Machine Learning in Structural Biology

Structural biology is the study of biological function with the awareness that molecules exist in four dimensions. AlphaFold2 demonstrated deep learning’s capability to solve one low-hanging problem in this field: predicting a single protein structure from its sequence, trained from the ~180,000 structures standardized and collected in the Protein Data Bank. However, there remain many harder unsolved challenges that need progress if we wish to understand and design functional biomolecules. There is a strong need to gather deep learning experts and biologists together to address these challenges. We have assembled and confirmed a set of diverse speakers who are world leaders in current challenges, including how to incorporate large-scale stability datasets, dynamics, ligand binding, into the fold of modern deep learning for structural biology. One of the biggest bottlenecks for all of these problems is the data available for training and how to create clear and stringent tests to evaluate progress. Our workshop will highlight two new benchmarks in a special track of our call for papers, and create a platform for open-sourced models, in collaboration with HuggingFace. We anticipate this workshop to be of great interest to many NeurIPS attendees, and to create lasting impact in establishing benchmarks and accessible modelling resources for deep learning and structural biology communities.