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Found 10 papers, 5 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.
Systematic Analysis of Biomolecular Conformational Ensembles with PENSA
PENSA implements various methods to systematically compare the distributions of these features across ensembles to find the significant differences between them and identify regions of interest.
Equivariant Graph Neural Networks for 3D Macromolecular Structure
Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning.
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.
Geometric Prediction: Moving Beyond Scalars
This novel and data-efficient ability to predict real-world geometric tensors opens the door to addressing many problems through the lens of geometric prediction, in areas such as 3D vision, robotics, and molecular and structural biology.
Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Predicting the structure of multi-protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery.
Transferrable End-to-End Learning for Protein Interface Prediction
While there has been an explosion in the number of experimentally determined, atomically detailed structures of proteins, how to represent these structures in a machine learning context remains an open research question.
End-to-End Learning on 3D Protein Structure for Interface Prediction
Despite an explosion in the number of experimentally determined, atomically detailed structures of biomolecules, many critical tasks in structural biology remain data-limited.
Open-Ended Question Answering
Protein Interface Prediction
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