  For quantitative structure-property relationship (QSPR) studies in
chemoinformatics, it is important to get interpretable relationship between
chemical properties and chemical features. However, the predictive power and
interpretability of QSPR models are usually two different objectives that are
difficult to achieve simultaneously. A deep learning architecture using
molecular graph encoding convolutional neural networks (MGE-CNN) provided a
universal strategy to construct interpretable QSPR models with high predictive
power. Instead of using application-specific preset molecular descriptors or
fingerprints, the models can be resolved using raw and pertinent features
without manual intervention or selection. In this study, we developed acute
oral toxicity (AOT) models of compounds using the MGE-CNN architecture as a
case study. Three types of high-level predictive models: regression model
(deepAOT-R), multi-classification model (deepAOT-C) and multi-task model
(deepAOT-CR) for AOT evaluation were constructed. These models highly
outperformed previously reported models. For the two external datasets
containing 1673 (test set I) and 375 (test set II) compounds, the R2 and mean
absolute error (MAE) of deepAOT-R on the test set I were 0.864 and 0.195, and
the prediction accuracy of deepAOT-C was 95.5% and 96.3% on the test set I and
II, respectively. The two external prediction accuracy of deepAOT-CR is 95.0%
and 94.1%, while the R2 and MAE are 0.861 and 0.204 for test set I,
respectively.
