Drugs inhibition prediction in P-gp enzyme: a comparative study of machine learning and graph neural network

Drug metabolism is a complex and highly regulated process that involves the safe breakdown and elimination of drugs from the body through chemical reactions. The P-glycoprotein (P-gp) plays a key role in drug metabolism, and interfere of drugs with its transport function leads to drug toxicity. Therefore, predicting P-gp inhibition is crucial to avoid adverse drug effects. To address this, machine learning and deep learning models offer a powerful approach to accurately predict the P-gp inhibition. In this study, we have utilized a publicly available P-gp dataset to develop classification models using various machine learning algorithms (SVM, RFC, HistGradient Boosting, AdaBoost) and graph neural networks. The dataset was transformed into molecular descriptors and graph feature vectors to explore the chemical space of metabolic enzymes. Our experimental results demonstrate that machine learning models outperform deep learning models in terms of accuracy and efficiency for independent datasets. Among all models, SVM exhibited superior predictive capabilities for the P-gp data set with an accuracy of 0.95 on independent datasets. Furthermore, the analysis of the importance of the characteristics of the best model highlighted the significant contributions of specific descriptors to the data set. Finally, our model outperformed previous studies when evaluated on an external dataset, emphasizing the efficacy of molecular features in providing more precise explanations of compound properties and biological activity.