Interpretable Machine Learning QSAR Models for Classification and Screening of VEGFR-2 Inhibitors in Anticancer Drug Discovery

Authors

  • Teuku Rizky Noviandy Department of Information Systems, Faculty of Engineering, Universitas Abulyatama, Aceh Besar 23372, Indonesia
  • Rinaldi Idroes School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia

DOI:

https://doi.org/10.60084/mp.v3i2.339

Keywords:

VEGFR-2 inhibitors, Machine learning, QSAR modeling, Drug discovery

Abstract

Cancer remains a major global health burden, with angiogenesis playing a central role in tumor growth and progression. Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2) is a key mediator of angiogenesis and an attractive therapeutic target, but existing inhibitors are limited by reduced efficacy, toxicity, and resistance, creating a need for more effective predictive models in drug discovery. In this study, an interpretable machine learning based QSAR approach was developed using a curated dataset of 10,221 VEGFR-2 inhibitors from ChEMBL represented by 164 molecular descriptors. Four algorithms, kNN, AdaBoost, Random Forest, and XGBoost, were compared, and XGBoost achieved the best results with an accuracy of 83.67 percent, sensitivity of 91.38 percent, specificity of 71.73 percent, F1-score of 87.17 percent, and AUC of 0.9009. Model interpretation with LIME identified molecular descriptors related to hydrogen bonding, electrostatics, and lipophilicity as key contributors to activity. These results indicate that interpretable ensemble models can combine strong predictive performance with mechanistic insights, supporting rational design and optimization of novel VEGFR-2 inhibitors for anticancer therapy.

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Published

2025-09-28

How to Cite

Noviandy, T. R., & Idroes, R. (2025). Interpretable Machine Learning QSAR Models for Classification and Screening of VEGFR-2 Inhibitors in Anticancer Drug Discovery. Malacca Pharmaceutics, 3(2), 58–66. https://doi.org/10.60084/mp.v3i2.339

Issue

Vol. 3 No. 2 (2025): September 2025

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Copyright (c) 2025 Teuku Rizky Noviandy, Rinaldi Idroes

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