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.

中文翻译：

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用于改进药物相互作用预测的安全多方计算和深度神经网络的集成框架。

药物相互作用（DDI）是药物开发和药物警戒中的一个关键问题。通过整合来自不同制药公司的多源数据来改进 DDI 预测非常重要。不幸的是，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)，这是一种用于 DDI 预测的安全的基于 MPC 的深度学习框架。MPCDDI利用秘密共享技术，整合来自多个机构的药物相关特征数据，开发了DDI预测的深度学习模型。在MPCDDI中，所有数据传输和深度学习操作都集成到安全的MPC框架中，以实现制药机构之间的高质量协作，而不会泄露私人药品相关信息。结果表明 MPCDDI 优于其他八个基线，并实现了与相应明文协作相似的性能。更有趣的是，MPCDDI 的性能明显优于使用单个机构的私有数据的方法。总之，MPCDDI 是促进协作和保护隐私的药物发现的有效框架。