{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T19:07:33Z","timestamp":1774379253114,"version":"3.50.1"},"reference-count":63,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:00:00Z","timestamp":1689724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug\u2013target interaction, protein\u2013protein interaction, and cell\u2013target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug\u2013target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug\u2013protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug\u2013protein interaction significantly improves the prediction of synergistic drug combinations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The source code and data are available at https:\/\/github.com\/fatemeh-rafiei\/DeepTraSynergy.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad438","type":"journal-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T23:50:01Z","timestamp":1689810601000},"source":"Crossref","is-referenced-by-count":81,"title":["DeepTraSynergy: drug combinations using multimodal deep learning with transformers"],"prefix":"10.1093","volume":"39","author":[{"given":"Fatemeh","family":"Rafiei","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences , Tehran 1417613151, Iran"}]},{"given":"Hojjat","family":"Zeraati","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences , Tehran 1417613151, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2135-8864","authenticated-orcid":false,"given":"Karim","family":"Abbasi","sequence":"additional","affiliation":[{"name":"Laboratory of System Biology, Bioinformatics & Artificial Intelligent in Medicine (LBB&AI), Faculty of Mathematics and Computer Science, Kharazmi University , Tehran 1571914911, Iran"}]},{"given":"Jahan B","family":"Ghasemi","sequence":"additional","affiliation":[{"name":"Chemistry Department, Faculty of Chemistry, School of Sciences, University of Tehran , Tehran 1417614411, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1928-5424","authenticated-orcid":false,"given":"Mahboubeh","family":"Parsaeian","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences , Tehran 1417613151, Iran"},{"name":"Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London , London W21PG, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0659-5183","authenticated-orcid":false,"given":"Ali","family":"Masoudi-Nejad","sequence":"additional","affiliation":[{"name":"Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran , Tehran 1417614411, Iran"}]}],"member":"286","published-online":{"date-parts":[[2023,7,19]]},"reference":[{"key":"2023080305202175100_btad438-B1","doi-asserted-by":"crossref","first-page":"4528","DOI":"10.1021\/acs.jcim.9b00626","article-title":"Deep transferable compound representation across domains and tasks for low data drug discovery","volume":"59","author":"Abbasi","year":"2019","journal-title":"J Chem Inf 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