Allows forecast of Cytochrome P450 enzymes (P450s) sites of metabolisms (SOMs). XenoSite is a machine-learning approach to model P450 metabolism using a neural network. It can identify experimentally observed SOMs within the top two rank positions for substrate sets of each P450 isozyme. It also can compute SMARTCyp predictions for P450 metabolism. These predictions are available for downloading in a flat file.
Predicts the ability of drug-like chemicals to inhibit five major drug metabolizing cytochrome P450 enzyme isoforms (CYPs) (i.e. CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4). CYP450model is a unified proteochemometric (PCM) model which was created and made publicly available under the Bioclipse Decision Support open source system. CYP450model was developed based on the assay of 16359 organic compounds towards their inhibition of the five CYPs using PCM modeling, all-in-all the dataset comprising 63391 interactions.
A freely available web resource that enables comparative analyses of drug-disposition genes. ADME SARfari does so by integrating a number of publicly available data sources, which have subsequently been used to build data mining services, predictive tools and visualizations for drug metabolism researchers. The data include the interactions of small molecules with ADME (absorption, distribution, metabolism and excretion) proteins responsible for the metabolism and transport of molecules; available pharmacokinetic (PK) data; protein sequences of ADME-related molecular targets for pre-clinical model species and human; alignments of the orthologues including information on known SNPs (Single Nucleotide Polymorphism) and information on the tissue distribution of these proteins.
Predicts the metabolizing Cytochromes P450 (CYPs) inhibition, including CYP1A2, CYP2C19, CYP2C9, CYP2D6 and CYP3A4. CypRules predicts its results based on C5.0 algorithm. The rules are calculated based on Mold2 PaDEL, and Pubchem 2D-fingerprints descriptors. The suggested ruleset that leads to the predicted outcome will be listed as well. This server will be helpful for researcher working in the field of drug discovery.
Facilitates computational nanoscience. SAMSON allows users to improve design of drugs, materials and nanosystems. It enables expert-in-the-loop design through interactive simulations. This tool permits the exportation of large-scale simulators, and the importation of trajectories to compute properties. It is useful in science and innovation in medicine, energy, materials, electronics, education fields.
A prediction tool for identifying possible transformation products of xenobiotic chemicals in the liver. PROXIMAL builds look-up tables that catalog the sites and types of structural modifications performed by Phase I and Phase II enzymes. It also searches for substructures that match the sites cataloged in the look-up tables, applies the corresponding modifications to generate a panel of possible transformation products, and ranks the products based on the activity and abundance of the enzymes involved.
Allows users to compute physicochemical descriptors as well as predict pharmacokinetics properties and druglike nature of one or multiple small molecules. SwissADME was made for application in drug discovery and medicinal chemistry contexts, which stresses for a balance between accuracy and speed in order to deal with a large number of molecules. Because of the predictive nature of the data returned by SwissADME, values should be handled with due care. Extra precaution should be taken if using SwissADME for any other activities outside the scope of drug design/discovery.
A tool for prediction of which cytochromes P450 isoforms (among 1A2, 2C9, 2C19, 2D6 and 3A4) a given molecule is likely to inhibit. The models are built from experimental high-throughput data using support vector machines and molecular signatures.
A web-server developed for predicting human cytochrome P450 SNPs (Single Nucleotide Polymorphisms) based on the SVM flanking sequence method. SCYPPred can yield the desired results by using the amino acid sequences information alone. It could be a useful bioinformatics tool for elucidating the mutation probability of a specific CYP450 enzyme. This tool can give predicted results only based on the input sequence information with an accuracy of 66.3%, a sensitivity of 65.5%, and a specificity of 66.3%.
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