NDLI: Does tautomerism influence the outcome of QSAR modeling?

Content Provider	Springer Nature Link
Author	Masand, Vijay H. Mahajan, Devidas T. Ben Hadda, Taibi Jawarkar, Rahul D. Alafeefy, Ahmed M. Rastija, Vesna Ali, Mohamed Ashraf
Copyright Year	2013
Abstract	Tautomerism is an important aspect associated with a variety of pharmacologically and biologically active compounds. It is a challenge to account for tautomerism in computer-aided drug designing (CADD). The estimations and calculations of many physico-chemical properties and theoretical descriptors of the molecules are sensitive to tautomerism. In this study, we have attempted to analyze the effect of tautomerism on feature selection and statistical performance/characteristics of conventional quantitative structure–activity relationship (QSAR) equations. These equations are developed using 2D and 3D-descriptors employing two different statistical methods, i.e., genetic algorithm (GA) and stepwise regression (SR). Five datasets of moderate sizes viz. (1) anti-malarial activity of synthetic prodiginines against multi-drug resistant strain (N = 43), (2) anti-malarial activity of bisaryl quinolones (N = 37), (3) anti-malarial activity of phosphoramidate and phosphorothioamidate analogs of amiprophos methyl (N = 36), (4) anti-proliferative activity of substituted N-phenyl ureidobenzenesulfonate derivatives (N = 44), and (5) anti-HIV activity of indolylarylsulfones as HIV-1 non-nucleoside reverse transcriptase inhibitors (N = 36) showing different types of tautomerism were used in the study. In each case, the developed model and the selected descriptors derived using one tautomer were applied on other tautomeric forms to understand the influence of tautomerism on QSAR equations. Different parameters like R, R 2, R adj 2 , R cv 2 , F, S and Y-randomization were used for thorough validation of all the models. The results revealed that tautomerism has significant influence on feature selection. In addition, it was found that tautomerism has a great influence on the performance of QSAR models of the second and the third datasets. However, no significant influence was observed on the statistical characteristics of QSAR models for datasets 1, 4, and 5. Therefore, it is suggested that separate models need to be developed for different tautomeric forms of a dataset.
Starting Page	1742
Ending Page	1757
Page Count	16
File Format	PDF
ISSN	10542523
Journal	Medicinal Chemistry Research
Volume Number	23
Issue Number	4
e-ISSN	15548120
Language	English
Publisher	Springer US
Publisher Date	2013-09-12
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Tautomerism QSAR Statistical robustness Stepwise regression Genetic algorithm Pharmacology/Toxicology Biochemistry Cell Biology
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Pharmacology, Toxicology and Pharmaceutics

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