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Prediction of chemical–protein interactions: multitarget-QSAR versus computational chemogenomic methods

Content Provider	Royal Society of Chemistry (RSC)
Author	Liu, Guixia Cheng, Feixiong Li, Jie Zhou, Yadi Li, Weihua Tang, Yun
Copyright Year	2012
Abstract	Elucidation of chemical–protein interactions (CPI) is the basis of target identification and drug discovery. It is time-consuming and costly to determine CPI experimentally, and computational methods will facilitate the determination of CPI. In this study, two methods, multitarget quantitative structure–activity relationship (mt-QSAR) and computational chemogenomics, were developed for CPI prediction. Two comprehensive data sets were collected from the ChEMBL database for method assessment. One data set consisted of 81 689 CPI pairs among 50 924 compounds and 136 G-protein coupled receptors (GPCRs), while the other one contained 43 965 CPI pairs among 23 376 compounds and 176 kinases. The range of the area under the receiver operating characteristic curve (AUC) for the test sets was 0.95 to 1.0 and 0.82 to 1.0 for 100 GPCR mt-QSAR models and 100 kinase mt-QSAR models, respectively. The AUC of 5-fold cross validation were about 0.92 for both 176 kinases and 136 GPCRs using the chemogenomic method. However, the performance of the chemogenomic method was worse than that of mt-QSAR for the external validation set. Further analysis revealed that there was a high false positive rate for the external validation set when using the chemogenomic method. In addition, we developed a web server named CPI-Predictor, http://www.lmmd.org/online_services/cpi_predictor/, which is available for free. The methods and tool have potential applications in network pharmacology and drug repositioning.
Starting Page	2373
Ending Page	2384
Page Count	12
File Format	HTM / HTML PDF
ISSN	1742206X
Volume Number	8
Issue Number	9
Journal	Molecular BioSystems
DOI	10.1039/c2mb25110h
Language	English
Publisher	Royal Society of Chemistry
Access Restriction	Open
Subject Keyword	Cross-validation (statistics) False positives and false negatives Receiver operating characteristic Training, test, and validation sets Kinase Cost per impression Drug repositioning Quantitative structure\u2013activity relationship Pharmacology Drug discovery Web server
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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Tang Y. Prediction of chemical-protein interactions: multitarget-QSAR versus computational chemogenomic methods. Mol Biosyst 2012

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