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

Authors :
Wang, Yu-Liang
Li, Jing-Yi
Shi, Xing-Xing
Wang, Zheng
Hao, Ge-Fei
Yang, Guang-Fu
Source :
Topics in Current Chemistry; Dec2021, Vol. 379 Issue 6, p1-24, 24p
Publication Year :
2021

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
23650869
Volume :
379
Issue :
6
Database :
Complementary Index
Journal :
Topics in Current Chemistry
Publication Type :
Academic Journal
Accession number :
153438417
Full Text :
https://doi.org/10.1007/s41061-021-00349-3