Although many quantitative structure–activity relationship (QSAR) models are trained and evaluated for their predictive merits, understanding what models have been learning is of critical importance. However, the interpretation and visualization of QSAR model results remain challenging, especially for ‘black box’ models such as deep neural network (DNN). Here, we take a step forward to interpret the learned chemical features from DNN QSAR models, and present VISAR, an interactive tool for visualizing the structure–activity relationship. VISAR first provides functions to construct and train DNN models. Then VISAR builds the activity landscapes based on a series of compounds using the trained model, showing the correlation between the chemical feature space and the experimental activity space after model training, and allowing for knowledge mining from a global perspective. VISAR also maps the gradients of the chemical features to the corresponding compounds as contribution weights for each atom, and visualizes the positive and negative contributor substructures suggested by the models from a local perspective. Using the web application of VISAR, users could interactively explore the activity landscape and the color-coded atom contributions. We propose that VISAR could serve as a helpful tool for training and interactive analysis of the DNN QSAR model, providing insights for drug design, and an additional level of model validation.

中文翻译：

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VISAR：一种用于解析由深度神经网络QSAR模型学习的化学特征的交互式工具。

尽管对许多定量构效关系模型（QSAR）进行了预测和评估，但了解所学习的模型至关重要。但是，QSAR模型结果的解释和可视化仍然具有挑战性，尤其是对于“黑匣子”模型，例如深度神经网络（DNN）。在这里，我们向前迈进了一步，以从DNN QSAR模型中解释学到的化学特征，并提出VISAR，这是一种可视化结构与活性关系的交互式工具。VISAR首先提供构建和训练DNN模型的功能。然后VISAR使用训练后的模型基于一系列化合物构建了活动景观，展示了模型训练后化学特征空间与实验活动空间之间的相关性，并允许从全球角度挖掘知识。VISAR还将化学特征的梯度映射为相应化合物的每个原子的贡献权重，并从局部角度可视化模型建议的正负贡献子结构。使用VISAR的Web应用程序，用户可以交互地探索活动景观和颜色编码的原子贡献。我们建议VISAR可以用作DNN QSAR模型的培训和交互式分析的有用工具，为药物设计提供见解，并提高模型验证的水平。并从局部角度可视化模型建议的正面和负面贡献者子结构。使用VISAR的Web应用程序，用户可以交互地探索活动景观和颜色编码的原子贡献。我们建议VISAR可以用作DNN QSAR模型的培训和交互式分析的有用工具，为药物设计提供见解，并提高模型验证的水平。并从局部角度可视化模型建议的正面和负面贡献者子结构。使用VISAR的Web应用程序，用户可以交互地探索活动景观和颜色编码的原子贡献。我们建议VISAR可以用作DNN QSAR模型的培训和交互式分析的有用工具，为药物设计提供见解，并提高模型验证的水平。