203 citations · 415 across the 11 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…
Geometric Latent Diffusion Models for 3D Molecule Generation
Minkai Xu, Alexander Powers, Ron Dror +2
Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries and advancing foundational science problems such as mol…
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…
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction
Christopher Fifty, Joseph M. Paggi, Ehsan Amid +2
Few-shot learning is a promising approach to molecular property prediction as supervised data is often very limited. However, many important molecular properties depend on complex…