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 nature and 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 AND IMPLEMENTATION: The Python code to reproduce the entirety of this analysis is available at: https://github.com/SuLab/MechRepoNet (archived at https://doi.org/10.5281/zenodo.6456335). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Limbal stem cell deficiency (LSCD) is a progressive corneal disease that renders the corneal epithelium unable to repair itself, which can lead to the eventual loss of vision. Advances in technology have allowed for the growth of limbal stem cells ex-vivo for the purposes of transplantation. One method used to evaluate the quality of these cultivated cells is cell density, which is typically calculated manually by experts, which is time-consuming and has high inter-rater variability. The goal of this project was to create a tool that automatically calculates cell density from digital images of the cultured cells. Results were compared against annotations from four experts with varying levels of experience. Cell counts had high correlation with expert annotations (r=0.64, p<0.01). When compared to human annotators with lower clinical experience, the algorithm achieved significantly better agreement with highly experienced annotators (r=0.75 vs r=0.19, p<0.01). These results suggest that the automated tool can provide meaningful cell density counts, which can potentially improve annotation consistency and reduce time required for evaluating LSCD cell cultures.