Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo.

中文翻译：

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kGCN：基于图的化学结构深度学习框架


深度学习正在发展成为执行化学信息学中各种任务的重要技术。特别是，据报道，图卷积神经网络（GCN）在与分子相关的许多类型的预测任务中表现良好。尽管 GCN 在各种应用中表现出巨大的潜力，但适当利用该资源以获得合理可靠的预测结果需要对 GCN 和编程有透彻的了解。为了利用 GCN 的力量使从化学家到化学信息学家的各种用户受益，引入了开源 GCN 工具 kGCN。为了支持具有不同编程技能水平的用户，kGCN 包括三个界面：针对化学家等编程技能有限的用户使用 KNIME 的图形用户界面 (GUI)，以及针对高级编程用户的命令行和 Python 库界面化学信息学家等技能。为了支持构建预测模型所需的三个步骤，即预处理、模型调整和结果解释，kGCN 包括典型预处理、自动模型调整的贝叶斯优化和预测原子贡献可视化的功能用于结果的解释。 kGCN 支持三种类型的方法：单任务、多任务和多模态预测。使用 kGCN 作为代表性案例研究，对抑制测定中四种基质金属蛋白酶（MMP-3、-9、-12 和 -13）的化合物-蛋白质相互作用进行预测。此外，kGCN 还提供了原子对预测贡献的可视化。 这种可视化有助于预测模型的验证和基于预测模型的分子设计，实现“可解释的人工智能”以了解影响人工智能预测的因素。 kGCN 可在 https://github.com/clinfo 上获取。