MatchMaker: a deep learning framework for drug synergy prediction

Kuru, Halil Ibrahim and Taştan, Öznur and Cicek, Ercument (2022) MatchMaker: a deep learning framework for drug synergy prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 19 (4). pp. 2334-2344. ISSN 1545-5963 (Print) 1557-9964 (Online)

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Drug combination therapies have been a viable strategy for the treatment of complex diseases such as cancer due to increased efficacy and reduced side effects. However, experimentally validating all possible combinations for synergistic interaction even with high-throughout screens is intractable due to vast combinatorial search space. Computational techniques can reduce the number of combinations to be evaluated experimentally by prioritizing promising candidates. We present MatchMaker that predicts drug synergy scores using drug chemical structure information and gene expression profiles of cell lines in a deep learning framework. For the first time, our model utilizes the largest known drug combination dataset to date, DrugComb. We compare the performance of MatchMaker with the state-of-the-art models and observe up to 15% correlation and 33% mean squared error (MSE) improvements over the next best method. We investigate the cell types and drug pairs that are relatively harder to predict and present novel candidate pairs. MatchMaker is built and available at
Item Type: Article
Uncontrolled Keywords: Cancer Cell Lines; Chemical Features; Chemicals; Computer architecture; Deep Learning; Drug Synergy; Drugs; Gene expression; Microprocessors; Prediction algorithms; Predictive models
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Öznur Taştan
Date Deposited: 01 Sep 2022 22:27
Last Modified: 01 Sep 2022 22:27

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