Data-driven QSPR analysis of anti-cancer drugs using python-based topological techniques

Kara Şen Y., Sağlam Özkan Y., Bektaş A. B.

Journal of the Indian Chemical Society, cilt.102, sa.10, 2025 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 102 Sayı: 10
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1016/j.jics.2025.101993
  • Dergi Adı: Journal of the Indian Chemical Society
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Analytical Abstracts, Chemical Abstracts Core
  • Anahtar Kelimeler: Anti-cancer drugs, Chemical and physical properties, Python algorithm, QSPR models, Topological indices
  • Bursa Uludağ Üniversitesi Adresli: Evet

Özet

This study proposes a machine learning-based Quantitative Structure–Property Relationship (QSPR) model for predicting the physicochemical properties of anti-cancer drugs by utilizing topological descriptors. The development of anti-cancer drugs poses a significant challenge due to the intricate relationship between drug efficacy and chemical structure. The present study utilizes machine learning regression models in combination with leave-one-out cross-validation (LOOCV) to predict a range of physicochemical properties, including boiling point, enthalpy, molar refractivity, complexity, molecular weight, heavy atom count, flash point, and polarizability. The models are developed using data from thirty anti-cancer drugs and assessed using performance metrics such as the correlation coefficient (R), the coefficient of determination (R2) and root mean square error (RMSE). The findings are encouraging, with a thorough comparison made between the observed values and the values predicted by the QSPR models.