  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.
