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

Abstract

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.