203 citations · 255 across the 7 of their papers we have counts for
4 papers · 1 filter
Enhancing Ligand Pose Sampling for Molecular Docking
Patricia Suriana, Ron O. Dror
Deep learning promises to dramatically improve scoring functions for molecular docking, leading to substantial advances in binding pose prediction and virtual screening. To train s…
FlexVDW: A machine learning approach to account for protein flexibility in ligand docking
Patricia Suriana, Joseph M. Paggi, Ron O. Dror
Most widely used ligand docking methods assume a rigid protein structure. This leads to problems when the structure of the target protein deforms upon ligand binding. In particular…
Learning from Protein Structure with Geometric Vector Perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana +2
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously…
End-to-End Learning on 3D Protein Structure for Interface Prediction
Raphael J. L. Townshend, Rishi Bedi, Patricia A. Suriana +1
Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited. Whet…