Development of QSAR-based (MLR/ANN) predictive models for effective design of pyridazine corrosion inhibitors

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dc.authoridAkpan, Ekemini Daniel/0000-0003-2402-3402
dc.authoridLee, Han-Seung/0000-0001-9776-5859
dc.authoridGuo, Lei/0000-0001-7849-9583
dc.authoridVerma, chandrabhan/0000-0001-9249-7242
dc.authoridEBENSO, ENO/0000-0002-0411-9258
dc.contributor.authorQuadri, Taiwo W.
dc.contributor.authorOlasunkanmi, Lukman O.
dc.contributor.authorAkpan, Ekemini D.
dc.contributor.authorFayemi, Omolola E.
dc.contributor.authorLee, Han-Seung
dc.contributor.authorLgaz, Hassane
dc.contributor.authorVerma, Chandrabhan
dc.date.accessioned2024-10-26T18:11:24Z
dc.date.available2024-10-26T18:11:24Z
dc.date.issued2022
dc.departmentSivas Cumhuriyet Üniversitesi
dc.description.abstractTwenty pyridazine derivatives with previously reported experimental data were utilized to develop predictive models for the anticorrosion abilities of pyridazine-based compounds. The models were developed by using quantitative structure-activity relationship (QSAR) as a tool to relate essential molecular descriptors of the pyridazines with their experimental inhibition efficiencies. Chemical descriptors associated with frontier molecular orbitals (FMOs) were obtained using density functional theory (DFT) calculations, while others were obtained from additional calculations effected on Dragon 7 software. Five descriptors together with concentrations of the pyridazine inhibitors were used to develop the multiple linear regression (MLR) and artificial neural network (ANN) models. The optimal ANN model yielded the best results with 111.5910, 10.5637 and 10.2362 for MSE, RMSE and MAPE respectively. The results revealed that ANN gave better results than MLR model. The proposed models suggested that the adsorption of pyridazine derivatives is dependent on the five descriptors.Five pyridazine compounds were theoretically designed.
dc.description.sponsorshipMSIT (Ministry of Science and ICT), Korea, under the Grand Information and Communication Technology Research Center support program [IITP-2020-0-101741]
dc.description.sponsorshipThe authors gratefully acknowledge the Centre for High Performance Computing (CHPC), CSIR, South Africa for providing access to computational resources with which DFT calculations were performed using Gaussian 09. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information and Communication Technology Research Center support program (IITP-2020-0-101741) supervised by the IITP (Institute for Information and Communications, Technology Planning, and Evaluation).
dc.identifier.doi10.1016/j.mtcomm.2022.103163
dc.identifier.issn2352-4928
dc.identifier.scopus2-s2.0-85123047873
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1016/j.mtcomm.2022.103163
dc.identifier.urihttps://hdl.handle.net/20.500.12418/30660
dc.identifier.volume30
dc.identifier.wosWOS:000766219500001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofMaterials Today Communications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectCorrosion inhibitors
dc.subjectQSAR analysis
dc.subjectMLR model
dc.subjectANN model
dc.subjectMolecular descriptors
dc.subjectPyridazine derivatives
dc.titleDevelopment of QSAR-based (MLR/ANN) predictive models for effective design of pyridazine corrosion inhibitors
dc.typeArticle

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