XenoSite

What is XenoSite?

XenoSite predicts how small-molecules become toxic after metabolism by liver enzymes.

Getting Help

Post comments, questions or problems on the forum.

Terms of Use

This website and the results it reports are free for all academic and non-commercial use. Licenses are available for secure commercial access and access to the programatic API.

More to Come

This site is under active development. More models and features will be available soon. What should we prioritize? Tell us on the forum.

Epoxidation

Epoxides are a common reactive metabolite, often formed by cytochromes P450 acting on aromatic or double bonds. The specific location on a molecule that undergoes epoxidation is its site of epoxidation (SOE). This algorithm systematically and quantitatively summarizes the knowledge from hundreds of epoxidation reactions, identifying SOEs with 94.9% area under the curve accuracy and separated epoxidized and non-epoxidized molecules with 78.6% accuracy.

Please cite:

  1. Hughes, T. B., Miller, G. P., Swamidass, S. J. (2015). Modeling Epoxidation of Drug-like Molecules with a Deep Machine Learning Network. ACS Central Science, 1(4), 168-180. https://doi.org/10.1021/acscentsci.5b00131

Quinonation

Quinone species, including quinone-imines, quinone-methides, and imine-methides, are electrophilic Michael acceptors that are often highly reactive, and comprise over 40% of all known reactive metabolites. Quinone metabolites are created by cytochromes P450 and peroxidases. This is there first published method for predicting quinone formation, including one- and two-step quinone formation. On the atom level, we predict sites of quinone formation with an AUC accuracy of 97.6%, and we identify molecules that form quinones with 88.2% AUC.

Please cite:

  1. Hughes, T. B. and Swamidass, S. J. (2017). Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism. Chemical Research in Toxicology. https://doi.org/10.1021/acs.chemrestox.6b00385

Reactivity

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

Phase 1

Phase I enzymes, which are responsible for the metabolism of more than 90% of FDA approved drugs, catalyze highly diverse types of reactions and produce metabolites with substantial structural variability. We propose a system for simultaneously labeling sites of metabolism and reaction types, classifying them into five key reaction classes: stable and unstable oxidations, dehydrogenation, hydrolysis, and reduction. These classes unambiguously identify 21 types of phase I reactions, which cover 92.3% of known reactions in our database. We used this labeling system to train a neural network on 20,736 human phase I metabolic reactions, able to identify reaction-type specific sites of metabolism with a cross-validated accuracy of 97.1% area under the receiver operator curve.

Please cite:

  1. The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors. Na Le Dang, Matthew K. Matlock, Tyler B. Hughes, and S. Joshua Swamidass Journal of Chemical Information and Modeling 2020 60 (3), 1146-1164. https://doi.org/10.1021/acs.jcim.9b00836

N-Dealkylation

Metabolic studies usually neglect to report or investigate aldehydes, even though they can be toxic. It is assumed that they are efficiently detoxified into carboxylic acids and alcohols. Nevertheless, some aldehydes are reactive and escape detoxification pathways to cause adverse events by forming DNA and protein adducts. This model accurately predicted the site of N-dealkylation within metabolized substrates (97% top-two and 94% area under the ROC curve).

Please cite:

  1. Computationally Assessing the Bioactivation of Drugs by N-Dealkylation Na Le Dang, Tyler B. Hughes, Grover P. Miller, and S. Joshua Swamidass. Chemical Research in Toxicology 2018 31 (2), 68-80. https://doi.org/10.1021/acs.chemrestox.7b00191

UGT Conjugation

Uridine diphosphate glucuronosyltransferases (UGTs) metabolize 15% of FDA approved drugs. Lead optimization efforts benefit from knowing how candidate drugs are metabolized by UGTs. The XenoSite UGT model predicts sites of UGT-mediated metabolism on drug-like molecules. In the training data, the sites of metabolism of 2839 UGT substrates are identified by our method with 86% (Top-1) and 97% (Top-2) accuracy.

Please cite:

  1. Dang, N. L, Hughes, T. B., Krishnamurthy, V., and Swamidass, S. J. (2016). A Simple Model Predicts UGT-Mediated Metabolism. Bioinformatics. https://doi.org/10.1093/bioinformatics/btw350