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Molecular docking aided machine learning for the identification of potential VEGFR inhibitors against renal cell carcinoma

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Abstract

Renal cell carcinoma is a highly vascular tumor associated with vascular endothelial growth factor (VEGF) expression. The Vascular Endothelial Growth Factor -2 (VEGF-2) and its receptor was identified as a potential anti-cancer target, and it plays a crucial role in physiology as well as pathology. Inhibition of angiogenesis via blocking the signaling pathway is considered an attractive target. In the present study, 150 FDA-approved drugs have been screened using the concept of drug repurposing against VEGFR-2 by employing the molecular docking, molecular dynamics, grou** data with Machine Learning algorithms, and density functional theory (DFT) approaches. The identified compounds such as Pazopanib, Atogepant, Drosperinone, Revefenacin and Zanubrutinib shown the binding energy − 7.0 to − 9.5 kcal/mol against VEGF receptor in the molecular docking studies and have been observed as stable in the molecular dynamic simulations performed for the period of 500 ns. The MM/GBSA analysis shows that the value ranging from − 44.816 to − 82.582 kcal/mol. Harnessing the machine learning approaches revealed that clustering with K = 10 exhibits the relevance through high binding energy and satisfactory logP values, setting them apart from compounds in distinct clusters. Therefore, the identified compounds are found to be potential to inhibit the VEGFR-2 and the present study will be a benchmark to validate the compounds experimentally.

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Acknowledgements

The authors VSD, VSJ and SS acknowledge VFSTR for providing Computational support to carry out this work.

Funding

Nil.

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Authors and Affiliations

  1. Department of Chemistry, School of Applied Sciences & Humanities, Vignan’s Foundation for Science, Technology and Research, Vadlamudi, Andhra Pradesh, India

    Vidya Sagar Jerra, Shaik Shareef & Srinivasadesikan Venkatesan

  2. Department of Pharmacology, Physiology & Biophysics, Boston University Chobanian & Avedisian School of Medicine, 700 Albany Street, Boston, MA, 02118, USA

    Balajee Ramachandran

  3. Machine Learning Department, Modulate Inc, 212 Elm Street, Suite 300, Somerville, MA, 02144, USA

    Angel Carrillo-Bermejo

  4. Centre for Bioinformatics, Department of Biochemistry, Karpagam Academy of Higher Education, Coimbatore, TamilNadu, India

    Rajamanikandan Sundararaj

  5. School of Pharmaceutical Science and Technology, Tian** University, Tian**, 300072, China

    Rajamanikandan Sundararaj

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  1. Vidya Sagar Jerra
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  2. Balajee Ramachandran
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  5. Rajamanikandan Sundararaj
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  6. Srinivasadesikan Venkatesan
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Contributions

VSJ, VSD: Conceptualization, design of work. VSJ, BR, VSD: Data curation, visualization, validation, writing—original draft. VSJ, BR, SS, VSD: Molecular Docking. BR, RM: Molecular Dynamics Simulations. ACB, BR: Machine Learning concepts. Supervision and Investigation: VSD, BR. The final version of the manuscript submitted was approved by all the authors.

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Correspondence to Srinivasadesikan Venkatesan.

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Jerra, V.S., Ramachandran, B., Shareef, S. et al. Molecular docking aided machine learning for the identification of potential VEGFR inhibitors against renal cell carcinoma. Med Oncol 41, 198 (2024). https://doi.org/10.1007/s12032-024-02419-0

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