College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
Shanghai Jiao Tong University, Shanghai, China.
J Comput Biol. 2023 Sep;30(9):1034-1045. doi: 10.1089/cmb.2023.0076. Epub 2023 Sep 14.
Drug-drug interaction (DDI) is a key concern in drug development and pharmacovigilance. It is important to improve DDI predictions by integrating multisource data from various pharmaceutical companies. Unfortunately, the data privacy and financial interest issues seriously influence the interinstitutional collaborations for DDI predictions. We propose multiparty computation DDI (MPCDDI), a secure MPC-based deep learning framework for DDI predictions. MPCDDI leverages the secret sharing technologies to incorporate the drug-related feature data from multiple institutions and develops a deep learning model for DDI predictions. In MPCDDI, all data transmission and deep learning operations are integrated into secure MPC frameworks to enable high-quality collaboration among pharmaceutical institutions without divulging private drug-related information. The results suggest that MPCDDI is superior to other eight baselines and achieves the similar performance to that of the corresponding plaintext collaborations. More interestingly, MPCDDI significantly outperforms methods that use private data from the single institution. In summary, MPCDDI is an effective framework for promoting collaborative and privacy-preserving drug discovery.
药物-药物相互作用（DDI）是药物开发和药物警戒中的一个关键关注点。通过整合来自不同制药公司的多源数据来提高 DDI 预测的准确性非常重要。然而，数据隐私和经济利益问题严重影响了 DDI 预测的机构间合作。我们提出了多方计算 DDI（MPCDDI），这是一种基于多方计算的安全深度学习框架，用于 DDI 预测。MPCDDI 利用秘密共享技术整合来自多个机构的与药物相关的特征数据，并开发用于 DDI 预测的深度学习模型。在 MPCDDI 中，所有的数据传输和深度学习操作都集成到安全的 MPC 框架中，使制药机构能够在不泄露私人药物相关信息的情况下进行高质量的合作。结果表明，MPCDDI 优于其他八个基线，并且与相应的明文合作具有相似的性能。更有趣的是，MPCDDI 显著优于使用单个机构的私人数据的方法。总之，MPCDDI 是促进协作和保护隐私的药物发现的有效框架。
