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

Abstract

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.

Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors

“…A method using the decision trees on the activation energies and solvent accessible surface area calculated from MOE [32–35] has been developed. Recently, the PASS algorithm using some 2D fingerprint descriptors [36] or the RASCAL (Random Attribute Subsampling Classification) algorithm [37] using some circular fingerprints has been employed to predict SOM by CYP450 enzymes [38, 39]. …”

Verifying the fully “Laplacianised” posterior Naïve Bayesian approach and more

“…For example, a cheminformatian might be interested in knowing whether: a particular small compound (ligand) is capable of inducing a desirable biological effect on a specific target protein [1, 2]; an enzyme catalyses a certain chemical reaction, or a catalytic mechanism of an enzyme is appropriate for a chemical reaction [3]; a substructure of a substrate is a site of metabolism [4]; a ligand is structurally similar to a reference set of ligands known to possess desirable physical, chemical and biological properties [2, 5, 6]; a protein is a potential target for a given ligand [2, 7]; etc.…”

Precursors for cytochrome P450 profiling breath tests from an in silico screening approach

“…O-and N-dealkylations suitable for VOC generation are found to yield reaction intermediates of relative high stability [41]. Additionally, we discarded predicted SOMs expected to be too buried for CYP-catalyzed oxidation [40,47,49]. For the 32 analyzed substrates of oxidative degradation the SOMs for dealkylation could be covered in 27 cases.…”

“…Positions where H·-abstraction resulted in an energy difference dE < 30 kcal mol −1 were considered as potential SOMs. As CYP-catalyzed oxidation reactions require accessibility of the SOM [47][48][49], we implemented a second selection criterion in our SOM prediction. Based on the energy-minimized structure from H·-abstraction calculations, we calculated solvent accessible surface areas (SASAs) for all hydrogens using MOE [31].…”