EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
Speaker
Time
Venue
Online
Meeting Info
zoom Meeting:
Meeting ID: 826 7530 1781
Password: 123456
Tencent Meeting:
https://meeting.tencent.com/dm/Hl6hU41JSVWL
Conference ID: 231 427 410
Password: 539132
Live Stream URL
https://meeting.tencent.com/l/2RRFESX6JefM
Abstract
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 heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps. We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand's bound pose and orientation. EquiBind achieves significant speed-ups and better quality compared to traditional and recent baselines. Further, we show extra improvements when coupling it with existing fine-tuning techniques at the cost of increased running time. Finally, we propose a novel and fast fine-tuning model that adjusts torsion angles of a ligand's rotatable bonds based on closed-form global minima of the von Mises angular distance to a given input atomic point cloud, avoiding previous expensive differential evolution strategies for energy minimization.
Bio
Hannes Stark is a PhD student at MIT in EECS supervised by Prof. Tommi Jaakkola and Prof. Regina Barzilay. He works on geometric deep learning fundamentals and applications to molecules.
