Precision Enhanced Bioactivity Prediction of Tyrosine Kinase Inhibitors by Integrating Deep Learning and Molecular Fingerprints Towards Cost-Effective and Targeted Cancer Therapy

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dc.contributor.authorYagin, Fatma Hilal
dc.contributor.authorGormez, Yasin
dc.contributor.authorColak, Cemil
dc.contributor.authorAlgarni, Abdulmohsen
dc.contributor.authorAl-Hashem, Fahaid
dc.contributor.authorArdigo, Luca Paolo
dc.date.accessioned2026-04-04T13:30:59Z
dc.date.available2026-04-04T13:30:59Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractBackground and Objective: Dysregulated tyrosine kinase signaling is a central driver of tumorigenesis, metastasis, and therapeutic resistance. While tyrosine kinase inhibitors (TKIs) have revolutionized targeted cancer treatment, identifying compounds with optimal bioactivity remains a critical bottleneck. This study presents a robust machine learning framework-leveraging deep artificial neural networks (dANNs), convolutional neural networks (CNNs), and structural molecular fingerprints-to accurately predict TKI bioactivity, ultimately accelerating the preclinical phase of drug development. Methods: A curated dataset of 28,314 small molecules from the ChEMBL database targeting 11 tyrosine kinases was analyzed. Using Morgan fingerprints and physicochemical descriptors (e.g., molecular weight, LogP, hydrogen bonding), ten supervised models, including dANN, SVM, CatBoost, and CNN, were trained and optimized through a randomized hyperparameter search. Model performance was evaluated using F1-score, ROC-AUC, precision-recall curves, and log loss. Results: SVM achieved the highest F1-score (87.9%) and accuracy (85.1%), while dANNs yielded the lowest log loss (0.25096), indicating superior probabilistic reliability. CatBoost excelled in ROC-AUC and precision-recall metrics. The integration of Morgan fingerprints significantly improved bioactivity prediction across all models by enhancing structural feature recognition. Conclusions: This work highlights the transformative role of machine learning-particularly dANNs and SVM-in rational drug discovery. By enabling accurate bioactivity prediction, our model pipeline can effectively reduce experimental burden, optimize compound selection, and support personalized cancer treatment design. The proposed framework advances kinase inhibitor screening pipelines and provides a scalable foundation for translational applications in precision oncology. By enabling early identification of bioactive compounds with favorable pharmacological profiles, the results of this study may support more efficient candidate selection for clinical drug development, particularly in regards to cancer therapy and kinase-associated disorders.
dc.description.sponsorshipKing Khalid University [2/21/46]; Deanship of Scientific Research and Graduate Studies at King Khalid University
dc.description.sponsorshipThis research was financially supported by the Deanship of Scientific Research and Graduate Studies at King Khalid University under research grant number (R.G.P.2/21/46).
dc.identifier.doi10.3390/ph18070975
dc.identifier.issn1424-8247
dc.identifier.issue7
dc.identifier.orcid0000-0001-7677-5070
dc.identifier.orcid0000-0002-9848-7958
dc.identifier.orcid0000-0001-5406-098X
dc.identifier.orcid0000-0002-7556-958X
dc.identifier.pmid40732265
dc.identifier.scopus2-s2.0-105011721480
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/ph18070975
dc.identifier.urihttps://hdl.handle.net/11616/108491
dc.identifier.volume18
dc.identifier.wosWOS:001539978900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofPharmaceuticals
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250329
dc.subjecttyrosine kinase inhibitors
dc.subjectdeep learning
dc.subjectbioactivity modeling
dc.subjectcheminformatics-based drug screening
dc.subjectprecision oncology
dc.titlePrecision Enhanced Bioactivity Prediction of Tyrosine Kinase Inhibitors by Integrating Deep Learning and Molecular Fingerprints Towards Cost-Effective and Targeted Cancer Therapy
dc.typeArticle

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