An In Silico Method for Predicting Drug Synergy Based on Multitask Learning


To make better use of all kinds of knowledge to predict drug synergy, it is crucial to successfully establish a drug synergy prediction model and leverage the reconstruction of sparse known drug targets. Therefore, we present an in silico method that predicts the synergy scores of drug pairs based on multitask learning (DSML) that could fuse drug targets, protein–protein interactions, anatomical therapeutic chemical codes, a priori knowledge of drug combinations. To simultaneously reconstruct drug–target protein interactions and synergistic drug combinations, DSML benefits indirectly from the associations with relation through proteins. In cross-validation experiments, DSML improved the ability to predict drug synergy. Moreover, the reconstruction of drug–target interactions and the incorporation of multisource knowledge significantly improved drug combination predictions by a large margin. The potential drug combinations predicted by DSML demonstrate its ability to predict drug synergy.

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This work has been supported by the National Natural Science Foundation of China (Grant Nos. 62002154, 61873089, and 61502221), Hunan Provincial Natural Science Foundation of China (Grant No. 2019JJ50520), Research Foundation of Hunan Educational Committee (Grant No. 20C1579), and Scientific Research Startup Foundation of University of South China (Grant No. 190XQD096).

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Correspondence to Pingjian Ding.

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Chen, X., Luo, L., Shen, C. et al. An In Silico Method for Predicting Drug Synergy Based on Multitask Learning. Interdiscip Sci Comput Life Sci (2021).

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  • Drug synergy
  • Multitask learning
  • Drug–target interaction
  • In silico technology