Nathan C. Frey

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

This paper introduces EVFI and DeepEVFI, machine learning methods that infer variant fitness from directed evolution data, identifying tighter binders missed by conventional approaches.

The paper compresses protein sequence and structure representations, showing that compressed embeddings retain structural accuracy and enable downstream applications.

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.

PLAID generates controllable all-atom protein structures from sequences using a diffusion model trained on sequence data, enabling functional control through Gene Ontology and organism keywords.

This paper trains a protein language model in 24 GPU hours, achieving performance comparable to a much larger model trained for 15,000x more GPU hours.

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.