Protein-Ligand Binding Predictions with SO(3)-Equivariant Neural Networks

April 02, 2021
Events, ACS2021

At the American Chemical Society Spring 2021 Virtual Meeting & Expo, several Atomwise members and partners were selected to present their research and work. Learn what our Atoms have been working on below and visit Atomwise at ACS Spring 2021 Virtual Meeting & Expo for other presentation sessions.


Brandon Anderson, PhDBrandon Anderson, PhD 
Atomwise

Atomwise Co-Authors: Stefan Schroedl, Izhar Wallach

Title: Protein-Ligand Binding Predictions with SO(3)-Equivariant Neural Networks

Division: COMP

 

Abstract

Virtual high-throughput screening of protein-ligand binding affinity is a valuable tool in computational drug design. Recently, structure-based machine-learning techniques have emerged as a powerful strategy for accurately predicting binding affinity. Simultaneously, SO(3)-equivariant neural networks can accurately predict physics-based properties of small molecules. The introduction of translation-and rotation-equivariant features allows the network to learn and exploit local geometry with fewer parameters than a full network. Here, we present recent advancements using an SO(3)-equivariant neural network to learn structure-based binding affinity predictions. Our network generates rotationally-equivariant features for both the ligand and receptors, based upon Tensor Field Networks with an equivariant attention mechanism. The ligand and receptor features are then combined in a single rotationally-equivariant interaction network. We apply our network to a variety of metrics against open-source screening benchmarks, and compare our performance with other strategies such as graph-based and voxel-based neural networks.

 


Slide Presentation

ACS Spring_Atomwise_Brandon Anderson

 


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