  Classifying chemicals according to putative modes of action (MOAs) is of
paramount importance in the context of risk assessment. However, current
methods are only able to handle a very small proportion of the existing
chemicals. We address this issue by proposing an integrative deep learning
architecture that learns a joint representation from molecular structures of
drugs and their effects on human cells. Our choice of architecture is motivated
by the significant influence of a drug's chemical structure on its MOA. We
improve on the strong ability of a unimodal architecture (F1 score of 0.803) to
classify drugs by their toxic MOAs (Verhaar scheme) through adding another
learning stream that processes transcriptional responses of human cells
affected by drugs. Our integrative model achieves an even higher classification
performance on the LINCS L1000 dataset - the error is reduced by 4.6%. We
believe that our method can be used to extend the current Verhaar scheme and
constitute a basis for fast drug validation and risk assessment.
