain, Grégoire, Stefano Ribes, Stefan P. Schmid, Alexandre Nozadze, Anna Kelmanson, Sudheesh Kumar Ethirajan, Mohd Zaki, Elton Pan, Georgia Channing, Connor W. Coley, Philippe Schwaller, Rocío Mercado, Alexandre Duval, Mathilde L. D. Franckel, S. Gleason

  509. Democratizing protein language model training, sharing and collaboration

    Jin Su, Zhikai Li, Tianli Tao, Chenchen Han, Yan He, Fengyuan Dai, Qingyan Yuan, Yuan Gao, Tong Si, Xuting Zhang, Yuyang Zhou, Junjie Shan, Xibin Zhou, Xing Chang, Shiyu Jiang, Dacheng Ma, The OPMC, Anthony Gitter, Milot Mirdita, Kevin Yang, Pascal Notin, Debora S. Marks, Pranam Chatterjee, Rohit Singh, Philip A. Romero, Michael Heinzinger, Jianming Liu, Jia Zheng, Stan Ziqing Li, An‐Ping Zeng, Huaizong Shen, Jijie Chai, Feng Ju, Noelia Ferruz, Anum Azam Glasgow, Philip M. Kim, Christopher Davis Snow, Vasilis Ntranos, Jianyi Yang, Liang Hong, Caixia Gao, Tong Si, Michael M. Bronstein, Xing Chang, Fajie Yuan, Jin Su, Zhikai Li, Tianli Tao, Chenchen Han, Yan He, Fengyuan Dai, Xuting Zhang, Yuyang Zhou, Junjie Shan, Xibin Zhou, Shiyu Jiang, Yuan Gao, Jiawei Zhang, Yuliang Fan, Yuyang Tao, Linqi Cheng, Xinzhe Zheng, Lei Chen, Rui Long, Lingjie Kong, Zhongji Pu, Jiaming Guan, Tianyuan Zhang, Cheng Li, Martin Steinegger, Sergey Ovchinnikov, Fajie Yuan

    Curated by Papers for Protein Design Using Deep Learning · 4.7 Transformer-based · 4.Scaffold to Sequence

  510. HeavyBuilder: Analysis of High-Throughput of Antibody Heavy Chain Repertoires in the Structural Space

    João Henrique Diniz Brandão Gervásio, Alexander Greenshields‐Watson, Nele P. Quast, Brennan Abanades, Liza Figueiredo Felicori, Charlotte Mary Deane

  511. Author Correction: Discovery and artificial intelligence-guided mechanistic elucidation of a narrow-spectrum antibiotic

    Denise B. Catacutan, Vian Tran, Autumn Arnold, Jeremie Alexander, Gabriele Corso, Yeganeh Yousefi, Megan M. Tu, Stewart McLellan, Dominique Tertigas, Kimberly Corneil, Jakob Magolan, Michael Gordon Surette, Eric D. Brown, Brian K. Coombes, Regina Barzilay, Jonathan M. Stokes

  512. Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds

    Oscar Davis, Michael S. Albergo, Nicholas M. Boffi, Michael M. Bronstein, Avishek Joey Bose

  513. AI-Guided Hydrophobic Core Design of Robust Six-Helix Bundle Proteins

    Yinying Meng, Guojin Tang, Ruishi Wang, Bin Zheng, Yuanhao Liu, Hantian Zhang, Peng Zheng

    Curated by Papers for Protein Design Using Deep Learning · 4.5 GNN-based · 4.Scaffold to Sequence