An activity cliff (AC) is formed by a pair of structurally similar compounds with a large difference in potency. Accordingly, ACs reveal structure–activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein, we have investigated the question if ACs could be predicted from image data. Therefore, pairs of structural analogs were extracted from different compound activity classes that formed or did not form ACs. From these compound pairs, consistently formatted images were generated. Image sets were used to train and test convolutional neural network (CNN) models to systematically distinguish between ACs and non-ACs. The CNN models were found to predict ACs with overall high accuracy, as assessed using alternative performance measures, hence establishing proof-of-principle. Moreover, gradient weights from convolutional layers were mapped to test compounds and identified characteristic structural features that contributed to successful predictions. Weight-based feature visualization revealed the ability of CNN models to learn chemistry from images at a high level of resolution and aided in the interpretation of model decisions with intrinsic black box character.

活性悬崖 (AC) 由一对结构相似的化合物形成，但效力差异很大。因此，AC 揭示了结构-活性关系 (SAR) 的不连续性，并为复合优化提供了 SAR 信息。在这里，我们研究了是否可以从图像数据中预测 AC 的问题。因此，从形成或不形成 ACs 的不同化合物活性类别中提取了成对的结构类似物。从这些化合物对中，生成了格式一致的图像。图像集用于训练和测试卷积神经网络 (CNN) 模型，以系统地区分 AC 和非 AC。发现 CNN 模型以整体高精度预测 AC，正如使用替代性能测量所评估的那样，因此建立了原理证明。而且，来自卷积层的梯度权重被映射到测试化合物，并确定了有助于成功预测的特征结构特征。基于权重的特征可视化揭示了 CNN 模型从高分辨率图像中学习化学成分的能力，并有助于解释具有内在黑盒特征的模型决策。