RS-Predictor Models Augmented with SMARTCyp Reactivities: Robust Metabolic Regioselectivity Predictions for Nine CYP Isozymes

Zaretzki, Jed; Rydberg, Patrik; Bergeron, Charles; Bennett, Kristin P.; Olsen, Lars; Breneman, Curt M.

doi:10.1021/ci300009z

Cited by 73 publications

(120 citation statements)

References 67 publications

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“…This performance is competitive with other published machine learning methods [14] , [15] but is achieved using 2D descriptors. This suggests that there are common patterns in the local structure of SoMs in the data sets that are captured by the atomic circular fingerprints and can be identified by machine learning methods.…”

Section: Discussionmentioning

confidence: 82%

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

et al. 2014

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BackgroundThe prediction of sites and products of metabolism in xenobiotic compounds is key to the development of new chemical entities, where screening potential metabolites for toxicity or unwanted side-effects is of crucial importance. In this work 2D topological fingerprints are used to encode atomic sites and three probabilistic machine learning methods are applied: Parzen-Rosenblatt Window (PRW), Naive Bayesian (NB) and a novel approach called RASCAL (Random Attribute Subsampling Classification ALgorithm). These are implemented by randomly subsampling descriptor space to alleviate the problem often suffered by data mining methods of having to exactly match fingerprints, and in the case of PRW by measuring a distance between feature vectors rather than exact matching. The classifiers have been implemented in CUDA/C++ to exploit the parallel architecture of graphical processing units (GPUs) and is freely available in a public repository.ResultsIt is shown that for PRW a SoM (Site of Metabolism) is identified in the top two predictions for 85%, 91% and 88% of the CYP 3A4, 2D6 and 2C9 data sets respectively, with RASCAL giving similar performance of 83%, 91% and 88%, respectively. These results put PRW and RASCAL performance ahead of NB which gave a much lower classification performance of 51%, 73% and 74%, respectively.Conclusions2D topological fingerprints calculated to a bond depth of 4-6 contain sufficient information to allow the identification of SoMs using classifiers based on relatively small data sets. Thus, the machine learning methods outlined in this paper are conceptually simpler and more efficient than other methods tested and the use of simple topological descriptors derived from 2D structure give results competitive with other approaches using more expensive quantum chemical descriptors. The descriptor space subsampling approach and ensemble methodology allow the methods to be applied to molecules more distant from the training data where data mining would be more likely to fail due to the lack of common fingerprints. The RASCAL algorithm is shown to give equivalent classification performance to PRW but at lower computational expense allowing it to be applied more efficiently in the ensemble scheme.

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Section: Discussionmentioning

confidence: 82%

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

et al. 2014

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“…However, there are several computational tools available for predicting sites of metabolism, which frequently use quantum chemical calculations that correlate to the activation energies of cytochromes P450. 34,35,46–49 Because of the absence of other methods for predicting quinone formation, we computed several quantum chemical descriptors to establish baseline performances (Tables S8 and S9). These descriptors were calculated by MOPAC, a quantum chemistry package that performs self-consistent field computations, with the COSMO implicit solvent model and the semiempirical method PM7.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism

Hughes

Swamidass

2017

Chem. Res. Toxicol.

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Many adverse drug reactions are thought to be caused by electrophilically reactive drug metabolites that conjugate to nucleophilic sites within DNA and proteins, causing cancer or toxic immune responses. 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. For example, cytochromes P450 oxidize acetaminophen to N-acetyl-p-benzoquinone imine, which is electrophilically reactive and covalently binds to nucleophilic sites within proteins. This reactive quinone metabolite elicits a toxic immune response when acetaminophen exceeds a safe dose. Using a deep learning approach, this study reports the first published method for predicting quinone formation: the formation of a quinone species by metabolic oxidation. We model both one- and two-step quinone formation, enabling accurate quinone formation predictions in nonobvious cases. We predict atom pairs that form quinones with an AUC accuracy of 97.6%, and we identify molecules that form quinones with 88.2% AUC. By modeling the formation of quinones, one of the most common types of reactive metabolites, our method provides a rapid screening tool for a key drug toxicity risk. The XenoSite quinone formation model is available at http://swami.wustl.edu/xenosite/p/quinone.

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“…5 RSP had an average prediction accuracy of 84% for 680 substrates distributed among nine specific CYP isozymes. The prediction accuracy of a given method was gauged using a traditional metric, whereby a substrate is considered to have been correctly predicted by a method if any of its experimentally verified SOM(s) are predicted by that method in the top two rankpositions.…”

Section: ■ Introductionmentioning

confidence: 99%

XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks

Zaretzki

Matlock

Swamidass

2013

J. Chem. Inf. Model.

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189

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Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

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RS-Predictor Models Augmented with SMARTCyp Reactivities: Robust Metabolic Regioselectivity Predictions for Nine CYP Isozymes

Cited by 73 publications

References 67 publications

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

Deep Learning to Predict the Formation of Quinone Species in Drug Metabolism

XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks

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