Prediction of Molecular Conformation Using Deep Generative Neural Networks

Xu, Congsheng1,2,3; Lu, Yi2,3; Deng, Xiaomei2,3; Yu, Peiyuan2,3

2023-10-01

10.1002/cjoc.202300269

CHINESE JOURNAL OF CHEMISTRY   Impact Factor & Quartile

1001-604X

1614-7065

["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 molecule. Traditional 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 accuracy. Recently, deep learning-based generative models have emerged as a promising solution to this problem. These 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 software. This 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.","Molecular conformations play a crucial role in fields such as materials science and drug design. Traditional methods like molecular dynamics and Monte Carlo simulations are limited in speed and accuracy. Deep learning models offer a promising solution by rapidly generating accurate molecular conformations. This Emerging Topic highlights recent progresses in using deep learning for predicting molecular conformations and explores the potential and challenges of this emerging field.image"]

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

SCI

English

Corresponding

We are grateful for the financial support from Guangdong Basic and Applied Basic Research Foundation (2021A1515010387), Guangdong Provincial Key Laboratory of Catalysis (2020B121201002), Shenzhen Higher Education Institution Stable Support Plan (2020092515[2021A1515010387] ; Guangdong Basic and Applied Basic Research Foundation[2020B121201002] ; Guangdong Provincial Key Laboratory of Catalysis[20200925152921001] ; Shenzhen Higher Education Institution Stable Support Plan[KQTD20210811090112004]

Chemistry

Chemistry, Multidisciplinary

WOS:001073274600001

WILEY-V C H VERLAG GMBH

CHEMISTRY

Web of Science

1.Harbin Inst Technol, Sch Chem & Chem Engn, Harbin 150001, Heilongjiang, Peoples R China
2.Southern Univ Sci & Technol, Dept Chem, Guangdong Prov Key Lab Catalysis, Shenzhen 518055, Guangdong, Peoples R China
3.Southern Univ Sci & Technol, Shenzhen Grubbs Inst, Guangdong Prov Key Lab Catalysis, Shenzhen 518055, Guangdong, Peoples R China

Xu, Congsheng,Lu, Yi,Deng, Xiaomei,et al. Prediction of Molecular Conformation Using Deep Generative Neural Networks[J]. CHINESE JOURNAL OF CHEMISTRY,2023.

Xu, Congsheng,Lu, Yi,Deng, Xiaomei,&Yu, Peiyuan.(2023).Prediction of Molecular Conformation Using Deep Generative Neural Networks.CHINESE JOURNAL OF CHEMISTRY.

Xu, Congsheng,et al."Prediction of Molecular Conformation Using Deep Generative Neural Networks".CHINESE JOURNAL OF CHEMISTRY (2023).