دورية أكاديمية
أعرض في EDS

Enhancing hERG Risk Assessment with Interpretable Classificatory and Regression Models.

التفاصيل البيبلوغرافية
العنوان: Enhancing hERG Risk Assessment with Interpretable Classificatory and Regression Models.
المؤلفون: Sanches IH; Laboratory for Molecular Modeling and Drug Design (LabMol), Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil.; Center for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil.; Center for the Research and Advancement in Fragments and Molecular Targets (CRAFT), School of Pharmaceutical Sciences at Ribeirao Preto, University of São Paulo, Ribeirão Preto, SP 05508-220, Brazil., Braga RC; InsilicAll Inc., São Paulo, SP 04571-010, Brazil., Alves VM; University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States., Andrade CH; Laboratory for Molecular Modeling and Drug Design (LabMol), Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil.; Center for Excellence in Artificial Intelligence (CEIA), Institute of Informatics, Universidade Federal de Goiás, Goiânia, GO 74690-900, Brazil.; Center for the Research and Advancement in Fragments and Molecular Targets (CRAFT), School of Pharmaceutical Sciences at Ribeirao Preto, University of São Paulo, Ribeirão Preto, SP 05508-220, Brazil.
المصدر: Chemical research in toxicology [Chem Res Toxicol] 2024 Jun 17; Vol. 37 (6), pp. 910-922. Date of Electronic Publication: 2024 May 23.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: American Chemical Society Country of Publication: United States NLM ID: 8807448 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1520-5010 (Electronic) Linking ISSN: 0893228X NLM ISO Abbreviation: Chem Res Toxicol Subsets: MEDLINE
أسماء مطبوعة: Publication: Washington Dc : American Chemical Society
Original Publication: Washington, DC : American Chemical Society, c1988-
مواضيع طبية MeSH: Quantitative Structure-Activity Relationship* , Ether-A-Go-Go Potassium Channels*/antagonists & inhibitors , Ether-A-Go-Go Potassium Channels*/metabolism, Humans ; Risk Assessment ; Regression Analysis ; Potassium Channel Blockers/pharmacology ; Potassium Channel Blockers/chemistry
مستخلص: The human Ether-à-go-go-Related Gene (hERG) is a transmembrane protein that regulates cardiac action potential, and its inhibition can induce a potentially deadly cardiac syndrome. In vitro tests help identify hERG blockers at early stages; however, the high cost motivates searching for alternative, cost-effective methods. The primary goal of this study was to enhance the Pred-hERG tool for predicting hERG blockage. To achieve this, we developed new QSAR models that incorporated additional data, updated existing classificatory and multiclassificatory models, and introduced new regression models. Notably, we integrated SHAP (SHapley Additive exPlanations) values to offer a visual interpretation of these models. Utilizing the latest data from ChEMBL v30, encompassing over 14,364 compounds with hERG data, our binary and multiclassification models outperformed both the previous iteration of Pred-hERG and all publicly available models. Notably, the new version of our tool introduces a regression model for predicting hERG activity (pIC50). The optimal model demonstrated an R 2 of 0.61 and an RMSE of 0.48, surpassing the only available regression model in the literature. Pred-hERG 5.0 now offers users a swift, reliable, and user-friendly platform for the early assessment of chemically induced cardiotoxicity through hERG blockage. The tool provides versatile outcomes, including (i) classificatory predictions of hERG blockage with prediction reliability, (ii) multiclassificatory predictions of hERG blockage with reliability, (iii) regression predictions with estimated pIC 50 values, and (iv) probability maps illustrating the contribution of chemical fragments for each prediction. Furthermore, we implemented explainable AI analysis (XAI) to visualize SHAP values, providing insights into the contribution of each feature to binary classification predictions. A consensus prediction calculated based on the predictions of the three developed models is also present to assist the user's decision-making process. Pred-hERG 5.0 has been designed to be user-friendly, making it accessible to users without computational or programming expertise. The tool is freely available at http://predherg.labmol.com.br.
