了解癌症治疗中的药物反应差异是个性化医学中最具挑战性的方面之一。近年来，在生物信息学的许多图表示学习场景中，图神经网络（GNNs）已成为最先进的方法之一。然而，为特定的药物敏感性数据集构建一个最佳的手工设计GNN模型需要对GNN模型的超参数进行手动设计和微调，这是耗时且需要专业知识的工作。在这项工作中，我们提出了AutoCDRP，一种利用图神经网络（GNNs）进行自动化癌症药物反应预测的新框架。我们的方法利用代理模型来高效搜索最有效的GNN架构。AutoCDRP使用代理模型来预测从搜索空间中抽样的GNN架构的性能，从而基于评估性能选择最佳的架构。因此，AutoCDRP可以通过探索搜索空间中所有GNN架构的性能来高效地确定最佳的GNN架构。通过在两个基准数据集上进行全面的实验，我们证明了AutoCDRP生成的GNN架构超过了最先进的设计。值得注意的是，AutoCDRP确定的最佳GNN架构始终优于第一个时期的最佳基准架构，进一步证明了其有效性。补充数据可在Bioinformatics在线获取。©作者2023年，由牛津大学出版社发表。

Understanding drug response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and requires expert knowledge.In this work, we propose AutoCDRP, a novel framework for automated cancer drug response predictor using graph neural networks (GNNs). Our approach leverages surrogate modeling to efficiently search for the most effective GNN architecture. AutoCDRP uses a surrogate model to predict the performance of GNN architectures sampled from a search space, allowing it to select the optimal architecture based on evaluation performance. Hence, AutoCDRP can efficiently identify the optimal GNN architecture by exploring the performance of all GNN architectures in the search space. Through comprehensive experiments on two benchmark datasets, we demonstrate that the GNN architecture generated by AutoCDRP surpasses state-of-the-art designs. Notably, the optimal GNN architecture identified by AutoCDRP consistently outperforms the best baseline architecture from the first epoch, providing further evidence of its effectiveness.https://github.com/BeObm/AutoCDRP.Supplementary data are available at Bioinformatics online.© The Author(s) 2023. Published by Oxford University Press.