Prediction of Molecular Conformation Using Deep Generative Neural Networks

作     者：Congsheng Xu Yi Lu Xiaomei Deng Peiyuan Yu Congsheng Xu;Yi Lu;Xiaomei Deng;Peiyuan Yu

作者机构：School of Chemistry and Chemical EngineeringHarbin Institute of TechnologyHarbinHeilongjiang150001 China Department of Chemistry and Shenzhen Grubbs InstituteGuangdong Provincial Key Laboratory of CatalysisSouthern University of Science and TechnologyShenzhenGuangdong518055 China 

出 版 物：《Chinese Journal of Chemistry》 (中国化学（英文版）)

年 卷 期：2023年第41卷第24期

页      面：3684-3688页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081704[工学-应用化学] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0817[工学-化学工程与技术] 070303[理学-有机化学] 0835[工学-软件工程] 0703[理学-化学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the financial support from Guangdong Basic and Applied Basic Research Foundation(2021A1515010387) Guangdong ProvincialKeyLaboratory ofCatalysis(2020B121201002) Shenzhen Higher Education Institution Stable Support Plan(20200925152921001) Shenzhen Science and Technology Program(KQTD20210811090112004) 

主　　题：Conformation generation Machine learning Deep Generative Neural Networks Drug design Reaction mechanisms 

摘      要：The accurate prediction of molecular conformations with high efficiency is crucial in various fields such as materials science,computational chemistry and computer-aided drug design,as the three-dimensional structures accessible at a given condition usually determine the specific physical,chemical,and biological properties of a *** approaches for the conformational sampling of molecules such as molecular dynamics simulations and Markov chain Monte Carlo methods either require an exponentially increasing amount of time as the degree of freedom of the molecule increases or suffer from systematic errors that fail to obtain important conformations,thus presenting significant challenges for conformation sampling with both high efficiency and high ***,deep learning-based generative models have emerged as a promising solution to this *** models can generate a large number of molecular conformations in a short time,and their accuracy is comparable and,in some cases,even better than that of current popular open-source and commercial *** Emerging Topic introduces the recent progresses of using deep learning for predicting molecular conformations and briefly discusses the potential and challenges of this emerging field.