NDLI: Statistically validated QSAR study of some antagonists of the human CCR5 receptor using least square support vector machine based on the genetic algorithm and factor analysis

Content Provider	Springer Nature Link
Author	Shahlaie, Mohsen Fassihi, Afshin Pourhossein, Alireza Arkan, Elham
Copyright Year	2012
Abstract	Quantitative relationships between molecular structures and CCR5 inhibitory activities of forty two 1-amino-2-phenyl-4-(piperidin-1-yl)-butane derivatives were discovered by chemometrics tools including GA-MLR and FA-MLR as linear models and GA-LS-SVM and FA-LS-SVM as nonlinear models. GA-MLR analysis indicated that the topological (X2A) and geometrical (MAXDN) parameters have the most significant influence on the CCR5 inhibitory activity. FA-MLR model describes the effect of topological, geometrical, and quantum indices on the CCR5 inhibitory activity of the studied molecules. A comparison between the developed statistical methods revealed that FA-LS-SVM represented superior results and it could predict about 95 % of variance in the inhibitory activity data. The resulted models were validated for generalization and predictability by leave-one-out (LOO) cross-validation method. External validation showed high predictability of the calibration models. The predictability of the obtained QSAR models was also investigated by the Tropsha and Roy proposed criteria. Further validation by Y-randomization method confirmed that the obtained models were not due to a chance correlation. The applicability domain of the models was defined by leverage value. None of the studied compounds were outside the domain of the models.
Starting Page	1399
Ending Page	1414
Page Count	16
File Format	PDF
ISSN	10542523
Journal	Medicinal Chemistry Research
Volume Number	22
Issue Number	3
e-ISSN	15548120
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-06-17
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	QSAR CCR5 antagonists LS-SVM Genetic algorithm Factor analysis Pharmacology/Toxicology Biochemistry Cell Biology
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Pharmacology, Toxicology and Pharmaceutics

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

QSAR analysis of some 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 inhibitors using genetic algorithm-least square support vector machine

QSAR study of VEGFR-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): a comparative approach

QSAR study of active human glucagon receptor antagonists by SW-MLR and SW-SVM methods

QSAR study on hERG inhibitory effect of kappa opioid receptor antagonists by linear and non-linear methods

Prediction of the binding affinities of adenosine A2A receptor antagonists based on the heuristic method and support vector machine

QSAR and docking analysis of A2B adenosine receptor antagonists based on non-xanthine scaffold

QSAR Study of 17β-HSD3 Inhibitors by Genetic Algorithm-Support Vector Machine as a Target Receptor for the Treatment of Prostate Cancer.

QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR)

Docking and CoMFA study on novel human CCR5 receptor antagonists

Statistically validated QSAR study of some antagonists of the human CCR5 receptor using least square support vector machine based on the genetic algorithm and factor analysis

Similar Documents

QSAR analysis of some 1-(3,3-diphenylpropyl)-piperidinyl amides and ureas as CCR5 inhibitors using genetic algorithm-least square support vector machine

QSAR study of VEGFR-2 inhibitors by using genetic algorithm-multiple linear regressions (GA-MLR) and genetic algorithm-support vector machine (GA-SVM): a comparative approach

QSAR study of active human glucagon receptor antagonists by SW-MLR and SW-SVM methods

QSAR study on hERG inhibitory effect of kappa opioid receptor antagonists by linear and non-linear methods

Prediction of the binding affinities of adenosine A2A receptor antagonists based on the heuristic method and support vector machine

QSAR and docking analysis of A2B adenosine receptor antagonists based on non-xanthine scaffold

QSAR Study of 17β-HSD3 Inhibitors by Genetic Algorithm-Support Vector Machine as a Target Receptor for the Treatment of Prostate Cancer.

QSAR study of Nav1.7 antagonists by multiple linear regression method based on genetic algorithm (GA–MLR)

Docking and CoMFA study on novel human CCR5 receptor antagonists

Statistically validated QSAR study of some antagonists of the human CCR5 receptor using least square support vector machine based on the genetic algorithm and factor analysis