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Lagnajit Pattanaik

3 accepted papers

2022

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

ICML 2022spotlight

Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. Existing methods are computationally expensive as they rely on…

2022

Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations

ICLR 2022poster

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. Chirality can strongly alter (bio)chemi…

2021

GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles

NeurIPS 2021spotlight

Prediction of a molecule’s 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements (e.g., torsion angles), separate optimization…