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As of 2022, current drug development pipelines last around 10 years, costing $2billion in average, while drug commercialization failure rates go up to 90%. These issues can be mitigated by drug repurposing, where chemical compounds are screened for new therapeutic indications in a systematic fashion. In prior works, this approach has been implemented through collaborative filtering. This semi-supervised learning framework leverages known drug-disease matchings in order to recommend new ones.

This repository is a part of the Marie Skłodowska-Curie Postdoctoral Fellowship-funded two-year-long RECeSS project (#101102016), and hosts the code for the open-source Python package stanscofi, for the development of collaborative filtering-based drug repurposing algorithms, along with notebooks and code files which helped to build novel drug repurposing datasets. In the short term, the RECeSS project would yield the first method that fully integrates biological interpretation and risk assessment to collaborative filtering-based repurposing. Long-term outcomes might help define sustainable and transparent drug development for rare diseases.

Research leads will be investigated by the postdoctoral fellow Dr. Clémence Réda. The project is primarily supervised by Pr. Olaf Wolkenhauer, at SBI Rostock (Universität Rostock), in collaboration with Dr. Jill-Jênn Vie, in the Soda team (Inria Saclay).

Please feel free to ask questions on the contact form of our project website or at [email protected].

Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HORIZON 2020 funding programme. Neither the European Union nor the granting authority can be held responsible for them.