Author:
Deepa Kumari
Affiliation:
CSIS Department, BITS Pilani, Hyderabad Campus, Shameerpet, Hyderabad, India
Keyword(s):
Drug-Drug Interaction, Side-Effects, Similarity Measures, Machine Learning.
Abstract:
Drug-Drug interaction (DDI) can lead to adverse reactions by decreasing the absorption rate in a patient body. The existing literature has limited focus on the impact of various similarity measures on DDI effects. This paper analyzes seven drug features (chemical substructures, targets, transporters, enzymes, side-effects, offsides, and carriers) obtained from Drugbank, Sider, TWOSIDES, and OFFSIDE databases to analyze DDI. This research examines five Machine Learning models (Logistic Regression, Random Forest, Decision Tree, KNN, ANN) on 16 different similarity measures to observe the performance of predicting samples through accuracy and AUC-curve analysis. The Jaccard similarity is chosen for further DDI prediction as it gives the best similarity score. The feature selection process (using Chi-Square) further reduces the time and space complexity. It compares combinations of every selected feature (chemical substructures, side-effects, offsides, enzymes) on Logistic Regression, Ra
ndom Forest, and XGB classifiers. The results show that the Random Forest Classifier predicts DDI with the best accuracy of 72%. It also uniquely categorizes the severity level of side effects (minor, moderate, and major) due to DDI events through multi-class classification. Thus, it gives a better clinical significance to fast-track the clinical trials.
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