计算机科学
虚拟筛选
对接(动物)
药物发现
机器学习
人工智能
人工神经网络
加速
蛋白质-配体对接
软件
图形
数据挖掘
理论计算机科学
生物信息学
并行计算
医学
护理部
生物
程序设计语言
作者
Huaipan Jiang,Jian Wang,Weilin Cong,Yihe Huang,Morteza Ramezani,Anup Sarma,Nikolay V. Dokholyan,Mehrdad Mahdavi,Mahmut Kandemir
标识
DOI:10.1021/acs.jcim.2c00127
摘要
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 studies, MedusaGraph generates the docking poses directly and achieves from 10 to 100 times speedup compared to state-of-the-art approaches, while having a slightly better docking accuracy.
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