Realizing private and practical pharmacological collaboration
Name
nihms-1021962.pdf
Description
Accepted version
Size
549.58 KB
Format
Adobe PDF
Checksum (MD5)
4554db41660ed1f8ab8216ed85db5461
Author(s)
Hie, Brian
Cho, Hyunghoon
Berger Leighton, Bonnie
Date Issued
October 18, 2018
Journal
Science
Publisher
American Association for the Advancement of Science
Citation
Hie, Brian et al. "Realizing private and practical pharmacological collaboration." Science 362, 6412 (2018): 347–350 © 2018 American Association for the Advancement of Science
Version
Author's final manuscript
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1126/science.aat4807
