Garegin A. Papoian

This paper introduces DODock and DOScore, hybrid ML/physics frameworks for docking and scoring that improve accuracy while generalizing better to novel molecules, showing significant improvements in hit rates for virtual screening.

This study identifies EGFR mutations conferring resistance to BLU-945 using deep mutational scanning and shows that combination treatment may suppress resistance mechanisms.

The paper introduces SDDF, a platform using volunteer computing and active learning to efficiently generate molecular conformation datasets for drug discovery.

The paper investigates the role of Ena/VASP proteins in axon growth by analyzing filopodial morphology and actin organization in Drosophila neurons.

The paper combines deep learning and mechanistic modeling to predict protein conformational ensembles and compares them to experimental MD structures, showing correlations and highlighting areas for improvement in folding and dynamics prediction.