What question did this study set out to answer?

This research aims to evaluate the efficacy of drugs for cervical cancer using topological indices and machine learning techniques.

March 26, 2026

On Degree‐Based Topological Indices and QSPR Analysis of Some Novel Drugs Used in the Treatment of Cervical Cancer Using Machine Learning With XGBoost and Regression Models

Key Points

This research aims to evaluate the efficacy of drugs for cervical cancer using topological indices and machine learning techniques.
Introduced a QSPR-based approach integrating topological indices with molecular features.
Estimated physicochemical properties such as boiling point, melting point, and enthalpy of vaporization.
Developed a linear regression model alongside an Extreme Gradient Boosting model for prediction.
Conducted comparative analyses between the two models to assess prediction accuracy.
The XGBoost model showed significantly improved prediction accuracy compared to the linear regression model.
Integration of topological indices with molecular features enhanced the estimation of drug properties.
The study emphasizes the role of machine learning in drug discovery for cervical cancer.

Abstract

ABSTRACT Topological indices (TIs), derived from molecular graph theory, serve as valuable tools in the evaluation of drug efficacy for cervical cancer treatment. These indices capture essential structural information that aids in predicting therapeutic potential and assessing toxicity, both of which are critical for the development of safe and effective anticancer agents. This study introduces a Quantitative Structure‐Property Relationship (QSPR)‐based approach that integrates TIs with molecular features to estimate key physicochemical properties, including boiling point (BP), melting point (MP), enthalpy of vaporization (EV), flash point (FP), index of refraction (IR), molar refractivity (MR), molar volume (MV), polarizability (P), surface tension (ST) and polar surface area (PSA). A linear regression (LR) model is constructed using these features and benchmarked against an Extreme Gradient Boosting (XGBoost) model. Comparative analysis reveals that the XGBoost model significantly enhances prediction accuracy over traditional regression methods. The findings highlight the effectiveness of combining machine learning techniques with topological descriptors in enhancing predictive modeling for drug discovery in cervical cancer.

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On Degree‐Based Topological Indices and QSPR Analysis of Some Novel Drugs Used in the Treatment of Cervical Cancer Using Machine Learning With XGBoost and Regression Models

Key Points

Abstract

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