Programming Library for Chemistry and Cheminformatics
OEChem TK is a programming library for chemistry and cheminformatics that is fast and flexible. OEChem TK has many simple yet powerful functions that handle the details of working with small molecules, as well as an expanding number of functions for dealing with proteins. High-level functions provide simplicity while low-level functions provide flexibility.
OEChem TK incorporates the chemistry models used by the main software providers.
- Facile management of molecules, atoms, bonds, and conformers
- Conformational and frame-of-reference coordinate transformations
- Maximum common substructure
- Substructure searching based on SMARTS or MDL query
- Perception of aromaticity with multiple models
- Chemical reaction parsing and processing
- Library generation based on SMIRKS or MDL reaction
- Tetrahedral and E/Z stereochemistry recognition
- CIP atom and bond stereo perception
- Ring perception and Kekulization
- Molecular canonicalization
- Multiconformer molecule handling
- Ability to store and recall generic primitives or user-defined objects on molecules, atoms, bonds, or conformers
One advantage of robust multiple chemistry perception is data integrity: OEChem TK is able to navigate through file formats with no loss of information. The table below shows the common molecule file formats supported by OEChem.
|Protein Databank PDB||Yes||Yes|
|Tripos Sybyl mol2||Yes||Yes|
|Canonical isomeric SMILES||Yes||Yes|
|FASTA protein sequence||Yes||Yes|
The OEChem TK also includes two sub-libraries designed to handle macromolecules (OEBio) and grids (OEGrid).
Key features of OEBio:
- protein residue, the primary, secondary and tertiary structure hierarchy perception
- crystal symmetry handling
- sequence alignment
- management of torsions, rotamer libraries, and alternate conformations
Key features of OEGrid:
- support for the following grid file formats: Grasp, GRD (OpenEye Binary format), CCP4, XPLOR
- Optimizing Fragment and Scaffold Docking by Use of Molecular Interaction Fingerprints Gilles Marcou, Didier Rognan. J. Chem. Inf. Model., 2007, 47 (1), 195-207.
- Database Clustering with a Combination of Fingerprint and Maximum Common Substructure Methods Martin Stahl, Harald Mauser. J. Chem. Inf. Model., 2005, 45 (3), 542-548.