Despite significant investment of resources, around 40% of drug candidates are discontinued due to toxicity, often arising from reactions between electrophilic drugs or drug metabolites and nucleophilic biological macromolecules, like DNA and proteins. A deep convolution neural network tp predict both sites of reactivity (SOR) and molecular reativity. Cross-validated predictions predicted with 89.8% AUC DNA SOR, and with 94.4% AUC protein SOR, separating reactive molecules with DNA and protein from nonreactive molecules with cross-validated AUCs of 78.7% and 79.8%, respectively.

Please cite:

  1. Hughes, T. B., Miller, G. P., Swamidass, S. J. (2015). Site of Reactivity Models Predict Molecular Reactivity of Diverse Chemicals with Glutathione. Chemical Research in Toxicology, 28(4), 797-809, https://doi.org/10.1021/acs.chemrestox.5b00017
  2. Hughes, T. B., Dang, N. L., Miller, G. P., Swamidass, S. J. (2016). Modeling Reactivity to Biological Macromolecules with a Deep Multitask Network. ACS Central Science. https://doi.org/10.1021/acscentsci.6b00162