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Found 4 papers, 4 papers with code
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery.
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Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
Molecular chirality, a form of stereochemistry most often describing relative spatial arrangements of bonded neighbors around tetrahedral carbon centers, influences the set of 3D conformers accessible to the molecule without changing its 2D graph connectivity.
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery.
Message Passing Networks for Molecules with Tetrahedral Chirality
Molecules with identical graph connectivity can exhibit different physical and biological properties if they exhibit stereochemistry-a spatial structural characteristic.
