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.

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 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.

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.

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.