MOTIVATION Quantitative structure-activity relationship (QSAR) and drug-target interaction (DTI) prediction are both commonly used in drug discovery. Collaboration among pharmaceutical institutions can lead to better performance in both QSAR and DTI prediction. However, the drug-related data privacy and intellectual property issues have become a noticeable hindrance for inter-institutional collaboration in drug discovery. RESULTS We have developed two novel algorithms under secure multiparty computation (MPC), including QSARMPC and DTIMPC, which enable pharmaceutical institutions to achieve high-quality collaboration to advance drug discovery without divulging private drug-related information. QSARMPC, a neural network model under MPC, displays good scalability and performance and is feasible for privacy-preserving collaboration on large-scale QSAR prediction. DTIMPC integrates drug-related heterogeneous network data and accurately predicts novel DTIs, while keeping the drug information confidential. Under several experimental settings that reflect the situations in real drug discovery scenarios, we have demonstrated that DTIMPC possesses significant performance improvement over the baseline methods, generates novel DTI predictions with supporting evidence from the literature and shows the feasible scalability to handle growing DTI data. All these results indicate that QSARMPC and DTIMPC can provide practically useful tools for advancing privacy-preserving drug discovery. AVAILABILITY AND IMPLEMENTATION The source codes of QSARMPC and DTIMPC are available on the GitHub: https://github.com/rongma6/QSARMPC_DTIMPC.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

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用于保护隐私的药物发现的安全多方计算。

动机定量结构-活性关系（QSAR）和药物-靶标相互作用（DTI）预测都是药物发现中常用的方法。制药机构之间的合作可以提高QSAR和DTI预测的性能。但是，与毒品有关的数据隐私和知识产权问题已成为毒品发现中机构间合作的显着障碍。结果我们在安全多方计算（MPC）下开发了两种新颖的算法，包括QSARMPC和DTIMPC，这些算法使制药机构能够实现高质量的合作，从而在不泄露与药物相关的私人信息的情况下促进药物开发。QSARMPC是MPC下的神经网络模型，具有良好的可扩展性和性能，对于大规模QSAR预测中的隐私保护协作是可行的。DTIMPC整合了与毒品有关的异构网络数据，并准确预测了新的DTI，同时使毒品信息保持机密。在反映真实药物发现场景中情况的几个实验设置下，我们证明了DTIMPC与基准方法相比具有显着的性能改进，并根据文献提供的支持证据生成了新颖的DTI预测，并显示了处理不断增长的DTI数据的可行性。所有这些结果表明，QSARMPC和DTIMPC可以为推进隐私保护药物发现提供实用的工具。可用性和实现QSARMPC和DTIMPC的源代码可在GitHub上获得：https：//github.com/rongma6/QSARMPC_DTIMPC.git。补充信息补充数据可从Bioinformatics在线获得。