A Study on Drug Similarity Measures for Predicting Drug-Drug Interactions and Severity Using Machine Learning Techniques

Deepa Kumari

2024

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, Random 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.

Paper Citation

in Harvard Style

Kumari D. (2024). A Study on Drug Similarity Measures for Predicting Drug-Drug Interactions and Severity Using Machine Learning Techniques. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 72-79. DOI: 10.5220/0012298300003636

in Bibtex Style

@conference{icaart24,
author={Deepa Kumari},
title={A Study on Drug Similarity Measures for Predicting Drug-Drug Interactions and Severity Using Machine Learning Techniques},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={72-79},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012298300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - A Study on Drug Similarity Measures for Predicting Drug-Drug Interactions and Severity Using Machine Learning Techniques
SN - 978-989-758-680-4
AU - Kumari D.
PY - 2024
SP - 72
EP - 79
DO - 10.5220/0012298300003636
PB - SciTePress