A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy. To address this, we applied machine learning techniques, specifically linear regression models combined with K-fold cross-validation, to predict critical properties such as Density, Boiling Point, Flash Point, Bioconcentration Factor (BCF), Organic Carbon Partition Coefficient (KOC), Polarizability, and Molar Volume. The models were developed using data from ten anti-arrhythmic drugs ([Formula: see text] to [Formula: see text]). We evaluated the models based on performance metrics such as R and [Formula: see text] and obtained significant results. Most accurate predictions are obtained for polarizability from models with H(G) and [Formula: see text].