Comparative Analysis of Linear Regression, Decision Tree, and Gradient Boosting Models for Predicting Drug Corrosion Inhibition Efficiency Using QSAR Descriptors

Abstract

Corrosion in industrial environments poses significant economic and safety challenges, necessitating the development of effective inhibitors. Organic compounds, particularly pharmaceuticals, have emerged as promising corrosion inhibitors due to their efficiency and environmental benefits. However, predicting these compounds' corrosion inhibition efficiency (CIE) remains complex and requires advanced computational methods. This study investigates the predictive capabilities of three machine learning (ML) models, namely linear regression, decision tree, and gradient boosting regression, using Quantitative Structure-Activity Relationship (QSAR) descriptors. A dataset containing 14 QSAR descriptors was compiled from experimental studies on various pharmaceutical-based inhibitors. The dataset was divided into training (90%) and testing (10%) subsets to evaluate model performance. The research follows the CRISP-DM methodology, a systematic framework that includes data preparation, model training, and evaluation. Key performance metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), were used to assess model accuracy. Among the models, Gradient Boosting Regression achieved the most promising results, with the lowest MSE (21.52) and the highest R² (0.21), reflecting its ability to capture non-linear relationships in the data. Despite the relatively modest R², this model demonstrates the potential for improving computational approaches to corrosion inhibition prediction. This study highlights the value of machine learning in optimizing the selection of corrosion inhibitors, potentially reducing the reliance on extensive laboratory testing and accelerating the discovery of efficient, eco-friendly solutions for industrial applications.