Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.

Autor: Gopal D; Department of Pharmacology, Sri Ramachandra Faculty of Pharmacy, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, 600116, India., Muthuraj R; Department of Pharmacology, Sri Ramachandra Faculty of Pharmacy, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, 600116, India., Balaya RDA; Centre for Integrative Omics Data Science, Yenepoya (Deemed to be University), Mangalore, Karnataka, India., Kanekar S; Centre for Integrative Omics Data Science, Yenepoya (Deemed to be University), Mangalore, Karnataka, India., Ahmed I; Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, India.; Division of Computer Aided Drug Design, Department of Pharmaceutical Chemistry, R. C. Patel Institute of Pharmaceutical Education and Research, Shirpur, India., Chandrasekaran J; Department of Pharmacology, Sri Ramachandra Faculty of Pharmacy, Sri Ramachandra Institute of Higher Education and Research (Deemed to be University), Chennai, 600116, India. jaikanthjai@gmail.com.
Jazyk: angličtina
Zdroj: Molecular diversity [Mol Divers] 2024 Dec; Vol. 28 (6), pp. 4343-4359. Date of Electronic Publication: 2024 Feb 28.
DOI: 10.1007/s11030-024-10819-7
Abstrakt: In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrating advanced cheminformatics, machine learning, and molecular simulation techniques. Our approach involved analyzing key interactions for FYN inhibition using established multi-kinase inhibitors such as Staurosporine, Dasatinib, and Saracatinib. We utilized ECFP4 circular fingerprints and the t-SNE machine learning algorithm to compare molecular similarities between FDA-approved drugs and known clinical trial inhibitors. This led to the identification of potential inhibitors, including Afatinib, Copanlisib, and Vandetanib. Using the DrugSpaceX platform, we generated a vast library of 72,196 analogues from these leads, which after careful refinement, resulted in 6008 promising candidates. Subsequent clustering identified 48 analogues with significant similarity to known inhibitors. Notably, two candidates derived from Vandetanib, DE27123047 and DE27123035, exhibited strong docking affinities and stable binding in molecular dynamics simulations. These candidates showed high potential as effective FYN kinase inhibitors, as evidenced by MMGBSA calculations and MCE-18 scores exceeding 50. Additionally, our exploration into their molecular architecture revealed potential modification sites on the quinazolin-4-amine scaffold, suggesting opportunities for strategic alterations to enhance activity and optimize ADME properties. Our research is a pioneering effort in drug discovery, unveiling novel candidates for FYN inhibition and demonstrating the efficacy of a multi-layered computational strategy. The molecular insights gained provide a pathway for strategic refinements and future experimental validations, setting a new direction in targeted drug development against diseases involving FYN kinase.
Competing Interests: Declarations. Competing interests: The authors declare no competing interests.
(© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)
Databáze: MEDLINE
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