Literature data-based de novo candidates for drug repurposing

Background: Drug repurposing offers a promising strategy for drug discovery. Drug repurposing involves identifying new therapeutic indications for existing, marketed drugs, thereby reducing the risks, costs, and time typically required for drug development. Various methods exist for drug repurposing, including high-throughput screening of drug compound libraries, computation in silico approaches, literature-based methods, etc. Currently, numerous methods utilize literature for data mining in drug repositioning; however, relatively few approaches leverage literature citation networks for this purpose. Results: We identified 19,553 potential drug pairs for repurposing by analyzing biomedical literature data through the Jaccard coefficient. Our results demonstrated that the literature-based Jaccard coefficient was the most effective similarity metric for identifying drug repurposing opportunities. To refine our selection process, we applied a threshold defined by the upper th quantile value of the Jaccard coefficient, enabling us to prioritize promising de novo drug repurposing candidates. Among the identified drug pairs, we found several with strong potential for repurposing, including combinations such as adapalene and bexarotene, guanabenz and tizanidine, alvimopan and methylnaltrexone, etc. Conclusion: We created a validation set consisting of both true positives and true negatives for drug pairs using the repoDB dataset, a widely recognized resource for drug repurposing. To evaluate the performance of various similarity metrics for drug pairs, we compared their effectiveness based on AUC, F₁ score, and AUCPR using the validation set.