Literature DB >> 33140820

Cloud 3D-QSAR: a web tool for the development of quantitative structure-activity relationship models in drug discovery.

Yu-Liang Wang1, Fan Wang2, Xing-Xing Shi2, Chen-Yang Jia1, Feng-Xu Wu2, Ge-Fei Hao3, Guang-Fu Yang4.   

Abstract

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure-Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/.
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  3D-QSAR; CoMFA; drug design; quantitative structure–activity relationship; web server

Year:  2021        PMID: 33140820     DOI: 10.1093/bib/bbaa276

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  2 in total

1.  In silico local QSAR modeling of bioconcentration factor of organophosphate pesticides.

Authors:  Purusottam Banjare; Balaji Matore; Jagadish Singh; Partha Pratim Roy
Journal:  In Silico Pharmacol       Date:  2021-04-04

2.  Two Myricetin-Derived Flavonols from Morella rubra Leaves as Potent α-Glucosidase Inhibitors and Structure-Activity Relationship Study by Computational Chemistry.

Authors:  Yilong Liu; Ruoqi Wang; Chuanhong Ren; Yifeng Pan; Jiajia Li; Xiaoyong Zhao; Changjie Xu; Kunsong Chen; Xian Li; Zhiwei Gao
Journal:  Oxid Med Cell Longev       Date:  2022-04-19       Impact factor: 7.310

  2 in total

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