Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints.

Molecule and atom fingerprints, similar to path-based Daylight fingerprints, can substantially improve the accuracy of P450 site-of-metabolism prediction models. Only two chemical fingerprints have been used in metabolism prediction, so little is known about the importance of fingerprint parameters on site of metabolism predictions. It is possible that different fingerprints might yield more accurate models.

Extending P450 site-of-metabolism models with region-resolution data.

Motivation: Cytochrome P450s are a family of enzymes responsible for the metabolism of approximately 90% of FDA-approved drugs. Medicinal chemists often want to know which atoms of a molecule-its metabolized sites-are oxidized by Cytochrome P450s in order to modify their metabolism. Consequently, there are several methods that use literature-derived, atom-resolution data to train models that can predict a molecule's sites of metabolism.

DR-predictor: incorporating flexible docking with specialized electronic reactivity and machine learning techniques to predict CYP-mediated sites of metabolism.

Computational methods that can identify CYP-mediated sites of metabolism (SOMs) of drug-like compounds have become required tools for early stage lead optimization. In recent years, methods that combine CYP binding site features with CYP/ligand binding information have been sought in order to increase the prediction accuracy of such hybrid models over those that use only one representation. Two challenges that any hybrid ligand/structure-based method must overcome are (1) identification of the best binding pose for a specific ligand with a given CYP and (2) appropriately incorporating the results of docking with ligand reactivity.

XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

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.

Scaffold network generator: a tool for mining molecular structures.

Summary: Scaffold network generator (SNG) is an open-source command-line utility that computes the hierarchical network of scaffolds that define a large set of input molecules. Scaffold networks are useful for visualizing, analysing and understanding the chemical data that is increasingly available through large public repositories like PubChem. For example, some groups have used scaffold networks to identify missed-actives in high-throughput screens of small molecules with bioassays.

RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules.

Summary: Regioselectivity-WebPredictor (RS-WebPredictor) is a server that predicts isozyme-specific cytochrome P450 (CYP)-mediated sites of metabolism (SOMs) on drug-like molecules. Predictions may be made for the promiscuous 2C9, 2D6 and 3A4 CYP isozymes, as well as CYPs 1A2, 2A6, 2B6, 2C8, 2C19 and 2E1. RS-WebPredictor is the first freely accessible server that predicts the regioselectivity of the last six isozymes.

RS-Predictor models augmented with SMARTCyp reactivities: robust metabolic regioselectivity predictions for nine CYP isozymes.

RS-Predictor is a tool for creating pathway-independent, isozyme-specific, site of metabolism (SOM) prediction models using any set of known cytochrome P450 (CYP) substrates and metabolites. Until now, the RS-Predictor method was only trained and validated on CYP 3A4 data, but in the present study, we report on the versatility the RS-Predictor modeling paradigm by creating and testing regioselectivity models for substrates of the nine most important CYP isozymes. Through curation of source literature, we have assembled 680 substrates distributed among CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1, and 3A4, the largest publicly accessible collection of P450 ligands and metabolites released to date.

Fast bundle algorithm for multiple-instance learning.

We present a bundle algorithm for multiple-instance classification and ranking. These frameworks yield improved models on many problems possessing special structure. Multiple-instance loss functions are typically nonsmooth and nonconvex, and current algorithms convert these to smooth nonconvex optimization problems that are solved iteratively.

RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set.

SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism.

SMARTCyp is an in silico method that predicts the sites of cytochrome P450-mediated metabolism of druglike molecules. The method is foremost a reactivity model, and as such, it shows a preference for predicting sites that are metabolized by the cytochrome P450 3A4 isoform. SMARTCyp predicts the site of metabolism directly from the 2D structure of a molecule, without requiring calculation of electronic properties or generation of 3D structures.