Michael Chungyoun

ProtAff predicts protein-protein binding affinity using sequence data, outperforming existing methods on an EGFR benchmark but underperforming structural methods in cross-target discrimination.

This paper introduces GermRL, a reinforcement learning framework that reduces germline bias in antibody language models, improving mutation diversity while maintaining structural plausibility.

This paper benchmarks AI models on predicting antibody developability properties using FLAb2, finding they cannot consistently predict most properties and performance depends on data and architecture.

This study shows that anti-citrullinated protein antibodies (ACPAs) in RA arise from germline antibodies targeting carbamylated proteins through affinity maturation and somatic hypermutations.

FLAb benchmarks deep learning models for predicting antibody fitness properties across six metrics, finding no model consistently correlates with all properties.