Driven by the wealth and diversity of bond, angle and torsion information in the Cambridge Structural Database, the CSD Conformer Generator produces realistic ensembles of low energy ligand structures.

These are ready to be exploited for drug design in the presence and also in the absence of detailed knowledge about the three-dimensional structure of the protein active site.

For materials scientists the ability to generate likely conformers of a molecule is also particularly useful when assessing the 3D interaction preferences prior to the observation of a solid state structure of that molecule, or when performing an in silico multi-component screen based on molecular complementarity.

  • The CSD Conformer Generator provides the ability to both minimise molecular conformations and also generate diverse conformer subsets based on CSD data.
  • The methodology starts from an input 3D molecular structure with all hydrogen atoms present, which is optionally minimised in the first step. After that, conformations are sampled based on CSD-derived rotamer distributions and ring templates. A final diverse set of conformers, clustered according to conformer similarity, is returned. Each conformer is locally optimised in torsion space.
  • The CSD Conformer Generator is available through the CSD-Materials and CSD-Discovery menus in Mercury, or via a command line utility, or using the CSD Python API.

This application is available to users of CSD-Discovery, CSD-Materials and CSD-Enterprise.

Knowledge-Based Libraries for Predicting the Geometric Preferences of Druglike Molecules. Robin Taylor, Jason Cole, Oliver Korb, and Patrick McCabe. J. Chem. Inf. Model., 2014, 54 (9), pp 2500–2514 DOI: 10.1021/ci500358p

Knowledge-Based Conformer Generation Using the Cambridge Structural Database. Jason C. Cole, Oliver Korb, Patrick McCabe, Murray G. Read, and Robin Taylor. J. Chem. Inf. Model., 2018, 58 (3), pp 615-629 DOI: 10.1021/acs.jcim.7b00697