Application of an expert system based on Genetic Algorithm-Adaptive Neuro-Fuzzy Inference System (GA-ANFIS) in QSAR of cathepsin K inhibitors

  • Authors:

  • Mohsen Shahlaei;Armin Madadkar-Sobhani;Lotfollah Saghaie;Afshin Fassihi

  • Affiliations:

  • Department of Medicinal Chemistry, School of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah, Iran and Department of Medicinal Chemistry, Faculty of Pharmacy and Pharmaceutical Sci ...;Department of Life Sciences, Barcelona Supercomputing Center, C/ Jordi Girona 31, Edificio Nexus II, 08028 Barcelona, Spain;Department of Medicinal Chemistry, Faculty of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, 81746-73461 Isfahan, Iran and Isfahan Pharmaceutical Sciences Research C ...;Department of Medicinal Chemistry, Faculty of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, 81746-73461 Isfahan, Iran and Isfahan Pharmaceutical Sciences Research C ...

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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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 (R"L"O"O^2=0.875) and validation through an external test set (R"p"r"e"d^2=0.932).