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Application of an Expert System Based on Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (Ga-Anfis) in Qsar of Cathepsin K Inhibitors Publisher



Shahlaei M1, 2 ; Madadkarsobhani A3 ; Saghaie L2, 4 ; Fassihi A2, 4
Authors
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Authors Affiliations
  1. 1. Department of Medicinal Chemistry, School of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah, Iran
  2. 2. Department of Medicinal Chemistry, Faculty of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, 81746-73461 Isfahan, Iran
  3. 3. Department of Life Sciences, Barcelona Supercomputing Center, 08028 Barcelona, C/ Jordi Girona 31, Edificio Nexus II, Spain
  4. 4. Isfahan Pharmaceutical Sciences Research Center, 81746-73461 Isfahan, Iran

Source: Expert Systems with Applications Published:2012


Abstract

One strategy to potentially improve the success of drug design and development is to use chemometrics methods early in the process to propose molecules and scaffolds with ideal binding and to clarify physicochemical features influencing in their activity. Adaptive Neuro-Fuzzy Interference System (ANFIS) was used to construct the nonlinear quantitative structure-activity relationship (QSAR) model. The Genetic Algorithm (GA) was used to select descriptors which are responsible for the cathepsin K inhibitory activity of studied compounds. ANFIS regression is a nonlinear regression technique developed to relate many regressors to one or several response variables. The accuracy of the generated QSAR model (R 2 = 0.916) is described using various evaluation techniques, such as leave-one-out procedure (RLOO2=0.875) and validation through an external test set (Rpred2=0.932). © 2011 Elsevier Ltd. All rights reserved.
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