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基于网络的定量构效关系资源有助于有效的药物发现。

Web-Based Quantitative Structure-Activity Relationship Resources Facilitate Effective Drug Discovery.

机构信息

Key Laboratory of Pesticide and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, 430079, People's Republic of China.

International Joint Research Center for Intelligent Biosensor Technology and Health, Central China Normal University, Wuhan, 430079, People's Republic of China.

出版信息

Top Curr Chem (Cham). 2021 Sep 23;379(6):37. doi: 10.1007/s41061-021-00349-3.

Abstract

Traditional drug discovery effectively contributes to the treatment of many diseases but is limited by high costs and long cycles. Quantitative structure-activity relationship (QSAR) methods were introduced to evaluate the activity of compounds virtually, which saves the significant cost of determining the activities of the compounds experimentally. Over the past two decades, many web tools for QSAR modeling with various features have been developed to facilitate the usage of QSAR methods. These web tools significantly reduce the difficulty of using QSAR and indirectly promote drug discovery. However, there are few comprehensive summaries of these QSAR tools, and researchers may have difficulty determining which tool to use. Hence, we systematically surveyed the mainstream web tools for QSAR modeling. This work may guide researchers in choosing appropriate web tools for developing QSAR models, and may also help develop more bioinformatics tools based on these existing resources. For nonprofessionals, we also hope to make more people aware of QSAR methods and expand their use.

摘要

传统的药物发现方法在治疗许多疾病方面非常有效,但受到高成本和长周期的限制。定量构效关系 (QSAR) 方法被引入到化合物活性的虚拟评估中,这节省了大量通过实验确定化合物活性的成本。在过去的二十年中,已经开发了许多具有不同特点的用于 QSAR 建模的网络工具,以方便 QSAR 方法的使用。这些网络工具显著降低了使用 QSAR 的难度,并间接地促进了药物发现。然而,关于这些 QSAR 工具的综合总结很少,研究人员可能难以确定使用哪个工具。因此,我们系统地调查了主流的 QSAR 建模网络工具。这项工作可以指导研究人员选择合适的网络工具来开发 QSAR 模型,也可以帮助在这些现有资源的基础上开发更多的生物信息学工具。对于非专业人士,我们也希望让更多的人了解 QSAR 方法并扩大其应用。

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