DrugPred: An ensemble learning model based on ESM2 for predicting potential druggable proteins

Abstract

The Human Genome Project has generated abundant data for a long time, but transforming this data into practical and usable drug or drug target products remains challenging. This study proposed an ensemble learning model, called DrugPred, to achieve prediction of drug targets using evolutionary scale modeling (ESM2) and amino acid composition (AAC) as features. ESM2 utilized deep learning technology to study the sequence-structure-function relationship of protein sequences, extracting highly abstract features of proteins. AAC translated protein sequences into amino acid percentages, reflecting the composition of amino acids in proteins. The integration of two features constituted a multidimensional and diverse feature space, enabling the model to perform well in predicting drug targets. We input the fused features into four machine learning algorithms for separate training and generated the prediction probabilities, then input them into a support vector machine for voting decisions. This ensemble learning overcame the bias of a single algorithm model in information learning and improved the stability and accuracy of the model. After comprehensive evaluation, the model achieved an accuracy of 0.9691 with an area under the receiver operating characteristic curve (AUC) value of 0.9868. We also used t-distributed Stochastic Neighbor Embedding (t-SNE) and SHapley Additive exPlanations (SHAP) techniques to explore the interpretability of the DrugPred model. This study provided a fresh perspective and method for identifying drug targets, offering robust support for future drug development. We have developed and made publicly accessible a web server based on the DrugPred model. The web server is accessible at http://drugpred.lin-group.cn/.