Euclidean Deep Learning Models for 3D Structures & Interactions of Molecules - Octavian-Eugen Ganea

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Abstract: Understanding the 3D structures and interactions of proteins and drug-like molecules is a key part of therapeutics discovery. A core problem is molecular docking, i.e., determining how two molecules attach and create a molecular complex. Having access to very fast accurate computational docking tools would enable applications such as virtual screening of cancer protein inhibitors, de novo drug design, or rapid in silico drug side-effect prediction. In this talk, I will show that geometry and deep learning (DL) can significantly reduce this enormous search space inherent in docking and molecular conformation prediction. I will present EquiDock and EquiBind, our recent DL architectures for direct shot prediction of the molecular complex, and GeoMol, a model for 3D molecular flexibility. 

Speaker: Octavian-Eugen Ganea - https://twitter.com/octavianEganea

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