{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T03:08:12Z","timestamp":1787368092882,"version":"build-2736575974"},"reference-count":48,"publisher":"Wiley","issue":"18","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Comput Chem"],"published-print":{"date-parts":[[2024,7,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Inhibiting the enzymes carbonic anhydrase I (CA I) and carbonic anhydrase II (CA II) presents a potential avenue for addressing nervous system ailments such as glaucoma and Alzheimer's disease. Our study explored harnessing explainable artificial intelligence (XAI) to unveil the molecular traits inherent in CA I and CA II inhibitors. The PubChem molecular fingerprints of these inhibitors, sourced from the ChEMBL database, were subjected to detailed XAI analysis. The study encompassed training 10 regression models using IC<jats:sub>50<\/jats:sub> values, and their efficacy was gauged using metrics including <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>, RMSE, and time taken. The Decision Tree Regressor algorithm emerged as the optimal performer (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>: 0.93, RMSE: 0.43, time\u2010taken: 0.07). Furthermore, the PFI method unveiled key molecular features for CA I inhibitors, notably PubChemFP432 (C(\uf8feO)N) and PubChemFP6978 (C(\uf8feO)O). The SHAP analysis highlighted the significance of attributes like PubChemFP539 (C(\uf8feO)NCC), PubChemFP601 (C(\uf8feO)OCC), and PubChemFP432 (C(\uf8feO)N) in CA I inhibitiotable n. Likewise, features for CA II inhibitors encompassed PubChemFP528(C(\uf8feO)OCCN), PubChemFP791 (C(\uf8feO)OCCC), PubChemFP696 (C(\uf8feO)OCCCC), PubChemFP335 (C(\uf8feO)NCCN), PubChemFP580 (C(\uf8feO)NCCCN), and PubChemFP180 (C(\uf8feO)NCCC), identified through SHAP analysis. The sulfonamide group (S), aromatic ring (A), and hydrogen bonding group (H) exert a substantial impact on CA I and CA II enzyme activities and IC<jats:sub>50<\/jats:sub> values through the XAI approach. These insights into the CA I and CA II inhibitors are poised to guide future drug discovery efforts, serving as a beacon for innovative therapeutic interventions.<\/jats:p>","DOI":"10.1002\/jcc.27335","type":"journal-article","created":{"date-parts":[[2024,3,16]],"date-time":"2024-03-16T06:37:12Z","timestamp":1710571032000},"page":"1530-1539","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Explainable artificial intelligence in the design of selective carbonic anhydrase <scp>I\u2010II<\/scp> inhibitors via molecular fingerprinting"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2917-8860","authenticated-orcid":false,"given":"Kevser K\u00fcbra","family":"K\u0131rbo\u011fa","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Department of Bioengineering Bilecik Seyh Edebali University  Bilecik Turkey"},{"name":"Bioengineering Department S\u00fcleyman Demirel University  Isparta Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4677-8104","authenticated-orcid":false,"given":"Mesut","family":"I\u015f\u0131k","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Department of Bioengineering Bilecik Seyh Edebali University  Bilecik Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s004330050101"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.copbio.2021.12.003"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/14756360410001689540"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/med.10025"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.2174\/1568016043477305"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/13543776.2019.1679117"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/14756366.2021.2004592"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.5114\/ceji.2017.67320"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/13543784.2018.1548608"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1152\/ajplung.00022.2021"},{"key":"e_1_2_9_12_1","first-page":"136","volume":"10","author":"Preisig P. A.","year":"1987","journal-title":"Ren. Physiol."},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.3390\/md20110721"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/14756366.2021.1945049"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1159\/000490475"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.5049\/EBP.2021.19.2.29"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/molecules26216380"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pbiomolbio.2005.07.001"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.2174\/138161207780765954"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11030-021-10217-3"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107492"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.2174\/1386207003331454"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.21707"},{"key":"e_1_2_9_24_1","first-page":"1","volume":"10","author":"Probst D.","year":"2018","journal-title":"J. Chem."},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq134"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2015.07.007"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/s19132969"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01240150"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11030-023-10649-z"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1001\/archopht.1990.01070070020005"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bmcl.2005.06.055"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11030-022-10527-0"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/ardp.202000102"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1080\/14756366.2020.1746784"},{"key":"e_1_2_9_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bmcl.2004.12.052"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw1074"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-36944-6"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep17209"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.26223"},{"key":"e_1_2_9_40_1","volume-title":"Why Do We Set A Random State in Machine Learning Models?","author":"Pramoditha R.","year":"2022"},{"key":"e_1_2_9_41_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btm344"},{"key":"e_1_2_9_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2021.08.011"},{"key":"e_1_2_9_43_1","doi-asserted-by":"publisher","DOI":"10.1021\/jm4004285"},{"key":"e_1_2_9_44_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00088"},{"key":"e_1_2_9_45_1","unstructured":"M.Waskom O.Botvinnik D.Kane P.Hobson S.Lukauskas D. C.Gemperline A.Qalieh mwaskom\/seaborn: v0.8.1.Zenodo.2017https:\/\/doi.org\/10.5281\/zenodo.883859"},{"key":"e_1_2_9_46_1","volume-title":"Shapley Regressions: A Framework for Statistical Inference on Machine Learning Models","author":"Joseph A.","year":"2019"},{"key":"e_1_2_9_47_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1017\/CBO9780511528446","volume-title":"The Shapley Value: Essays in Honor of Lloyd S. Shapley","author":"Roth A. E.","year":"1988"},{"key":"e_1_2_9_48_1","doi-asserted-by":"publisher","DOI":"10.3390\/app13158782"},{"key":"e_1_2_9_49_1","unstructured":"Guhanesvar Feature Selection Based on Mutual Information Gain for Classification and Regression.2021."}],"container-title":["Journal of Computational Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jcc.27335","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T05:34:16Z","timestamp":1740116056000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jcc.27335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,15]]},"references-count":48,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2024,7,5]]}},"alternative-id":["10.1002\/jcc.27335"],"URL":"https:\/\/doi.org\/10.1002\/jcc.27335","archive":["Portico"],"relation":{},"ISSN":["0192-8651","1096-987X"],"issn-type":[{"value":"0192-8651","type":"print"},{"value":"1096-987X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,15]]},"assertion":[{"value":"2023-12-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-02-10","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-03-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}