MatchMaker: A deep learning framework for drug synergy prediction

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buir.contributor.authorKuru, Halil İbrahim
buir.contributor.authorÇiçek, Ercüment
buir.contributor.orcidKuru, Halil İbrahim|0000-0003-4356-8846
buir.contributor.orcidÇiçek, Ercüment|0000-0001-8613-6619
dc.contributor.authorKuru, Halil İbrahim
dc.contributor.authorTaştan, Ö.
dc.contributor.authorÇiçek, Ercüment
dc.date.accessioned2022-01-28T06:28:48Z
dc.date.available2022-01-28T06:28:48Z
dc.date.issued2021-06-04
dc.departmentDepartment of Computer Engineeringen_US
dc.description( Early Access )en_US
dc.description.abstractDrug 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 https://github.com/tastanlab/matchmakeren_US
dc.identifier.doi10.1109/TCBB.2021.3086702en_US
dc.identifier.eissn1557-9964
dc.identifier.issn1545-5963
dc.identifier.urihttp://hdl.handle.net/11693/76852
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://doi.org/10.1109/TCBB.2021.3086702en_US
dc.source.titleIEEE/ACM Transactions on Computational Biology and Bioinformaticsen_US
dc.subjectDeep learningen_US
dc.subjectDrug synergyen_US
dc.subjectChemical featuresen_US
dc.subjectCancer cell linesen_US
dc.titleMatchMaker: A deep learning framework for drug synergy predictionen_US
dc.typeArticleen_US

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