Linear to Neural Networks Regression: QSPR of Drugs via Degree-Distance Indices

TL;DR

This paper explores the use of degree-distance topological indices combined with machine learning models to predict physical properties of drug molecules, aiming to improve drug discovery processes.

Contribution

It introduces an innovative integration of degree-distance indices with various machine learning techniques for molecular property prediction.

Findings

Topological indices effectively predict physicochemical properties.

Nonlinear models outperform linear regression in accuracy.

Vertex-edge weightings enhance predictive performance.

Abstract

This study conducts a Quantitative Structure Property Relationship (QSPR) analysis to explore the correlation between the physical properties of drug molecules and their topological indices using machine learning techniques. While prior studies in drug design have focused on degree-based topological indices, this work analyzes a dataset of 166 drug molecules by computing degree-distance-based topological indices, incorporating vertex-edge weightings with respect to different six atomic properties (atomic number, atomic radius, atomic mass, density, electronegativity, ionization). Both linear models (Linear Regression, Lasso, and Ridge Regression) and nonlinear approaches (Random Forest, XGBoost, and Neural Networks) were employed to predict molecular properties. The results demonstrate the effectiveness of these indices in predicting specific physicochemical properties and underscore the practical relevance of computational methods in molecular property estimation. The study provides an innovative perspective on integrating topological indices with machine learning to enhance predictive accuracy, highlighting their potential application in drug discovery and development processes. This predictive may also explain that establishing a reliable relationship between topological indices and physical properties enables chemists to gain preliminary insights into molecular behavior before conducting experimental analyses, thereby optimizing resource utilization in cheminformatics research.