{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T21:23:32Z","timestamp":1774560212249,"version":"3.50.1"},"reference-count":83,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic functions. Notwithstanding the success of current models, understanding the mechanism of drug synergy from a chemical\u2013gene\u2013tissue interaction perspective lacks study, hindering current algorithms from drug mechanism study. Here, we proposed a deep neural network model termed DTSyn (Dual Transformer encoder model for drug pair Synergy prediction) based on a multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer encoder to capture chemical substructure\u2013gene and gene\u2013gene associations and a coarse-granularity transformer encoder to extract chemical\u2013chemical and chemical\u2013cell line interactions. DTSyn achieved the highest receiver operating characteristic area under the curve of 0.73, 0.78. 0.82 and 0.81 on four different cross-validation tasks, outperforming all competing methods. Further, DTSyn achieved the best True Positive Rate (TPR) over five independent data sets. The ablation study showed that both transformer encoder blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, representing the potential mechanisms of drug action. By leveraging the attention mechanism and pretrained gene embeddings, DTSyn shows improved interpretability ability. Thus, we envision our model as a valuable tool to prioritize synergistic drug pairs with chemical and cell line gene expression profile.<\/jats:p>","DOI":"10.1093\/bib\/bbac302","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T22:10:38Z","timestamp":1659391838000},"source":"Crossref","is-referenced-by-count":58,"title":["DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations"],"prefix":"10.1093","volume":"23","author":[{"given":"Jing","family":"Hu","sequence":"first","affiliation":[{"name":"Baidu, Inc. , 701, Na Xian Road, 201210, Shanghai , China"}]},{"given":"Jie","family":"Gao","sequence":"additional","affiliation":[{"name":"Baidu, Inc. , 701, Na Xian Road, 201210, Shanghai , China"}]},{"given":"Xiaomin","family":"Fang","sequence":"additional","affiliation":[{"name":"Baidu, Inc. , Xue Fu Road, 518000, Shenzhen , China"}]},{"given":"Zijing","family":"Liu","sequence":"additional","affiliation":[{"name":"Baidu, Inc. , Xue Fu Road, 518000, Shenzhen , China"}]},{"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"Baidu, Inc. , Xue Fu Road, 518000, Shenzhen , China"}]},{"given":"Weili","family":"Huang","sequence":"additional","affiliation":[{"name":"HWL Consulting LLC , 3328 Antigua Dr, 97408, Oregon, US"}]},{"given":"Hua","family":"Wu","sequence":"additional","affiliation":[{"name":"Baidu, Inc. , No. 10 Shangdi 10th Street, 100085, Beijing , China"}]},{"given":"Guodong","family":"Zhao","sequence":"additional","affiliation":[{"name":"Baidu, Inc. , 701, Na Xian Road, 201210, Shanghai , China"}]}],"member":"286","published-online":{"date-parts":[[2022,8,1]]},"reference":[{"issue":"3","key":"2022092013213608500_ref1","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1124\/pr.58.3.10","article-title":"Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination 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