AI-Driven Protein Structure Prediction: A Review of Deep Learning Methods for Protein Folding and Drug Design

Abstract

Predicting a protein's three-dimensional structure from its amino acid sequence, long regarded as one of the central unsolved problems in computational biology, has been transformed over the past several years by deep learning systems capable of achieving accuracy approaching that of experimental structure determination for many protein families. This paper reviews the methods underlying this transformation, tracing the field's progression from early template-based and co-evolution-based approaches through end-to-end deep learning architectures that jointly reason over sequence, evolutionary, and geometric information. We describe the architectural innovations that enabled this progress, including attention-based processing of evolutionary sequence alignments and geometry-aware structure modules, and examine extensions to protein complex prediction, protein language models, and generative de novo protein design. We review applications in drug discovery, protein function annotation, protein engineering, disease mechanism elucidation, and vaccine and antibody design, summarize the benchmarks used to evaluate structure prediction methods, and present a case study illustrating how a predicted structure is used within a drug discovery pipeline. We discuss remaining challenges, including prediction accuracy for intrinsically disordered regions, multi-state and dynamic conformational prediction, and experimental validation bottlenecks, and outline future directions connecting structure prediction with generative protein design and integrative structural biology.