Classification of some chemical drugs by genetic algorithm and deep neural network hybrid method

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dc.authoridAVCU, Fatih Mehmet/0000-0002-1973-7745
dc.authoridKarakaplan, Mustafa/0000-0002-9664-4112
dc.authorwosidAVCU, Fatih Mehmet/ABG-8390-2020
dc.authorwosidKarakaplan, Mustafa/H-3708-2018
dc.contributor.authorKarakaplan, Mustafa
dc.contributor.authorAvcu, Fatih Mehmet
dc.date.accessioned2024-08-04T20:49:18Z
dc.date.available2024-08-04T20:49:18Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractDeep neural networks (DNN) and genetic algorithm (GA) are gaining importance quickly with many successful applications in the field of science and technology. They are indispensable tool for the numerical solution of difficult problems. It is possible to optimize DNNs using the GA and this combination can be used to classify data. In this article, some drugs are classified by Monte Carlo sampling with combination of GA and DNN due to stochastic nature of the domain, exponential number of variables and small number of chemical species. In addition to the values obtained from the databases of selected drugs, molecular dynamic and ab initio molecular mechanical calculation results were also used. The aim of this study is to generalize the molecular classification with the data obtained from chemical databases as well as molecular docking results by using the combination of deep learning and GA and its usability in drug design. The selected drugs are some agonist and antagonist drugs that bind to dopamine receptors, which are widely studied and well known in the literature. To train the DNN, input datasets were chosen by the GA framework written in pure Python named PyEvolve. Classification of drugs has been analyzed with the focus on orbital energies and docking results. It is possible to use this algorithm in many in silico calculations such as affinity and separation processes. The reliability of the algorithm was tested with the results given in the literature and the expected values were estimated at 93.8%.en_US
dc.description.sponsorshipInonu University Scientific Research Project Department [FBA-2019-1726]en_US
dc.description.sponsorshipThe authors would like to thanks to Inonu University Scientific Research Project Department with FBA-2019-1726 project number. Also, the numerical calculations reported in this article were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources).en_US
dc.identifier.doi10.1002/cpe.6242
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.issue13en_US
dc.identifier.scopus2-s2.0-85101249890en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1002/cpe.6242
dc.identifier.urihttps://hdl.handle.net/11616/99776
dc.identifier.volume33en_US
dc.identifier.wosWOS:000620327100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofConcurrency and Computation-Practice & Experienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectdeep learningen_US
dc.subjectdrug designen_US
dc.subjectgenetic algorithmen_US
dc.subjectmachine learningen_US
dc.subjectmolecular dockingen_US
dc.subjectmolecular modelingen_US
dc.subjectstructure optimizationen_US
dc.titleClassification of some chemical drugs by genetic algorithm and deep neural network hybrid methoden_US
dc.typeArticleen_US

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