Motivation Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug’s mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications. Results In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in natureand found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet’s ability to use mechanistic insight to identify a drug’s mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug-target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method’s utility in providing mechanistic insight into repurposing predictions it provides. Availability The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet Supplementary information Supplementary data are available at Bioinformatics online.

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Motivation: Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug's mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications. Results: In this work, we examine the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrate 18 data sources to produce a knowledge graph, MechRepoNet capable of capturing the information in these paths. Applying the Rephetio repurposing algorithm to MechRepoNet, using only a subset of relationships known to be mechanistic in nature, we find adequate predictive ability on an evaluation set with AUROCvalue of 0.83. The resulting repurposing model allows us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We find that DrugMechDB paths, when present in the network are rated highly among potential mechanisms. We then demonstrate MechRepoNet's ability to use mechanistic insight to identify a drug's mechanistic target, with a mean reciprocal rank of .525 on a test set of known drug-target interactions. Finally, we walk through a repurposing example of the anti-cancer drug imatinib for use in the treatment of asthma, to demonstrate this method's utility in providing mechanistic insight into repurposing predictions it provides.

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