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Cited by 76Computational Drug Discovery MethodsPrivacy-Preserving Technologies in DataCryptography and Data Security

Realizing private and practical pharmacological collaboration

Open access
Hie, Brian, Cho, Hyunghoon, Berger, Bonnie·2018·Science
DOI 10.1126/science.aat4807PMC PMC6519716
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Abstract

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

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  • Hie et al. proposed a framework for predicting drug-target interactions based on private datasets from individual entities.

    “For instance, Hie et al. proposed a framework for allowing predicting drug–target interactions based on private datasets from individual entities [160].”

    From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [160]

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Cites

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Research works printed but not linked: 26

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Cited by

Sorted by citations

Authors
Hie, B., Cho, H., & Berger, B.
Affiliations
MIT Computer Science and Artificial Intelligence Laboratory (US)
Corresponding author
  • Hie, Brian
  • Cho, Hyunghoon
  • Berger, Bonnie
ORCID
Venue
Science
Publisher
American Association for the Advancement of Science (AAAS)
Published
2018-10-19
Volume
362
Issue
6412
Pages
347-350
ISSN
  • 0036-8075 (ISSN-L)
  • 1095-9203
DOI
10.1126/science.aat4807
PMC
PMC6519716
OpenAlex
W2896757914
Cited by
76
References
1
Citations by year
2026: 5 · 2025: 5 · 2024: 13 · 2023: 8 · 2022: 18 · 2021: 6 · 2020: 11 · 2019: 9 · 2018: 1
FWCI
5.05
Type
journal article
Language
en
Open access
Locations
  • Science (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Funders
National Institutes of Health (R01GM081871)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
Keywords
drug-target interaction prediction, cryptographic protocols, data privacy, intellectual property, privacy-preserving machine learning, drug discovery
MeSH terms
Computer Simulation, Confidentiality, Humans, Pharmacology, Drug Delivery Systems, Information Dissemination, Databases, Pharmaceutical

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