比较MMPDB和REINVENT4生成的分子与药物发现设计团队的想法

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Shan Sun,  and , David J. Huggins*, 
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引用次数: 0

摘要

这项研究比较了由Sanders三机构治疗发现研究所的药物发现项目团队设计的分子与由两种计算工具(MMPDB和REINVENT4)生成的分子。研究了七个不同化学型的不同测试案例,以探索这些计算工具在药物发现早期阶段补充人类专业知识的潜力。通过比较MMPDB和REINVENT4生成的分子结构和性质与项目设计团队设计的分子结构和性质,我们旨在评估这些工具的价值。结果表明,MMPDB和REINVENT4覆盖的化学空间比药物发现项目组的想法覆盖的区域大。然而,这两种方法覆盖的化学空间有很大的不同,两种方法都不能完全覆盖药物发现项目组确定的化学空间。因此,计算方法相互补充,并与药物发现项目组的想法相辅相成。生成分子设计工具的有效应用有可能通过扩大药物发现过程中探索的化学空间和实现最佳探索来加速新治疗候选药物的识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Comparing Molecules Generated by MMPDB and REINVENT4 with Ideas from Drug Discovery Design Teams

Comparing Molecules Generated by MMPDB and REINVENT4 with Ideas from Drug Discovery Design Teams

This study compares molecules designed by drug discovery project teams from the Sanders Tri-Institutional Therapeutics Discovery Institute with molecules generated by two computational tools: MMPDB and REINVENT4. Seven different test cases with diverse chemotypes are studied in order to explore the potential of these computational tools in complementing human expertise in the early stages of drug discovery. By comparing the molecular structures and properties generated by MMPDB and REINVENT4 to those designed by project design teams, we aim to assess the value of such tools. The results indicate that MMPDB and REINVENT4 cover regions of chemical space larger than those covered by ideas from the drug discovery project teams. However, the chemical spaces covered by the two methods are quite different, and neither method completely covers the chemical space identified by the drug discovery project teams. Thus, the computational methods are complementary to one another and to drug discovery project team ideation. Effective application of generative molecule design tools has the potential to accelerate the identification of novel therapeutic candidates by expanding the chemical space explored during drug discovery and enabling optimal exploration.

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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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