In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method.

Wang Wang,Xiao Xiao,Chen Chen

doi:10.3390/ijms20174106

Abstract

Drug-induced liver injury (DILI) is a major factor in the development of drugs and the safety of drugs. If the DILI cannot be effectively predicted during the development of the drug, it will cause the drug to be withdrawn from markets. Therefore, DILI is crucial at the early stages of drug research. This work presents a 2-class ensemble classifier model for predicting DILI, with 2D molecular descriptors and fingerprints on a dataset of 450 compounds. The purpose of our study is to investigate which are the key molecular fingerprints that may cause DILI risk, and then to obtain a reliable ensemble model to predict DILI risk with these key factors. Experimental results suggested that 8 molecular fingerprints are very critical for predicting DILI, and also obtained the best ratio of molecular fingerprints to molecular descriptors. The result of the 5-fold cross-validation of the ensemble vote classifier method obtain an accuracy of 77.25%, and the accuracy of the test set was 81.67%. This model could be used for drug-induced liver injury prediction.

Highlights

New drug development was affected by many factors [1], which made 90% potential drugs failing in the clinical trial phase [2]
On the independent test set the model achieved an accuracy of 81.67%, an SE of 64.55%, an SP of 96.15%, an area under the curve (AUC) of 80.35%, this result showed that our integrated model can effectively and Experimental can objectively reflect the ability of the model to predict hepatotoxicity of compound
On the independent test set the model achieved an accuracy of 81.67%, an SE of 64.55%, an SP of 96.15%, an AUC of 80.35%, this result showed that our integrated model can effectively and stably predict the liver damage of drugs

Summary

Introduction

New drug development was affected by many factors [1], which made 90% potential drugs failing in the clinical trial phase [2]. Previous studies showed that drug efficacy and toxicity are the two main causes of drug development failure [3], in which liver damage is the most important cause [4]. In the practice of clinical medication, prevention of drug-induced liver injury is one of the most important issues [5]. Many works have presented a number of methods to assess the risk of drug-induced liver injury, they are time-consuming and labor-intensive, and always yielded unsatisfactory results [6].

Objectives

Methods

Findings

Discussion

Conclusion