Realizing private and practical pharmacological collaboration

Sharing pharmaceutical research Increased collaboration will enhance our ability to predict new therapeutic drug candidates. Such data sharing is currently limited by concerns about intellectual property and competing commercial interests. Hie et al. introduce an end-to-end pipeline, using modern cryptographic tools, for secure pharmacological collaboration. Multiple entities can thus securely combine their private datasets to collectively obtain more accurate predictions of new drug-target interactions. The computational pipeline is practical, producing results with improved accuracy in a few days over a wide area network on a real dataset with more than a million interactions. Science, this issue p. 347 A computational protocol enables private pharmacological data to be securely combined. Although combining data from multiple entities could power life-saving breakthroughs, open sharing of pharmacological data is generally not viable because of data privacy and intellectual property concerns. To this end, we leverage modern cryptographic tools to introduce a computational protocol for securely training a predictive model of drug–target interactions (DTIs) on a pooled dataset that overcomes barriers to data sharing by provably ensuring the confidentiality of all underlying drugs, targets, and observed interactions. Our protocol runs within days on a real dataset of more than 1 million interactions and is more accurate than state-of-the-art DTI prediction methods. Using our protocol, we discover previously unidentified DTIs that we experimentally validated via targeted assays. Our work lays a foundation for more effective and cooperative biomedical research.