Andrew Leaver‐Fay

DyAb predicts antibody properties and designs new sequences with high binding affinity using a low-data approach.

This paper designs orthogonal TCR β interfaces to prevent mispairing in dual-TCR therapeutics, achieving high pairing fidelity and demonstrating enhanced potency in targeting cancer antigens.

This paper introduces a machine learning framework for therapeutic antibody design that integrates computational methods with experimental validation, resulting in significantly improved binding affinity for multiple targets.

EquiFold predicts all-atom protein structures using an SE(3)-equivariant coarse-grained geometric representation without sequence alignments or language model embeddings, matching AlphaFold accuracy orders of magnitude faster in preliminary tests.

The paper presents a framework for ensuring reproducibility in bio-macromolecular modeling through automated benchmarks and a test server, demonstrating its utility for Rosetta and other tools.