癌症异质性极大地影响了癌症治疗的结果。在体外预测药物反应有望帮助制定个性化的治疗方案。近年来，已经提出了几种基于机器学习和深度学习的计算模型用于预测体外药物反应。然而，这些方法大多捕捉基于单一药物描述（如药物结构）的药物特征，没有考虑药物与生物实体之间的关系（如目标、疾病和副作用）。此外，这些方法大多将药物和细胞系的特征分别收集，而忽视了药物和细胞系之间的成对相互作用。在本文中，我们提出了一种名为MSDRP的深度学习框架，用于预测药物反应。MSDRP使用交互模块捕捉药物和细胞系之间的相互作用，并通过相似性网络融合（SNF）算法整合药物和生物实体之间的多个关联/相互作用，优于一些最先进的模型在所有实验的所有性能指标上。来自de novo测试和独立测试的实验结果证明了我们模型在新药物上的出色表现。此外，几个案例研究说明了使用从多源数据中得出的药物相似性矩阵的特征向量来表示药物的合理性以及我们模型的可解释性。MSDRP的代码可在https://github.com/xyzhang-10/MSDRP获取。©作者2023年。由牛津大学出版。

Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g., drug structure), without considering the relationships between drugs and biological entities (e.g., target, diseases and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines.In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion (SNF) algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multi-source data to represent drugs and the interpretability of our model.The codes of MSDRP are available at https://github.com/xyzhang-10/MSDRP.© The Author(s) 2023. Published by Oxford University Press.