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The emergence of machine learning force fields in drug design

文献类型：期刊论文

作者Chen, Mingan1,2,3; Jiang, Xinyu1,4; Zhang, Lehan1,4; Chen, Xiaoxu1,4,5; Wen, Yiming1,4,5; Gu, Zhiyong1,4,5; Li, Xutong1,4,6; Zheng, Mingyue1,4,5,6
刊名MEDICINAL RESEARCH REVIEWS
出版日期2024-01-03
页码36
ISSN号0198-6325
关键词computational chemistry drug design force field machine learning molecular modeling
DOI10.1002/med.22008
通讯作者Li, Xutong(lixutong@simm.ac.cn) ; Zheng, Mingyue(myzheng@simm.ac.cn)
英文摘要In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.
WOS关键词MOLECULAR-DYNAMICS SIMULATIONS ; PROTEIN-LIGAND BINDING ; CHEMICAL UNIVERSE ; CHEMISTRY ; DATABASE ; ACCURATE ; PARAMETERS ; INHIBITORS ; PREDICTION ; MECHANICS
资助项目National Key Research and Development Program of China (2022YFC3400504 to Mingyue Zheng)[2022YFC3400504] ; National Key Research and Development Program of China[T2225002] ; National Key Research and Development Program of China[82273855] ; National Key Research and Development Program of China[82204278] ; National Key Research and Development Program of China[LG202102-01-02] ; National Key Research and Development Program of China[E2G805H] ; National Natural Science Foundation of China ; Shanghai Municipal Science and Technology Major Project
WOS研究方向Pharmacology & Pharmacy
语种英语
出版者WILEY
WOS记录号WOS:001135827900001
源URL[http://119.78.100.183/handle/2S10ELR8/308502]  
专题中国科学院上海药物研究所
通讯作者Li, Xutong; Zheng, Mingyue
作者单位1.Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai, Peoples R China
2.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai, Peoples R China
3.Lingang Lab, Shanghai, Peoples R China
4.Univ Chinese Acad Sci, Sch Pharm, Beijing, Peoples R China
5.UCAS, Hangzhou Inst Adv Study, Sch Pharmaceut Sci & Technol, Hangzhou, Peoples R China
6.Chinese Acad Sci, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai Inst Mat Med, Shanghai 201203, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Mingan,Jiang, Xinyu,Zhang, Lehan,et al. The emergence of machine learning force fields in drug design[J]. MEDICINAL RESEARCH REVIEWS,2024:36.
APA Chen, Mingan.,Jiang, Xinyu.,Zhang, Lehan.,Chen, Xiaoxu.,Wen, Yiming.,...&Zheng, Mingyue.(2024).The emergence of machine learning force fields in drug design.MEDICINAL RESEARCH REVIEWS,36.
MLA Chen, Mingan,et al."The emergence of machine learning force fields in drug design".MEDICINAL RESEARCH REVIEWS (2024):36.

入库方式： OAI收割

来源：上海药物研究所

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