A Graph-Based Machine Learning Framework for Predicting Physicochemical Properties of Antiviral Drugs via Topological Indices.

This research develops a two-stage machine learning framework for predicting physicochemical properties of antiviral drugs through quantitative structure-property relationship (QSPR) modeling. We analyzed a diverse data set of 59 antiviral compounds, leveraging SMILES-based molecular descriptors to predict the first stage six topological indices: First Zagreb, Second Zagreb, ABC, Randic, Harmonic, and Forgotten. The models that provided predictions closest to the actual topological index values were employed in the second stage to estimate six physicochemical properties: molar refractivity, polar surface area, polarizability, molar volume, molecular weight, and complexity. This framework showed high predictive performance, achieving coefficient of determination of 0.9950 for molecular weight and 0.9891 for polarizability. Correlation analysis demonstrated strong relations between the topological indices and molecular properties, with the Randic index having the highest at 0.9969. A comparison with previous studies was conducted to evaluate the effectiveness of the proposed framework. This integrated pipeline provides an accurate, interpretable, and scalable framework for QSPR-based prediction of physicochemical properties in antiviral drugs.