  Prediction of toxicity levels of chemical compounds is an important issue in
Quantitative Structure-Activity Relationship (QSAR) modeling. Although toxicity
prediction has achieved significant progress in recent times through deep
learning, prediction accuracy levels obtained by even very recent methods are
not yet very high. We propose a multimodal deep learning method using multiple
heterogeneous neural network types and data representations. We represent
chemical compounds by strings, images, and numerical features. We train fully
connected, convolutional, and recurrent neural networks and their ensembles.
Each data representation or neural network type has its own strengths and
weaknesses. Our motivation is to obtain a collective performance that could go
beyond individual performance of each data representation or each neural
network type. On a standard toxicity benchmark, our proposed method obtains
significantly better accuracy levels than that by the state-of-the-art toxicity
prediction methods.
