Predicting Drug Target Interaction by Integrating Drug Fingerprint and Drug Side Effect Using Machine Learning

Abstract

Drug discovery is an important step before drug development. Drug discovery is the process of identifying, testing a drug before medical use. Drugs are used to cure diseases by interacting with the target, which is the protein in the human cells. Many resources are wasted (cost and time) on lab experiments to discover drugs and its application. Yet machine learning enhanced the process of drug discovery and the prediction of drug-target interaction, which helped in predicting new drugs and finding more applications for old drugs. Predicting drug-target interaction starting by studying the nature of drugs and its properties. Most of the datasets existing are drugs, targets and their interactions datasets. We compiled our dataset to include side effect as drug feature. The dataset contains 400 drugs, 794 targets and 3990 side effects. In this study, a machine-learning model is implemented using three different classifiers: Decision Tree, Random Forest (RF) and K-Nearest Neighbors (K-NN) for classification. Drug fingerprint and side effect were used as input features to train our model. Three different experiments were conducted using fingerprint, side effect and both fingerprint and side effect. Results showed improvement in prediction when integrating both drug fingerprint and side effect. K-NN scored best results in the three experiment with an average accuracy of 94.69%.