Sarah A. Robinson

ConformAb designs antibody sequences that maintain CDR canonical conformations, improving binding affinity without target-specific data.

ANARCII is a language model that numbers antigen receptor sequences without alignment, achieving high accuracy and efficiency for diverse sequences.

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.

Ab-Ligity identifies sequence-dissimilar antibodies that bind to the same epitope by analyzing antibody-antigen interfaces, outperforming existing tools in precision and speed.