Multidrug representation learning based on pretraining model and molecular graph for drug interaction and combination prediction

Author:

Ren Shujie1,Yu Liang1ORCID,Gao Lin1ORCID

Affiliation:

1. School of Computer Science and Technology, Xidian University , Xi’an, Shaanxi 710071, China

Abstract

Abstract Motivation Approaches for the diagnosis and treatment of diseases often adopt the multidrug therapy method because it can increase the efficacy or reduce the toxic side effects of drugs. Using different drugs simultaneously may trigger unexpected pharmacological effects. Therefore, efficient identification of drug interactions is essential for the treatment of complex diseases. Currently proposed calculation methods are often limited by the collection of redundant drug features, a small amount of labeled data and low model generalization capabilities. Meanwhile, there is also a lack of unique methods for multidrug representation learning, which makes it more difficult to take full advantage of the originally scarce data. Results Inspired by graph models and pretraining models, we integrated a large amount of unlabeled drug molecular graph information and target information, then designed a pretraining framework, MGP-DR (Molecular Graph Pretraining for Drug Representation), specifically for drug pair representation learning. The model uses self-supervised learning strategies to mine the contextual information within and between drug molecules to predict drug–drug interactions and drug combinations. The results achieved promising performance across multiple metrics compared with other state-of-the-art methods. Our MGP-DR model can be used to provide a reliable candidate set for the combined use of multiple drugs. Availability and implementation Code of the model, datasets and results can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MGP-DR). Supplementary information Supplementary data are available at Bioinformatics online.

Funder

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference55 articles.

1. Trends in the market for antihypertensive drugs;Ali;Nat. Rev. Drug Discov,2017

2. Prediction of effective drug combinations by an improved naive Bayesian algorithm;Bai;Int. J. Mol. Sci,2018

3. What is synergy?;Berenbaum;Pharmacol. Rev,1989

4. Derivatives of pyrimidine, phthalimide and anthranilic acid as inhibitors of human hydroxysteroid dehydrogenase AKR1C1;Brozic;Chem. Biol. Interact,2009

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