Artificial Intelligence and Bioinformatics for Predicting Protein Structure, Function, and Molecular Interactions

Abstract

Proteins serve as the fundamental execution machinery of biological systems, and elucidating their three-dimensional architectures, functional roles, and molecular interactions is essential for understanding biochemical pathways, disease mechanisms, and rational biopharmaceutical engineering. The exponential accumulation of genomic sequencing data has vastly outpaced the throughput of empirical structural biology techniques such as X-ray crystallography, NMR spectroscopy, and cryo-EM, creating a critical gap that artificial intelligence (AI) and bioinformatics have increasingly filled. This review provides a critical evaluation of modern AI methods in structural bioinformatics, synthesizing the theoretical evolution from classical homology modeling and ab initio approaches to deep learning paradigms, including Convolutional Neural Networks, Graph Neural Networks, and Transformer-based architectures. We examine AlphaFold2's Evoformer module, single-sequence protein language models such as ESMFold and ESM-3, and next-generation multimodal all-atom systems including AlphaFold 3, RoseTTAFold All-Atom, Boltz-1, Chai-1, and Protenix, which extend prediction to heterogeneous biomolecular complexes. We further assess high-throughput functional annotation tools, including DeepGOPlus, DeepFRI, DeepGO-SE, CLEAN, and FAPM, alongside generative protein design strategies leveraging continuous latent spaces and self-consistency metrics. AI-driven molecular docking approaches, including DiffDock, DiffDock-Glide, and SurfDock, are evaluated for their capacity to model protein-ligand interactions with improved physical plausibility. Despite these advances, significant challenges persist, including dynamic conformational heterogeneity, intrinsically disordered proteins, non-globular systems, and the absence of structured water networks and cofactors in static models. We conclude by discussing multi-axis verification standards, experimental integration, and open infrastructure ecosystems that support transparency and reproducibility in computational structural biology.