Ian R. Humphreys

The paper predicts 18,316 human protein-protein interactions with 90% precision using enhanced coevolutionary analysis and a new deep learning model.

AtomWorks, a modular atom-level data framework, trains structure prediction and design models; the resulting RF3 improves chirality handling and performs between AF3 and Boltz.

This study designs novel serine hydrolases with high catalytic efficiency using computational methods, achieving structures close to design models and distinct from natural enzymes.

This study uses deep learning to predict protein-protein interactions in bacterial pathogens, identifying new complexes involving essential genes and virulence factors, with half of tested predictions validated.

This paper introduces RFAA and RFdiffusionAA, models that can predict and design complex biomolecular assemblies including proteins, nucleic acids, small molecules, and covalent modifications.