AlphaFold and the Transformation of Structural Biology
AlphaFold and the Transformation of Structural Biology: Evolution, Applications, Limitations, and Future Directions
We have a new publication out that discusses the history of AlphaFold and future directions in the field. You can see the abstract below and click here to read the preprint.
Abstract
The protein folding problem is the challenge of predicting a protein’s three-dimensional structure from its amino acid sequence. This problem has been a central challenge in molecular biology for over fifty years. The advent of AlphaFold, a deep learning system developed by DeepMind, represented a paradigm shift in structural biology by demonstrating near-experimental accuracy in protein structure prediction. This review traces the evolution of the AlphaFold family of models, from its breakthrough performance in CASP14 through the expanded capabilities of AlphaFold 3 and the recent emergence of the proprietary Isomorphic Labs Drug Design Engine (IsoDDE). We examine the architectural innovations that underpin AlphaFold’s success, its broad applications in drug discovery, virology, and protein engineering, and its well-documented limitations in modeling intrinsically disordered regions, conformational ensembles, and allosteric mechanisms. We also discuss the growing tension between open science and commercial interests in AI-driven structural biology. The review draws primarily on peer-reviewed literature and curated expert sources to provide an accessible yet rigorous overview of the current state and future trajectory of AI-based protein structure prediction.
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