A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications


  • Shivashankar Subramanian The University of Melbourne
  • Ioana Baldini IBM Research
  • Sushma Ravichandran IBM Research
  • Dmitriy A. Katz-Rogozhnikov IBM Research
  • Karthikeyan Natesan Ramamurthy IBM Research
  • Prasanna Sattigeri IBM Research
  • Kush R. Varshney IBM Research
  • Annmarie Wang Massachusetts Institute of Technology
  • Pradeep Mangalath Harvard University
  • Laura B. Kleiman Cures Within Reach for Cancer




More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of these drugs represents a major opportunity to rapidly improve outcomes for cancer patients and reduce healthcare costs. In many cases, there is already evidence of efficacy for cancer, but trying to manually extract such evidence from the scientific literature is intractable. In this emerging applications paper, we introduce a system to automate non-cancer generic drug evidence extraction from PubMed abstracts. Our primary contribution is to define the natural language processing pipeline required to obtain such evidence, comprising the following modules: querying, filtering, cancer type entity extraction, therapeutic association classification, and study type classification. Using the subject matter expertise on our team, we create our own datasets for these specialized domain-specific tasks. We obtain promising performance in each of the modules by utilizing modern language processing techniques and plan to treat them as baseline approaches for future improvement of individual components.




How to Cite

Subramanian, S., Baldini, I., Ravichandran, S., Katz-Rogozhnikov, D. A., Natesan Ramamurthy, K., Sattigeri, P., Varshney, K. R., Wang, A., Mangalath, P., & Kleiman, L. B. (2020). A Natural Language Processing System for Extracting Evidence of Drug Repurposing from Scientific Publications. Proceedings of the AAAI Conference on Artificial Intelligence, 34(08), 13369-13381. https://doi.org/10.1609/aaai.v34i08.7052



IAAI Technical Track: Emerging Papers