LOGS 第2022/08/12期 || 麻省理工学院Hannes Stärk: EquiBind-用于药物结合的图几何深度学习

图学习研讨会（LOGS）公众号会不定期地举行图学习相关的研讨会，邀请相关领域的专家，一线科研人员和顶会论文作者进行分享，希望能够给大家提供一个相互交流，研讨，和学习的平台。这一期我们邀请到了麻省理工学院博士研究生Hannes Stärk，他将为我们带来一期用于药物结合的图几何深度学习精彩报告。

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报告嘉宾：Hannes Stärk @ MIT

报告题目

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

报告摘要

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.

报告人简介

Hi! Hannes Stärk is a PhD student at MIT with an M.Sc. Informatics from TU Munich. He works on graph/geometric machine learning and self-supervised learning, often with applications to proteins and smaller molecules. At MIT CSAIL, he is advised by Prof. Tommi Jaakkola and Prof. Regina Barzilay.

参考文献

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

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