Artificial Intelligence in Chemical Engineering: Protein Design from First Principles to Structural Prediction

Abstract

Machine learning and artificial intelligence are improving the speed and accuracy of every step during the protein design process. Early computational strategies relied on physics-based modeling and energy functions to identify amino acid sequences and desired folds. Recent advances in deep-learning structure prediction, diffusion-based backbone generation, and graph-based sequence design now allow researchers to explore the protein sequence and structural space more efficiently. These developments allow proteins to be used as fundamental systems whose components can be engineered with high precision. Computational predictions still struggle to properly account for conformational dynamics, catalytic environments, external interactions, and the broader chemical diversity present in natural enzymes. This review covers the progression from physics-based methods to deep learning, generative methods, and includes current strategies for evaluating stability and function in silico and experimentally.