A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm

ABSTRACT Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter, , is added in the original transfer function as a time-varying variable. The TVBGSA-based model was internally and externally validated based on , , , , , , Y-randomization test, and applicability domain evaluation. The validation results indicate that the proposed TVBGSA model is robust and not due to chance correlation. The descriptor selection and prediction performance of TVBGSA outperform BGSA method. TVBGSA shows higher of 0.957, of 0.951, of 0.954, of 0.938, and lower and compared to obtained results by BGSA, indicating the best prediction performance of the proposed TVBGSA model. The results clearly reveal that the proposed TVBGSA method is useful for constructing reliable and robust QSARs for predicting antidiabetic activity of DPP-IV inhibitors prior to designing and experimental synthesizing of new DPP-IV inhibitors.

[1]  Haithem Taha Mohammad Ali,et al.  A QSAR classification model for neuraminidase inhibitors of influenza A viruses (H1N1) based on weighted penalized support vector machine , 2017, SAR and QSAR in environmental research.

[2]  Siti Zaiton Mohd Hashim,et al.  Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm , 2012, Appl. Math. Comput..

[3]  U. Saqib,et al.  3D-QSAR studies on triazolopiperazine amide inhibitors of dipeptidyl peptidase-IV as anti-diabetic agents , 2009, SAR and QSAR in environmental research.

[4]  R. Todeschini,et al.  Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing / Volume II: Appendices, References , 2009 .

[5]  Bo Xing,et al.  Gravitational Search Algorithm , 2014 .

[6]  Bo Xing,et al.  Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms , 2013 .

[7]  C. Jiang,et al.  3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors , 2012 .

[8]  Bhumika D. Patel,et al.  3D-QSAR studies of dipeptidyl peptidase-4 inhibitors using various alignment methods , 2014, Medicinal Chemistry Research.

[9]  Hossam Faris,et al.  Binary dragonfly optimization for feature selection using time-varying transfer functions , 2018, Knowl. Based Syst..

[10]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[11]  V. Abbate,et al.  Design, Synthesis and in Vivo Evaluation of Novel Glycosylated Sulfonylureas as Antihyperglycemic Agents , 2015, Molecules.

[12]  V. Matassa,et al.  From lead to preclinical candidate: optimization of beta-homophenylalanine based inhibitors of dipeptidyl peptidase IV. , 2009, Bioorganic & medicinal chemistry letters.

[13]  Hossein Nezamabadi-pour,et al.  BGSA: binary gravitational search algorithm , 2010, Natural Computing.

[14]  Julio Caballero,et al.  Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks , 2006, Journal of molecular modeling.

[15]  E Estrada.  On the Topological Sub-Structural Molecular Design (TOSS-MODE) in QSPR/QSAR and Drug Design Research , 2000, SAR and QSAR in environmental research.

[16]  Sajjad Gharaghani,et al.  Hybrid docking-QSAR studies of DPP-IV inhibition activities of a series of aminomethyl-piperidones , 2016, Comput. Biol. Chem..

[17]  Hossein Nezamabadi-pour,et al.  A comprehensive survey on gravitational search algorithm , 2018, Swarm Evol. Comput..

[18]  K. V. Arya,et al.  An effective gbest-guided gravitational search algorithm for real-parameter optimization and its application in training of feedforward neural networks , 2017, Knowl. Based Syst..

[19]  Roberto Todeschini,et al.  Structure/Response Correlations and Similarity/Diversity Analysis by GETAWAY Descriptors, 1. Theory of the Novel 3D Molecular Descriptors , 2002, J. Chem. Inf. Comput. Sci..

[20]  S. Paliwal,et al.  Development of a robust QSAR model to predict the affinity of pyrrolidine analogs for dipeptidyl peptidase IV (DPP- IV) , 2011, Journal of enzyme inhibition and medicinal chemistry.

[21]  Hao Tian,et al.  A new approach for unit commitment problem via binary gravitational search algorithm , 2014, Appl. Soft Comput..

[22]  H. Fujii,et al.  Novel series of 3-amino-N-(4-aryl-1,1-dioxothian-4-yl)butanamides as potent and selective dipeptidyl peptidase IV inhibitors. , 2012, Bioorganic & medicinal chemistry letters.

[23]  Gerta Rücker,et al.  y-Randomization and Its Variants in QSPR/QSAR , 2007, J. Chem. Inf. Model..

[24]  S. Jain,et al.  Trifluorophenyl-based inhibitors of dipeptidyl peptidase-IV as antidiabetic agents: 3D-QSAR COMFA, CoMSIA methodologies , 2017, Network Modeling Analysis in Health Informatics and Bioinformatics.

[25]  Jim Euchner.  Design , 2014, Catalysis from A to Z.

[26]  M. A. Motaleb,et al.  Rational design and synthesis of new tetralin-sulfonamide derivatives as potent anti-diabetics and DPP-4 inhibitors: 2D & 3D QSAR, in vivo radiolabeling and bio distribution studies. , 2018, Bioorganic chemistry.

[27]  V. Vyas,et al.  CoMFA and CoMSIA studies on C-aryl glucoside SGLT2 inhibitors as potential anti-diabetic agents , 2013, SAR and QSAR in environmental research.

[28]  Janet M. Baker,et al.  Dragon , 1989, HLT.

[29]  Synthesis and biological evaluation of xanthine derivatives on dipeptidyl peptidase 4. , 2013, Chemical & pharmaceutical bulletin.

[30]  J. Shaw,et al.  Global and societal implications of the diabetes epidemic , 2001, Nature.

[31]  Tayebeh Baghgoli,et al.  Descriptor selection evaluation of binary gravitational search algorithm in quantitative structure-activity relationship studies of benzyl phenyl ether diamidine's antiprotozoal activity and Chalcone's anticancer potency , 2018, Chemometrics and Intelligent Laboratory Systems.

[32]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[33]  Azah Mohamed,et al.  Optimal power quality monitor placement in power systems using an adaptive quantum-inspired binary gravitational search algorithm , 2014 .

[34]  Jie-Oh Lee,et al.  Synthesis and biological evaluation of homopiperazine derivatives with beta-aminoacyl group as dipeptidyl peptidase IV inhibitors. , 2008, Bioorganic & medicinal chemistry letters.

[35]  Xiaodong Li,et al.  A time-varying transfer function for balancing the exploration and exploitation ability of a binary PSO , 2017, Appl. Soft Comput..

[36]  Hao Tian,et al.  Improved gravitational search algorithm for unit commitment considering uncertainty of wind power , 2014, Energy.

[37]  Paola Gramatica,et al.  Principles of QSAR models validation: internal and external , 2007 .

[38]  M. Mousavi,et al.  Gravitational search algorithm: A new feature selection method for QSAR study of anticancer potency of imidazo[4,5-b]pyridine derivatives , 2013 .

[39]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..