尽管深度神经网络（DNN）在预测癌症药物的疗效方面取得了成功，但其决策过程的缺乏可解释性是一个重要的挑战。以前的研究提出了模仿基因本体结构的方法，以便解释网络中每个神经元的作用。然而，这些以前的方法需要大量的GPU资源，并且限制了其用于基因组广泛模型的拓展。我们开发了SparseGO，一种稀疏可解释的神经网络，用于预测癌细胞系的药物反应以及其作用机制（MoA）。为了确保模型的泛化性，我们在多个数据集上进行了训练，并使用三种交叉验证方案评估了其性能。其高效性使其能够与基因表达一起使用。此外，SparseGO结合了XAI技术（DeepLIFT）和支持向量机，以计算发现药物的作用机制。与其他方法相比，SparseGO的稀疏实现显著减少了GPU内存使用和训练速度，使其能够处理基因表达而不是突变作为输入数据。使用表达来改进SparseGO的准确性并使其能够用于药物重定位。此外，基因表达还可以预测使用265种药物来训练SparseGO的MoA。在parbendazole和PD153035等研究较少的药物上进行了验证。SparseGO是一种有效的XAI方法，用于预测，更重要的是，理解药物反应。由Cancer Research UK [C355/A26819]、AECC科学基金会和Fondazione AIRC资助的Accelerator Award Programme，巴斯克政府基金 (PIBA_2020_1_0055项目) 和Synlethal项目 (RETOS Investigacion, 西班牙政府)。版权所有 © 2023 作者。Elsevier B.V.发表并保留所有权利。

Although Deep Neural Networks (DDNs) have been successful in predicting the efficacy of cancer drugs, the lack of explainability in their decision-making process is a significant challenge. Previous research proposed mimicking the Gene Ontology structure to allow for interpretation of each neuron in the network. However, these previous approaches require huge amount of GPU resources and hinder its extension to genome-wide models.We developed SparseGO, a sparse and interpretable neural network, for predicting drug response in cancer cell lines and their Mechanism of Action (MoA). To ensure model generalization, we trained it on multiple datasets and evaluated its performance using three cross-validation schemes. Its efficiency allows it to be used with gene expression. In addition, SparseGO integrates an eXplainable Artificial Intelligence (XAI) technique, DeepLIFT, with Support Vector Machines to computationally discover the MoA of drugs.SparseGO's sparse implementation significantly reduced GPU memory usage and training speed compared to other methods, allowing it to process gene expression instead of mutations as input data. SparseGO using expression improved the accuracy and enabled its use on drug repositioning. Furthermore, gene expression allows the prediction of MoA using 265 drugs to train it. It was validated on understudied drugs such as parbendazole and PD153035.SparseGO is an effective XAI method for predicting, but more importantly, understanding drug response.The Accelerator Award Programme funded by Cancer Research UK [C355/A26819], Fundación Científica de la AECC and Fondazione AIRC, Project PIBA_2020_1_0055 funded by the Basque Government and the Synlethal Project (RETOS Investigacion, Spanish Government).Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.