Sites of metabolism detection software tools | Drug discovery data analysis

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.

An integrated drug discovery software. MOE is able to track design ideas and ligand modifications with property models, produce correlation plots to visualize Structure, Property, Activity Relationships and visualize hydrophobic and charged protein surface to study aggregation prone regions. It can also automatically align and superpose antibody structures using the MOE Project protocol, generate and search advanced antibody queries with the Project Search application and build full length Ig structures including bispecifics with the Antibody Homology Modeler.

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.

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 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.

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.