深度生成模型在新药设计中的应用进展。

IF 3.9 2区 化学 Q2 CHEMISTRY, APPLIED Molecular Diversity Pub Date : 2024-08-04 DOI:10.1007/s11030-024-10942-5
Yingxu Liu, Chengcheng Xu, Xinyi Yang, Yanmin Zhang, Yadong Chen, Haichun Liu
{"title":"深度生成模型在新药设计中的应用进展。","authors":"Yingxu Liu,&nbsp;Chengcheng Xu,&nbsp;Xinyi Yang,&nbsp;Yanmin Zhang,&nbsp;Yadong Chen,&nbsp;Haichun Liu","doi":"10.1007/s11030-024-10942-5","DOIUrl":null,"url":null,"abstract":"<div><p>The deep molecular generative model has recently become a research hotspot in pharmacy. This paper analyzes a large number of recent reports and reviews these models. In the central part of this paper, four compound databases and two molecular representation methods are compared. Five model architectures and applications for deep molecular generative models are emphatically introduced. Three evaluation metrics for model evaluation are listed. Finally, the limitations and challenges in this field are discussed to provide a reference and basis for developing and researching new models published in future.</p><h3>Graphical abstract</h3><p>Artificial intelligence has made significant leaps with the rapid development of big data and high-performance computing technology. As a technical means, artificial intelligence and deep learning have been deeply applied in all aspects of drug research, equipping researchers with innovative solutions and insights.</p>\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":708,"journal":{"name":"Molecular Diversity","volume":"28 4","pages":"2411 - 2427"},"PeriodicalIF":3.9000,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application progress of deep generative models in de novo drug design\",\"authors\":\"Yingxu Liu,&nbsp;Chengcheng Xu,&nbsp;Xinyi Yang,&nbsp;Yanmin Zhang,&nbsp;Yadong Chen,&nbsp;Haichun Liu\",\"doi\":\"10.1007/s11030-024-10942-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The deep molecular generative model has recently become a research hotspot in pharmacy. This paper analyzes a large number of recent reports and reviews these models. In the central part of this paper, four compound databases and two molecular representation methods are compared. Five model architectures and applications for deep molecular generative models are emphatically introduced. Three evaluation metrics for model evaluation are listed. Finally, the limitations and challenges in this field are discussed to provide a reference and basis for developing and researching new models published in future.</p><h3>Graphical abstract</h3><p>Artificial intelligence has made significant leaps with the rapid development of big data and high-performance computing technology. As a technical means, artificial intelligence and deep learning have been deeply applied in all aspects of drug research, equipping researchers with innovative solutions and insights.</p>\\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>\",\"PeriodicalId\":708,\"journal\":{\"name\":\"Molecular Diversity\",\"volume\":\"28 4\",\"pages\":\"2411 - 2427\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Molecular Diversity\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11030-024-10942-5\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Molecular Diversity","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s11030-024-10942-5","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
引用次数: 0

摘要

深度分子生成模型近来已成为药学领域的研究热点。本文分析了近期的大量报道,并对这些模型进行了评述。在本文的中心部分,比较了四个化合物数据库和两种分子表示方法。重点介绍了深度分子生成模型的五种模型架构和应用。列出了模型评估的三个评价指标。最后,讨论了该领域的局限性和挑战,为今后开发和研究新模型提供参考和依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Application progress of deep generative models in de novo drug design

The deep molecular generative model has recently become a research hotspot in pharmacy. This paper analyzes a large number of recent reports and reviews these models. In the central part of this paper, four compound databases and two molecular representation methods are compared. Five model architectures and applications for deep molecular generative models are emphatically introduced. Three evaluation metrics for model evaluation are listed. Finally, the limitations and challenges in this field are discussed to provide a reference and basis for developing and researching new models published in future.

Graphical abstract

Artificial intelligence has made significant leaps with the rapid development of big data and high-performance computing technology. As a technical means, artificial intelligence and deep learning have been deeply applied in all aspects of drug research, equipping researchers with innovative solutions and insights.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Molecular Diversity
Molecular Diversity 化学-化学综合
CiteScore
7.30
自引率
7.90%
发文量
219
审稿时长
2.7 months
期刊介绍: Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;
期刊最新文献
Integrated computational approaches for identification of potent pyrazole-based glycogen synthase kinase-3β (GSK-3β) inhibitors: 3D-QSAR, virtual screening, docking, MM/GBSA, EC, MD simulation studies. Transcriptome and interactome-based analyses to unravel crucial proteins and pathways involved in Acinetobacter baumannii pathogenesis. Fe3O4@SiO2@[Aminoglycol][Formate] as a new superparamagnetic nanocatalyst and [Aminoglycol][Formate] as a novel ionic liquid catalyst for preparation of new dimethyldihydropyrimido[4,5-b]quinolone derivatives. Identification of potential antigenic proteins and epitopes for the development of a monkeypox virus vaccine: an in silico approach. In silico studies on nicotinamide analogs as competitive inhibitors of nicotinamidase in methicillin-resistant Staphylococcus aureus.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1