Title: Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models,
Abstract: In the last few years, we have seen the transformative impact of deep
learning in many applications, particularly in speech recognition and computer
vision. Inspired by Google's Inception-ResNet deep convolutional neural network
(CNN) for image classification, we have developed "Chemception", a deep CNN for
the prediction of chemical properties, using just the images of 2D drawings of
molecules. We develop Chemception without providing any additional explicit
chemistry knowledge, such as basic concepts like periodicity, or advanced
features like molecular descriptors and fingerprints. We then show how
Chemception can serve as a general-purpose neural network architecture for
predicting toxicity, activity, and solvation properties when trained on a
modest database of 600 to 40,000 compounds. When compared to multi-layer
perceptron (MLP) deep neural networks trained with ECFP fingerprints,
Chemception slightly outperforms in activity and solvation prediction and
slightly underperforms in toxicity prediction. Having matched the performance
of expert-developed QSAR/QSPR deep learning models, our work demonstrates the
plausibility of using deep neural networks to assist in computational chemistry
research, where the feature engineering process is performed primarily by a
deep learning algorithm.