Beyond AlphaFold: The Next Wave of ML Innovations in Protein Modelling

William McCorkindale

Abstract:

In the wake of the landmark achievements of AlphaFold, the field of protein modelling is experiencing a proliferation of novel Machine Learning (ML) techniques. This talk offers a comprehensive review of these emerging methods, from the use of protein language models for structural modelling (ESMFold) and function annotation (ProteInfer), to the application of diffusion models for protein design (RFDiffusion) and protein-ligand docking (DiffDock). I will also discuss anticipated developments on both the near-term and distant horizons of this field, with examples of how these ML approaches are being applied at CHARM Therapeutics to accelerate drug discovery.

Speaker bio:

William is an interdisciplinary scientist working on the application of machine learning & cheminformatics for drug discovery in the Modelling & Informatics team at CHARM. Funded by the Gates Scholarship, William completed his PhD under Dr Alpha Lee at the University of Cambridge, researching the application of deep learning for modelling chemical reactions, bioactivity, and structure-based virtual screening. William previously worked on extending the capabilities of AlphaFold as a Research Scientist intern at Google DeepMind.