Structure-Based Networks for Drug Validation



Cangea, Cătălina, Grauslys, Arturas, Liò, Pietro and Falciani, Francesco
Structure-Based Networks for Drug Validation.

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Abstract

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.

Item Type: Article
Additional Information: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216
Uncontrolled Keywords: q-bio.QM, q-bio.QM, cs.AI, cs.LG, stat.ML
Depositing User: Symplectic Admin
Date Deposited: 02 Mar 2020 11:27
Last Modified: 02 Mar 2020 11:51
Related URLs:
URI: http://livrepository.liverpool.ac.uk/id/eprint/3076791