准确预测癌症药物反应（CDR）是现代肿瘤学中的长期挑战，是个性化治疗的基础。目前的计算方法通过建模整个药物和细胞系之间的反应来实现CDR预测，而没有考虑反应结果可能主要归因于一些更细微级别的“子组件”，例如药物的特权亚结构或癌细胞的基因特征，从而产生难以解释的预测结果。在此，我们提出了SubCDR，一种用于可解释CDR预测的子组件引导的深度学习方法，以识别驱动反应结果的最相关子组件。从技术上讲，SubCDR建立在一系列深度神经网络之上，可以从每个药物和细胞系的剖析中提取一组功能性子组件，并将CDR预测分解为识别子组件之间的配对交互作用。这种子组件交互形式可以提供一个可追踪的路径，明确指示哪些子组件对反应结果的贡献更大。我们通过对GDSC数据集进行广泛的计算实验，验证了SubCDR优于现有CDR预测方法的优越性。关键是，我们发现了许多预测病例，证明了SubCDR在寻找驱动反应的关键子组件和利用这些子组件发现新的治疗药物方面的优势。这些结果表明，SubCDR对生物医学研究人员特别是抗癌药物设计领域非常有用。版权：© 2023 Liu, Zhang. 本文是根据知识共享署名许可协议发布的开放访问文章，允许任何媒体以任何形式自由使用、发布和再现，前提是保留原作者和来源的署名。

Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, without the consideration that response outcomes may primarily attribute to a few finer-level 'subcomponents', such as privileged substructures of the drug or gene signatures of the cancer cell, thus producing predictions that are hard to explain. Herein, we present SubCDR, a subcomponent-guided deep learning method for interpretable CDR prediction, to recognize the most relevant subcomponents driving response outcomes. Technically, SubCDR is built upon a line of deep neural networks that enables a set of functional subcomponents to be extracted from each drug and cell line profile, and breaks the CDR prediction down to identifying pairwise interactions between subcomponents. Such a subcomponent interaction form can offer a traceable path to explicitly indicate which subcomponents contribute more to the response outcome. We verify the superiority of SubCDR over state-of-the-art CDR prediction methods through extensive computational experiments on the GDSC dataset. Crucially, we found many predicted cases that demonstrate the strength of SubCDR in finding the key subcomponents driving responses and exploiting these subcomponents to discover new therapeutic drugs. These results suggest that SubCDR will be highly useful for biomedical researchers, particularly in anti-cancer drug design.Copyright: © 2023 Liu, Zhang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.