Nikhil Branson

AntiDIF improves antibody inverse folding by increasing sequence diversity while maintaining high sequence recovery rates and structural accuracy.

The paper evaluates deep learning models for drug response prediction, finds transcriptomics data drives performance, and introduces new models that learn chemical features.

This study benchmarks zero-shot single-cell foundation model embeddings for perturbation effect prediction and finds they do not consistently improve over baseline models under distribution shift.