203 citations · 415 across the 11 of their papers we have counts for
12 papers
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…
Systematic Analysis of Biomolecular Conformational Ensembles with PENSA
Martin Vögele, Neil J. Thomson, Sang T. Truong +3
Atomic-level simulations are widely used to study biomolecules and their dynamics. A common goal in such studies is to compare simulations of a molecular system under several condi…
Equivariant Graph Neural Networks for 3D Macromolecular Structure
Bowen Jing, Stephan Eismann, Pratham N. Soni +1
Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning. Here, we extend recent work on geometric vector perceptron…
ATOM3D: Tasks On Molecules in Three Dimensions
Raphael J. L. Townshend, Martin Vögele, Patricia Suriana +10
Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry. In particular, deep neural net…
Protein model quality assessment using rotation-equivariant, hierarchical neural networks
Stephan Eismann, Patricia Suriana, Bowen Jing +2
Proteins are miniature machines whose function depends on their three-dimensional (3D) structure. Determining this structure computationally remains an unsolved grand challenge. A…
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…
Geometric Prediction: Moving Beyond Scalars
Raphael J. L. Townshend, Brent Townshend, Stephan Eismann +1
Many quantities we are interested in predicting are geometric tensors; we refer to this class of problems as geometric prediction. Attempts to perform geometric prediction in real-…
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Stephan Eismann, Raphael J. L. Townshend, Nathaniel Thomas +3
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure pred…
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…