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Metabolite prediction software tools | Drug discovery data analysis

Predictions of potential metabolites based on chemical structure are becoming increasingly important in drug discovery to guide medicinal chemistry efforts that address metabolic issues and to support experimental metabolite screening and identification.

Source text:
(Ridder and Wagener, 2008) SyGMa: combining expert knowledge and empirical scoring in the prediction of metabolites. ChemMedChem.

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Meteor Nexus
Provides a knowledge-based software that gives users accurate metabolism predictions quickly. Meteor Nexus is a cost-effective solution for that provides information for decision-making when there is little or no experimental metabolism data available. It helps to interpret data from mass spectrometry (MS) metabolism studies quickly and accurately. Furthermore, it shows predicted biotransformation and metabolite information graphically, and shows extensive supporting data from the knowledge base.
PPS / EAWAG-BBD Pathway Prediction System
Predicts plausible pathways for microbial degradation of chemical compounds. PPS uses biotransformation rules, based on reactions found in the EAWAG-BBD database or in the scientific literature. PPS predictions are most accurate for compounds that are: (i) similar to compounds whose biodegradation pathways are reported in the scientific literature; (ii) in environments exposed to air, in moist soil or water, at moderate temperatures and pH, with no competing chemicals or toxins; and (iii) the sole source of energy, carbon, nitrogen, or other essential element for the microbes in these environments, rather than present in trace amounts. Users can choose if they will view all or only the more likely aerobic transformations. The PPS uses Chemaxon's MarvinSketch and MarvinView Java applets as plugins.
SyGMa / Systematic Generation of potential Metabolites
Predicts the potential metabolites of a given parent structure. SyGMa uses a set of reaction rules covering a broad range of phase 1 and phase 2 metabolism has been derived from metabolic reactions reported in the Metabolite Database to occur in humans. An empirical probability score is assigned to each rule representing the fraction of correctly predicted metabolites in the training database. This score is used to refine the rules and to rank predicted metabolites. The rule set of SyGMa covers approximately 70 % of biotransformation reactions observed in humans.
Predicts drug-likelihood of a compound. DrugMint is a predictive model that allows users to interactively draw/modify a molecule using a Marvin applet. It incorporates different modules to support the variation in the input file provided by the user: (i) “Draw Structure” were user can sketch molecule of interest, (ii) "Virtual Screening" used for screening a chemical library and (iii) "Analog Design" for generating different analogs of a chemical scaffold via using user defined linkers and R-groups.
Predicts the metabolic and degradation products of chemical compounds. Meta-PC performs an expert evaluation of the chemical structure of the query compound and does not need prior test data about the actual metabolism of the compound. Meta-PC comes with four powerful dictionaries: Mammalian Metabolism, Aerobic Microbial Biodegradation, Anaerobic Microbial Biodegradation and Photo Degradation Dictionary. By selecting a particular dictionary users can change the capabilities of Meta-PC for a more targeted use.
Predicts the metabolic fate of a compound in the drug discovery process or during the dispositional research phase. MetabolExpert also helps the analysis of metabolic experiments. CompuDrug's MetabolExpert is a tool for initial estimation of the structural formula of metabolites, which might be formed by a substance in humans, animals or in plants. It is a rule-based system with open architecture, in other words, the chemists, metabolism researchers, drug disposition experts and environmental managers can understand, expand, modify or optimize the data on which the metabolic structural estimation relies. The input of the structural transformation is facilitated by an easy-to-use graphical interface, and the metabolic tree graph is displayed in a way that is especially suitable for reporting.
Predicts xenobiotic metabolites and identifies major metabolites. Metabolizer is an in silico metabolic pathway prediction tool, which can be a unique assistant in pre-clinical drug discovery and toxicology studies. Metabolizer is capable of predicting the major and minor metabolites and estimating their metabolic stability. The enumeration process of the metabolites is based on a manually curated knowledge base that can be further extended or even replaced for alternative purposes. Metabolizer provides a graphical user interface that allows extension of selected branches of the metabolic tree, investigation of major pathways and browsing metabolic products. The interface also provides rich export and import options for reporting and analysis of results.
Incorporates curated information on biological effects of small molecule compounds. MetaDrug predictions rely on manually curated information about compound targets, metabolic fate, ADME properties, and therapeutic and side effects. Nearly 6,000 human proteins are covered by compound information. Every target in MetaDrug comes with protein interactions to explore biological pathways affected by the user’s compounds and network neighborhood of drug targets. OMICs data analysis capabilities provide an additional approach for solving the compound’s mechanisms of action, discovering drug efficacy biomarkers, and corroborating the hypotheses generated by classical structure-based methods.
Predicts metabolic transformations. MetaPrint2D-React which can make predictions concerning a wide range of reactions, is able to predict the types of transformation that can take place at ease site of metabolism, and the likely metabolite formed. MetaPrint2D-React is an experimental version of MetaPrint2D, a tool that predicts xenobiotic metabolism through data-mining and statistical analysis of known metabolic transformations reported in scientific literature. The two versions are freely available as web applications.
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