Euclidean Deep Learning Models for 3D Structures and Interactions of Molecules
Speaker: Octavian Ganea, MIT
Location: 60 Fifth Avenue 150
Date: March 21, 2022, 2 p.m.
Host: Rajesh Ranganath
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. However, existing computer models are insufficient, being very time-consuming and having difficulties exploring the vast space of molecular complex candidates. 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, my recent DL architectures for direct shot prediction of the molecular complex, and GeoMol, a model for 3D molecular flexibility. I will argue that the governing laws of geometry, physics, or chemistry that naturally constrain these 3D structures should be incorporated in DL solutions in a mathematically meaningful way. This will be exemplified by leveraging key modeling concepts such as SE(3)-equivariant graph matching networks, optimal transport for binding pocket prediction, and torsion angle neural networks. My approaches reduce the inference runtimes of open-source and commercial software by factors of tens or hundreds, while being competitive or better in terms of quality. Finally, I will highlight a number of exciting on-going and future efforts in the space of artificial intelligence for structural biology and chemistry.
Octavian Ganea is a postdoctoral researcher at CSAIL-MIT working with Tommi Jaakkola and Regina Barzilay on deep learning solutions for drug discovery and structural biology using geometric and physical inductive biases. He is part of and contributes to the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, the Abdul Latif Jameel Clinic for Machine Learning in Health, the DARPA Accelerated Molecular Discovery program, and the ELLIS society. Octavian received his PhD from ETH Zurich under the supervision of Thomas Hofmann working on non-Euclidean representation learning for graphs, hierarchical data, and natural language processing. His published research includes a spotlight at ICLR 2022, spotlights at NeurIPS 2021 and 2018, and oral talks at ICML 2018 and 2019.
In-person attendance only available to those with active NYU ID cards.