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Deep learning algorithms excel at extracting patterns from raw data, and withlarge datasets, they have been very successful in computer vision and naturallanguage applications. However, in other domains, large datasets on which tolearn representations from may not exist. In this work, we develop a novelmultimodal CNN-MLP neural network architecture that utilizes bothdomain-specific feature engineering as well as learned representations from rawdata. We illustrate the effectiveness of such network designs in the chemicalsciences, for predicting biodegradability. DeepBioD, a multimodal CNN-MLPnetwork is more accurate than either standalone network designs, and achievesan error classification rate of 0.125 that is 27% lower than the currentstate-of-the-art. Thus, our work indicates that combining traditional featureengineering with representation learning can be effective, particularly insituations where labeled data is limited.

Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction