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Science. 2018 Oct 19;362(6412):347-350. doi: 10.1126/science.aat4807.

Realizing private and practical pharmacological collaboration.

Author information

1
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
2
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA. bab@mit.edu.
3
Department of Mathematics, MIT, Cambridge, MA 02139, USA.

Abstract

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.

PMID:
30337410
DOI:
10.1126/science.aat4807
[Indexed for MEDLINE]

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