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Article

Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects

KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 9000 Gent, Belgium
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Academic Editors: Jung-woo Chae and In-hwan Baek
Pharmaceuticals 2021, 14(5), 429; https://0-doi-org.brum.beds.ac.uk/10.3390/ph14050429
Received: 22 March 2021 / Revised: 27 April 2021 / Accepted: 28 April 2021 / Published: 2 May 2021
Combining drugs, a phenomenon often referred to as polypharmacy, can induce additional adverse effects. The identification of adverse combinations is a key task in pharmacovigilance. In this context, in silico approaches based on machine learning are promising as they can learn from a limited number of combinations to predict for all. In this work, we identify various subtasks in predicting effects caused by drug–drug interaction. Predicting drug–drug interaction effects for drugs that already exist is very different from predicting outcomes for newly developed drugs, commonly called a cold-start problem. We propose suitable validation schemes for the different subtasks that emerge. These validation schemes are critical to correctly assess the performance. We develop a new model that obtains AUC-ROC =0.843 for the hardest cold-start task up to AUC-ROC =0.957 for the easiest one on the benchmark dataset of Zitnik et al. Finally, we illustrate how our predictions can be used to improve post-market surveillance systems or detect drug–drug interaction effects earlier during drug development. View Full-Text
Keywords: polypharmacy; drug–drug interaction; prediction; cross-validation; machine learning; cold-start problems polypharmacy; drug–drug interaction; prediction; cross-validation; machine learning; cold-start problems
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MDPI and ACS Style

Dewulf, P.; Stock, M.; De Baets, B. Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects. Pharmaceuticals 2021, 14, 429. https://0-doi-org.brum.beds.ac.uk/10.3390/ph14050429

AMA Style

Dewulf P, Stock M, De Baets B. Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects. Pharmaceuticals. 2021; 14(5):429. https://0-doi-org.brum.beds.ac.uk/10.3390/ph14050429

Chicago/Turabian Style

Dewulf, Pieter, Michiel Stock, and Bernard De Baets. 2021. "Cold-Start Problems in Data-Driven Prediction of Drug–Drug Interaction Effects" Pharmaceuticals 14, no. 5: 429. https://0-doi-org.brum.beds.ac.uk/10.3390/ph14050429

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