{"title":"Web-Based Quantitative Structure–Activity Relationship Resources Facilitate Effective Drug Discovery","authors":"Yu-Liang Wang, Jing-Yi Li, Xing-Xing Shi, Zheng Wang, Ge-Fei Hao, Guang-Fu Yang","doi":"10.1007/s41061-021-00349-3","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p><h3>Graphic Abstract</h3>\n <figure><div><div><div><picture><source><img></source></picture></div></div></div></figure>\n </div>","PeriodicalId":802,"journal":{"name":"Topics in Current Chemistry","volume":"379 6","pages":""},"PeriodicalIF":8.6000,"publicationDate":"2021-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Topics in Current Chemistry","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s41061-021-00349-3","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Chemistry","Score":null,"Total":0}
引用次数: 6
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.
期刊介绍:
Topics in Current Chemistry provides in-depth analyses and forward-thinking perspectives on the latest advancements in chemical research. This renowned journal encompasses various domains within chemical science and their intersections with biology, medicine, physics, and materials science.
Each collection within the journal aims to offer a comprehensive understanding, accessible to both academic and industrial readers, of emerging research in an area that captivates a broader scientific community.
In essence, Topics in Current Chemistry illuminates cutting-edge chemical research, fosters interdisciplinary collaboration, and facilitates knowledge-sharing among diverse scientific audiences.