Structure-Based Networks for Drug Validation
This work addresses the limitation of current methods in handling a large proportion of chemicals for drug validation and risk assessment, though it is incremental in improving classification performance.
The paper tackles the problem of classifying chemicals by their modes of action (MOAs) for risk assessment, proposing an integrative deep learning model that combines molecular structures and transcriptional responses to achieve a 4.6% error reduction on the LINCS L1000 dataset.
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.