Improving predictive efficacy for drug resistance in novel hiv-1 protease inhibitors through transfer learning mechanisms

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dc.contributor.authorTunç, Hüseyin
dc.contributor.authorYılmaz, Sümeyye
dc.contributor.authorDarendeli Kiraz, Büşra Nur
dc.contributor.authorSarı, Murat
dc.contributor.authorKotil, Seyfullah Enes
dc.contributor.authorŞensoy, Özge
dc.contributor.authorDurdağı, Serdar
dc.date.accessioned2025-10-10T09:54:02Z
dc.date.available2025-10-10T09:54:02Z
dc.date.issued2024
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Rektörlük, Rejeneratif ve Restoratif Tıp Araştırmaları Merkezi (REMER)
dc.departmentİstanbul Medipol Üniversitesi, Rektörlük, Sağlık Bilim ve Teknolojileri Araştırma Enstitüsü
dc.description.abstractThe human immunodeficiency virus presents a significant global health challenge due to its rapid mutation and the development of resistance mechanisms against antiretroviral drugs. Recent studies demonstrate the impressive performance of machine learning (ML) and deep learning (DL) models in predicting the drug resistance profile of specific FDA-approved inhibitors. However, generalizing ML and DL models to learn not only from isolates but also from inhibitor representations remains challenging for HIV-1 infection. We propose a novel drug-isolate-fold change (DIF) model framework that aims to predict drug resistance score directly from the protein sequence and inhibitor representation. Various ML and DL models, inhibitor representations, and protein representations were analyzed through realistic validation mechanisms. To enhance the molecular learning capacity of DIF models, we employ a transfer learning approach by pretraining a graph neural network (GNN) model for activity prediction on a data set of 4855 HIV-1 protease inhibitors (PIs). By performing various realistic validation strategies on internal and external genotype-phenotype data sets, we statistically show that the learned representations of inhibitors improve the predictive ability of DIF-based ML and DL models. We achieved an accuracy of 0.802, AUROC of 0.874, and r of 0.727 for the unseen external PIs. By comparing the DIF-based models with a null model consisting of isolate-fold change (IF) architecture, it is observed that the DIF models significantly benefit from molecular representations. Combined results from various testing strategies and statistical tests confirm the effectiveness of DIF models in testing novel PIs for drug resistance in the presence of an isolate.
dc.description.sponsorshipTürkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) ; Türkiye Cumhuriyeti Kalkınma Bakanlığı
dc.identifier.citationTunç, H., Yılmaz, SümeyyeDarendeli Kiraz, B. N., Sarı, M., Kotil, S. E., Şensoy, Ö. ... Durdağı, S. (2024). Improving predictive efficacy for drug resistance in novel hiv-1 protease inhibitors through transfer learning mechanisms. Journal of Chemical Information and Modeling, 64(20), 7844-7863. http://dx.doi.org/10.1021/acs.jcim.4c01037
dc.identifier.doi10.1021/acs.jcim.4c01037
dc.identifier.endpage7863
dc.identifier.issn1549-9596
dc.identifier.issn1549-960X
dc.identifier.issue20
dc.identifier.pmid39393002
dc.identifier.scopus2-s2.0-85206493696
dc.identifier.scopusqualityQ1
dc.identifier.startpage7844
dc.identifier.urihttp://dx.doi.org/10.1021/acs.jcim.4c01037
dc.identifier.urihttps://hdl.handle.net/20.500.12511/13080
dc.identifier.volume64
dc.identifier.wosWOS:001335472600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorŞensoy, Özge
dc.institutionauthorid0000-0001-5950-3436
dc.language.isoen
dc.relation.ispartofJournal of Chemical Information and Modeling
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/121C523
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/TR10/21/YEP/0133
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDrug Resistance
dc.subjectHIV Infections
dc.subjectHIV Protease Inhibitors
dc.subjectDeep Learning
dc.titleImproving predictive efficacy for drug resistance in novel hiv-1 protease inhibitors through transfer learning mechanisms
dc.typeArticle

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