References: Comput Toxicol. 2018 May;6:55-63. (PMID: 29806042)
Front Pharmacol. 2020 Jan 28;10:1631. (PMID: 32063849)
J Chem Inf Model. 2010 Jul 26;50(7):1189-204. (PMID: 20572635)
Toxicology. 2021 Dec;464:153018. (PMID: 34757159)
Curr Top Med Chem. 2014;14(11):1399-415. (PMID: 24805060)
Fed Regist. 2005 Oct 20;70(202):61133-4. (PMID: 16237859)
BMC Bioinformatics. 2019 May 29;20(Suppl 10):250. (PMID: 31138104)
J Chem Inf Model. 2016 Jul 25;56(7):1243-52. (PMID: 27280890)
Sci Robot. 2019 Dec 18;4(37):. (PMID: 33137719)
Nucleic Acids Res. 2019 Jan 8;47(D1):D930-D940. (PMID: 30398643)
Cell. 1999 Apr 16;97(2):175-87. (PMID: 10219239)
J Chem Inf Model. 2010 Jan;50(1):68-78. (PMID: 20053000)
Sci Adv. 2020 Oct 14;6(42):. (PMID: 33055163)
J Cheminform. 2018 Jun 26;10(1):29. (PMID: 29943074)
Nat Mach Intell. 2020 Jan;2(1):56-67. (PMID: 32607472)
J Cheminform. 2021 Aug 16;13(1):60. (PMID: 34399849)
BMC Med. 2016 Feb 04;14:10. (PMID: 26843061)
Curr Drug Saf. 2020;15(1):4-12. (PMID: 31584381)
J Cheminform. 2015 May 20;7:20. (PMID: 26052348)
Bioinformatics. 2019 Mar 15;35(6):1067-1069. (PMID: 30165565)
PLoS One. 2020 Nov 4;15(11):e0234946. (PMID: 33147278)
Nucleic Acids Res. 2021 Jul 2;49(W1):W5-W14. (PMID: 33893803)
Structure. 2021 Mar 4;29(3):203-212.e4. (PMID: 33450182)
J Chem Inf Model. 2021 Sep 27;61(9):4758-4770. (PMID: 34506150)
Curr Pharm Des. 2007;13(34):3494-504. (PMID: 18220786)
J Clin Pharmacol. 2006 May;46(5):498-507. (PMID: 16638733)
Front Chem. 2017 Feb 23;5:7. (PMID: 28503546)
J Chem Inf Model. 2022 Apr 25;62(8):1830-1839. (PMID: 35404051)
J Chem Inf Model. 2020 Dec 28;60(12):6007-6019. (PMID: 33259212)
Nat Rev Drug Discov. 2003 Jun;2(6):439-47. (PMID: 12776219)
J Chem Inf Model. 2012 Nov 26;52(11):3099-105. (PMID: 23092397)
J Chem Inf Model. 2018 Jun 25;58(6):1224-1233. (PMID: 29772901)
J Appl Toxicol. 2012 Oct;32(10):834-42. (PMID: 22744888)
Bioinformatics. 2020 May 1;36(10):3049-3055. (PMID: 32022860)
Front Pharmacol. 2018 Sep 19;9:1035. (PMID: 30333745)
Drug Discov Today. 2005 Jan 15;10(2):149-55. (PMID: 15718164)
المشرفين على المادة: 0 (Ether-A-Go-Go Potassium Channels)
0 (Potassium Channel Blockers)
تواريخ الأحداث: Date Created: 20240523 Date Completed: 20240617 Latest Revision: 20240622
رمز التحديث: 20240622
مُعرف محوري في PubMed: PMC11187631
DOI: 10.1021/acs.chemrestox.3c00400
PMID: 38781421
قاعدة البيانات: MEDLINE
الوصف
تدمد:1520-5010
DOI:10.1021/acs.chemrestox.3c00400