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Found 6 papers, 4 papers with code
Enhancing Ligand Pose Sampling for Molecular Docking
To train scoring functions-and to perform molecular docking-one must generate a set of candidate ligand binding poses.
FlexVDW: A machine learning approach to account for protein flexibility in ligand docking
Here we present a deep learning model trained to take receptor flexibility into account implicitly when predicting van der Waals energy.
ATOM3D: Tasks On Molecules in Three Dimensions
We implement several classes of three-dimensional molecular learning methods for each of these tasks and show that they consistently improve performance relative to methods based on one- and two-dimensional representations.
Protein model quality assessment using rotation-equivariant, hierarchical neural networks
Proteins are miniature machines whose function depends on their three-dimensional (3D) structure.
Learning from Protein Structure with Geometric Vector Perceptrons
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 leverages the graph-structured and geometric aspects of the problem domain.
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines.
