准确而稳健地估计协同药物组合对于药物精准度非常重要。尽管已经开发了一些计算方法，但由于药物组合的复杂机制和癌症样本的异质性，一些预测仍然不可靠，尤其是跨数据集预测。我们提出了JointSyn，利用双视图联合学习来预测样本药物组合的具体效果来自于药物和细胞的特性。 JointSyn 在各种基准的预测准确性和稳健性方面均优于现有的最先进方法。 JointSyn的每个视图都捕获了药物协同相关的特征，并为药物组合的最终预测做出了互补的贡献。此外，具有微调功能的 JointSyn 提高了其使用少量实验测量来预测新型药物组合或癌症样本的泛化能力。我们还使用 JointSyn 生成了泛癌药物协同作用的估计图谱，并探索了癌症之间的差异模式。这些结果证明了 JointSyn 预测药物协同作用的潜力，支持个性化组合疗法的开发。源代码和数据可在 https://github.com/LihongCSBLab/JointSyn 上获取。补充数据可在 Bioinformatics online 上获取。© 作者(s) 2024。由牛津大学出版社出版。

Accurate and robust estimation of the synergistic drug combination is important for medicine precision. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples.We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and make complementary contributes to the final prediction of drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies.Source code and data are available at https://github.com/LiHongCSBLab/JointSyn.Supplementary data are available at Bioinformatics online.© The Author(s) 2024. Published by Oxford University Press.