Application progress of deep generative models in de novo drug design

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

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Abstract

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.

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深度生成模型在新药设计中的应用进展。

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。

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来源期刊

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;