How Much Chemistry Does a Deep Neural Network Need to Know to Make Accurate Predictions?

Understand

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.