Predicting Protein-Ligand Docking Structure with Graph Neural Network.

ABSTRACT: Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both low accuracy and high latency. A few recent machine learning-based approaches have been proposed for virtual screening by improving the ability to evaluate protein-ligand binding affinity, but such methods rely heavily on conventional docking software to sample docking poses, which results in excessive execution latencies. Here, we propose and evaluate a novel graph neural network (GNN)-based framework, MedusaGraph, which includes both pose-prediction (sampling) and pose-selection (scoring) models. Unlike the previous machine learning-centric

SUBMITTER: Jiang H 

PROVIDER: S-EPMC10279412 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature