  The meteoric rise of deep learning models in computer vision research, having
achieved human-level accuracy in image recognition tasks is firm evidence of
the impact of representation learning of deep neural networks. In the chemistry
domain, recent advances have also led to the development of similar CNN models,
such as Chemception, that is trained to predict chemical properties using
images of molecular drawings. In this work, we investigate the effects of
systematically removing and adding localized domain-specific information to the
image channels of the training data. By augmenting images with only 3
additional basic information, and without introducing any architectural
changes, we demonstrate that an augmented Chemception (AugChemception)
outperforms the original model in the prediction of toxicity, activity, and
solvation free energy. Then, by altering the information content in the images,
and examining the resulting model's performance, we also identify two distinct
learning patterns in predicting toxicity/activity as compared to solvation free
energy. These patterns suggest that Chemception is learning about its tasks in
the manner that is consistent with established knowledge. Thus, our work
demonstrates that advanced chemical knowledge is not a pre-requisite for deep
learning models to accurately predict complex chemical properties.
