A simplified similarity-based approach for drug-drug interaction prediction.

Autor: Shtar G; Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.; Department of Information Systems, University of Haifa, Haifa, Israel., Solomon A; Department of Information Systems, University of Haifa, Haifa, Israel., Mazuz E; Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel., Rokach L; Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel., Shapira B; Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Nov 09; Vol. 18 (11), pp. e0293629. Date of Electronic Publication: 2023 Nov 09 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0293629
Abstrakt: Drug-drug interactions (DDIs) are a critical component of drug safety surveillance. Laboratory studies aimed at detecting DDIs are typically difficult, expensive, and time-consuming; therefore, developing in-silico methods is critical. Machine learning-based approaches for DDI prediction have been developed; however, in many cases, their ability to achieve high accuracy relies on data only available towards the end of the molecule lifecycle. Here, we propose a simple yet effective similarity-based method for preclinical DDI prediction where only the chemical structure is available. We test the model on new, unseen drugs. To focus on the preclinical problem setting, we conducted a retrospective analysis and tested the models on drugs that were added to a later version of the DrugBank database. We extend an existing method, adjacency matrix factorization with propagation (AMFP), to support unseen molecules by applying a new lookup mechanism to the drugs' chemical structure, lookup adjacency matrix factorization with propagation (LAMFP). We show that using an ensemble of different similarity measures improves the results. We also demonstrate that Chemprop, a message-passing neural network, can be used for DDI prediction. In computational experiments, LAMFP results in high accuracy, with an area under the receiver operating characteristic curve of 0.82 for interactions involving a new drug and an existing drug and for interactions involving only existing drugs. Moreover, LAMFP outperforms state-of-the-art, complex graph neural network DDI prediction methods.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Shtar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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