  Deep learning algorithms excel at extracting patterns from raw data, and with
large datasets, they have been very successful in computer vision and natural
language applications. However, in other domains, large datasets on which to
learn representations from may not exist. In this work, we develop a novel
multimodal CNN-MLP neural network architecture that utilizes both
domain-specific feature engineering as well as learned representations from raw
data. We illustrate the effectiveness of such network designs in the chemical
sciences, for predicting biodegradability. DeepBioD, a multimodal CNN-MLP
network is more accurate than either standalone network designs, and achieves
an error classification rate of 0.125 that is 27% lower than the current
state-of-the-art. Thus, our work indicates that combining traditional feature
engineering with representation learning can be effective, particularly in
situations where labeled data is limited.
