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.