基于多组学数据和药物信息，预测癌细胞系对药物的反应是现代肿瘤学研究的一个重要领域，因为它可以促进个性化治疗的发展。尽管现有模型取得了令人鼓舞的性能，但大多数模型忽视了不同组学之间的差异，并且缺乏多组学数据的有效整合。此外，现有方法尚未对细胞系/药物属性和细胞系-药物关联的显式建模进行彻底研究。为了解决这些问题，我们提出了 RedCDR，一种用于癌症药物反应 (CDR) 预测的双关系蒸馏模型。具体来说，设计了并行双分支架构，以实现细胞系/药物属性和细胞系-药物关联信息的独立学习和交互式融合。为了促进多组学数据的自适应交互集成，所提出的多组学编码器引入了细胞系之间的多重相似性关系，并考虑了不同组学数据的重要性。为了实现从两个独立的属性和关联分支到它们的融合的知识迁移，提出了一种由表示蒸馏和预测蒸馏组成的双关系蒸馏机制。在 GDSC 和 CCLE 数据集上进行的实验表明，RedCDR 在 CDR 预测方面优于以前最先进的方法。源代码可在 https://github.com/mhxu1998/RedCDR 获取。

Based on multi-omics data and drug information, predicting the response of cancer cell lines to drugs is a crucial area of research in modern oncology, as it can promote the development of personalized treatments. Despite the promising performance achieved by existing models, most of them overlook the variations among different omics and lack effective integration of multi-omics data. Moreover, the explicit modeling of cell line/drug attribute and cell line-drug association has not been thoroughly investigated in existing approaches. To address these issues, we propose RedCDR, a dual relation distillation model for cancer drug response (CDR) prediction. Specifically, a parallel dual-branch architecture is designed to enable both the independent learning and interactive fusion feasible for cell line/drug attribute and cell line-drug association information. To facilitate the adaptive interacting integration of multi-omics data, the proposed multi-omics encoder introduces the multiple similarity relations between cell lines and takes the importance of different omics data into account. To accomplish knowledge transfer from the two independent attribute and association branches to their fusion, a dual relation distillation mechanism consisting of representation distillation and prediction distillation is presented. Experiments conducted on the GDSC and CCLE datasets show that RedCDR outperforms previous state-of-the-art approaches in CDR prediction. The source code is available at https://github.com/mhxu1998/RedCDR.