Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders

作者：Muhammad Waleed Iqbal, Muhammad Shahab, Zakir Ullah, Guojun Zheng, İrfan Anjum, Gamal A. Shazly, Atrsaw Asrat Mengistie, Xinxiao Sun, Qipeng Yuan · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-024-83767-9 · 被引用次数：8 · 研究领域：Synthesis and biological activity、Quinazolinone synthesis and applications、Computational Drug Discovery Methods

Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivity problems of previously discovered inhibitors like dasatinib and bosutinib, we employed an integrated machine learning and structure-based drug repurposing strategy to find novel, targeted, and non-toxic Src kinase inhibitors. Different machine learning models including random forest (RF), k-nearest neighbors (K-NN), decision tree, and support vector machine (SVM), were trained using already available bioactivity data of Src kinase targeting compounds. The performance evaluation of these models demonstrated SVM as the best model, which was further utilized to shortlist 51 highly potent compounds by screening an FDA-approved library of 1040 drugs. Molecular docking and molecular dynamic simulation were subsequently employed to evaluate the binding affinity and stability of the proposed compounds. Orlistat, acarbose and afatinib were identified as the potent leads, demonstrating stable conformations and stronger interactions, validated by root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (RoG), and hydrogen bond analyses. Molecular Mechanics/Generalized Born Surface Area (MMGBSA) analysis validated their binding affinities by providing comparably lower binding free energies for orlistat (- 3...