Virtual Screening of Pharmaceutical Compounds with hERG Inhibitory Activity (Cardiotoxicity) using Ensemble Learning

TL;DR

This paper develops an ensemble machine learning model combining multiple classifiers to predict cardiotoxicity of drug compounds using 2-D molecular descriptors, aiming to improve in silico screening accuracy.

Contribution

It introduces a novel ensemble classifier integrating Random Forests, SVMs, and neural networks for cardiotoxicity prediction based on molecular structure features.

Findings

Ensemble model outperforms individual classifiers in accuracy.

Max-Voting and Weighted-Average techniques enhance prediction reliability.

Model demonstrates high sensitivity and specificity in cross-validation.

Abstract

In silico prediction of cardiotoxicity with high sensitivity and specificity for potential drug molecules can be of immense value. Hence, building machine learning classification models, based on some features extracted from the molecular structure of drugs, which are capable of efficiently predicting cardiotoxicity is critical. In this paper, we consider the application of various machine learning approaches, and then propose an ensemble classifier for the prediction of molecular activity on a Drug Discovery Hackathon (DDH) (1st reference) dataset. We have used only 2-D descriptors of SMILE notations for our prediction. Our ensemble classification uses 5 classifiers (2 Random Forest Classifiers, 2 Support Vector Machines and a Dense Neural Network) and uses Max-Voting technique and Weighted-Average technique for final decision.