Predicting Drugs Metabolized by Cytochrome Enzymes

Abstract

In the era of rapid technological evolution, embracing the strength of machine learning, deep learning, and other computational approaches merged with biological and biochemical domains has enhanced multiple medical applications. Drug discovery is one of the fields that have been rapidly developing. This project will focus on predicting drugs metabolized by Cytochrome enzymes. Therefore, using machine learning, deep learning, and pre-trained approaches would illustrate the strength of recent computational methods used in the medical field; consequently, it will reduce the limitations of traditional techniques in drug development by limiting the cost and time during clinical trials. This study will prepare the dataset to extract descriptors and build Logistic Regression, Support Vector Machine, Random Forest, Recurrent Neural Networks, ChemBERTa, and Galactica, along with parameter tunning to evaluate the best model through ROC Curve, Confusion Matrix, and F1-score. This proposed study shows that random forest outperformed other models with a 0.907 f1-score.