Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA.
Department of Mathematics, MIT, Cambridge, MA 02139, USA.
Science. 2018 Oct 19;362(6412):347-350. doi: 10.1126/science.aat4807.
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.
虽然整合来自多个实体的数据可以推动拯救生命的突破，但由于数据隐私和知识产权的问题，开放地共享药理学数据通常是不可行的。为此，我们利用现代加密工具引入了一个计算协议，该协议可以在一个汇总数据集上安全地训练药物-靶标相互作用（DTI）的预测模型，该协议通过可证明确保所有基础药物、靶标和观察到的相互作用的机密性来克服数据共享的障碍。我们的协议在一个超过 100 万相互作用的真实数据集上可以在几天内运行，并且比最先进的 DTI 预测方法更准确。使用我们的协议，我们发现了以前未识别的 DTI，我们通过靶向测定实验验证了这些 DTI。我们的工作为更有效和合作的生物医学研究奠定了基础。
