Predicts the sites in molecules that are most liable to cytochrome P450 mediated metabolism. It has been shown to be applicable to metabolism by the isoforms 1A2, 2A6, 2B6, 2C8, 2C19, 2E1, and 3A4 (CYP3A4), and specific models for the isoform 2C9 (CYP2C9) and isoform 2D6 (CYP2D6) are included from version 2.1.

Provides models allowing to predict sites of metabolism. FAME is a machine learning method that relies on descriptors based on 2D representation of a molecule and uses a random forest (RF) model to facilitate predictions. The software uses a slightly modified version of the visualization implemented in SMARTCyp. It can be used by researchers with confidential data as no transmission of such to external sources is required.

A freely available web-server SOMP to predict the sites of metabolism (SOM) based on the structural formula of chemicals has been developed. It is based on the analyses of “structure-SOM” relationships using a Bayesian approach and Labeled Multilevel Neighbourhoods of Atoms (LMNA) descriptors to represent the structures of over 1000 metabolized xenobiotics. The server allows predicting SOMs that are catalysed by 1A2, 2C9, 2C19, 2D6 and 3A4 isoforms of cytochrome P450 and enzymes of the UDP-glucuronosyltransferase family. The average Invariant Accuracy of Prediction that was calculated for the training sets (using leave-one-out cross-validation) and evaluation sets is 0.9 and 0.95, respectively.

A publicly available server for fast web-based prediction of cytochrome P450 (CYP)-mediated metabolism on user-submitted molecules. RS-WebPredictor predicts site of metabolism for nine isozymes, and uses models trained on the largest collection of CYP substrate and metabolite data publicly available. Server execution time is fast, taking on average 2s to encode a submitted molecule and 1s to apply a given model, allowing for high-throughput use in lead optimization projects.

Predicts metabolic transformations related to cytochrome and flavin-containing monooxygenase (FMO) mediated reactions in phase I metabolism. MetaSite is a computational procedure that considers both enzyme-substrate recognition, which is a thermodynamic factor, and the chemical transformations induced by the enzyme, which is a kinetic factor. It gives unprecedented prediction performance, and has now been updated to include the major FMO isoform (FMO3). Key features include the automatic suggestion of fragment modification to optimize specific metabolic issues (MetaDesign), and an interaction map of the substrate with the enzyme cavity (32D) to aid optimisation in the context of the enzyme.

Provides a method implemented as a freely available web service. RA is a web-application for in silico prediction of reacting atoms. Prediction of sites of transformation for drug-like compounds for nine classes of reactions: aliphatic hydroxylation, aromatic hydroxylation, C-oxidation, N-oxidation, S-oxidation, N-glucuronidation, O-glucuronidation, N-dealkylation, O-dealkylation. Predictions are based on PASS (Prediction of Activity Spectra for Substances) technology.

Consists of multiple models for the most important P450 oxidation reactions such as aliphatic hydroxylation, N-dealkylation, O-dealkylation, aromatic hydroxylation, double bond oxidation, N-oxidation, and S-oxidation. CypScore is an in-silico approach for predicting the likely sites of cytochrome P450-mediated metabolism of druglike organic molecules. It is considered as a non-specific oxidation enzyme, which might be compared with the outcome of a microsomal Metabol-ID assay.

A computational protocol which comprises docking of ligands to heme-containing CYPs. SOM Prediction also offers prediction of binding energies through a newly developed scoring function, followed by analyses of the docked structures and molecular orbitals of the ligand molecules, for predicting the sites of metabolism (SOM) of ligands. The present methodology of SOM prediction could help drug designers at the stage of lead optimization.

Provides equally accurate results in predicting properties for molecules with novel scaffolds as for analogs of well-known drugs. QikProp facilitates decisions about a molecule's suitability with the prediction of pharmaceutically relevant properties (octanol/water and water/gas log Ps, log S, log BB, overall CNS activity, Caco-2 and MDCK cell permeabilities, log Khsa for human serum albumin binding, and log IC50 for HERG K+-channel blockage). It bases its predictions on the full 3D molecular structure; unlike fragment-based approaches.

Predicts the regioselectivity of metabolism by seven of the major drug metabolising isoforms of Cytochrome P450 enzymes. StarDrop P450 metabolism prediction module identifies the sites at which metabolism is likely to occur if the compound is a substrate of that isoform. In addition, predictions of the resulting metabolite structures aid metabolite ID and analysis of the properties of potential metabolites. It enables to predict metabolic vulnerability for the P450 isoforms (CYP3A4, CYP2D6, CYP2C9, CYP1A2, CYP2C19, CYP2C8, CYP2E1)

Predicts over 140 properties including solubility, logP, pKa, sites of CYP metabolism, and Ames mutagenicity. ADMET Predictor is state of the art ADMET property prediction software. The ADMET Modeler™ module in ADMET Predictor allows one to rapidly and easily create high quality QSAR/QSPR models based on your own data. The program has an intuitive user interface that allows one to easily manipulate and visualize data. This software is proposed by Simulation Plus.

Predicts first pass metabolic pathways by quickly identifying sites on the molecule where Phase II metabolic transformations (in other words, conjugation) may occur. MEXAlert was developed to be an ideal assistant for high-throughput screening. It is a rule based system; the rules are selected from among the Phase II transformations in the animal knowledge base, and modified according to in vivo experimental examples of first-pass effect pathways. Results are presented in tabular format, prepared to be ready to export to any spreadsheet editor.

Offers a uniform interface for the prediction of ADME (Absorption, Distribution, Metabolism, and Excretion), toxicological, and physicochemical property endpoints. ACD/Percepta Platform investigates and characterizes molecules through property prediction, and analyzes and interprets results to guide future work. It offers a single interface for the analysis and interpretation of predicted data. A web based application is also available. The portal offers an off-the-shelf browser-based solution for deployment of Percepta property prediction and decision-making tools and a fully supported alternative to organizations seeking to replace in-house prediction dashboards.

Predicts xenobiotic metabolism through data-mining and statistical analysis of known metabolic transformations. Two versions of the tool are available: MetaPrint2D (which predicts sites of phase I metabolism, defined as the addition of oxygen or elimination reactions) and MetaPrint2D-React (which can make predictions concerning a wider range of reactions, and is able to predict the types of transformation that can take place at ease site of metabolism, and the likely metabolite formed.)

Predicts and visualizes human cytochromes P450 (CYP450)-mediated sites of metabolism (SOM) of a molecule, on the basis of a previously reported model. SOMEViz provides an access for predicting SOM of molecules with reasonable accuracy, and predicted results are shown in a user-friendly as well as interactive way, which may help chemists explore metabolism properties of chemicals in the early stage of drug discovery. The web-based graphical user interface (GUI) of SOMEViz offers user a straightforward way to manage and visualize the SOM prediction results.