Molecular image-convolutional neural network (CNN) assisted QSAR models for predicting contaminant reactivity toward OH radicals: Transfer learning, data augmentation and model interpretation

In this study, we used molecular images as a representation for organic compounds and combined them with a convolutional neural network (CNN) to develop quantitative structure-activity relationships (QSARs) for predicting compound rate constants toward OH radicals. We applied transfer learning and data augmentation to train molecular image-CNN models and the Gradient-weighted Class Activation Mapping (Grad-CAM) method to interpret them. Results showed that data augmentation and transfer learning can effectively enhance the robustness and predictive performance of the models, with the root-mean-square-error (RMSE) values on the test dataset (RMSE_test) decreasing from (0.395–0.45) to (0.284–0.339) after applying data augmentation, and the RMSE on the training dataset (RMSE_train) decreasing from (0.452–0.592) to (0.123–0.151) after applying transfer learning. The obtained molecular image-CNN models showed comparative predictive performance (RMSE_test 0.284–0.339) with the molecular fingerprint-based models (RMSE_test 0.30–0.35). Grad-CAM interpretation showed that the molecular image-CNN models correctly chose the molecular features in the images and identified key functional groups that influenced the reactivity. The applicability domain analysis showed that the molecular image-CNN models have a broader applicability domain than molecular fingerprints-based models and the reactivity of any new compounds with a maximum similarity of over 0.85 to the compounds in the training dataset can be reliably predicted. This study demonstrated that molecular image-CNN is a new tool to develop QSARs for environmental applications and can be used to build trustful models that make meaningful predictions.