Realizing private and practical pharmacological collaboration
Author(s)Hie, Brian; Cho, Hyunghoon; Berger Leighton, Bonnie
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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.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Mathematics
American Association for the Advancement of Science
Hie, Brian et al. "Realizing private and practical pharmacological collaboration." Science 362, 6412 (2018): 347–350 © 2018 American Association for the Advancement of Science
Author's final manuscript