Taylor Robin, Cole Jason, Korb Oliver, McCabe Patrick
Cambridge Crystallographic Data Centre , 12 Union Road, Cambridge CB2 1EZ, United Kingdom.
J Chem Inf Model. 2014 Sep 22;54(9):2500-14. doi: 10.1021/ci500358p. Epub 2014 Aug 19.
We describe the automated generation of libraries for predicting the geometric preferences of druglike molecules. The libraries contain distributions of molecular dimensions based on crystal structures in the Cambridge Structural Database (CSD). Searching of the libraries is performed in cascade fashion to identify the most relevant distributions in cases where precise structural features are poorly represented by existing crystal structures. The libraries are fully comprehensive for bond lengths, valence angles, and rotamers and produce templates for the large majority of unfused and fused rings. Geometry distributions for rotamers and rings take into account any atom chirality that may be present. Library validation has been performed on a set of druglike molecules whose structures were published after the latest CSD entry contributing to the libraries. Hence, the validation gives a true indication of prediction accuracy.
我们描述了用于预测类药物分子几何偏好的库的自动生成。这些库包含基于剑桥结构数据库（CSD）中晶体结构的分子尺寸分布。库的搜索以级联方式进行，以便在现有晶体结构难以精确表征特定结构特征的情况下识别最相关的分布。这些库对于键长、价角和旋转异构体是完全全面的，并为绝大多数未稠合和稠合环生成模板。旋转异构体和环的几何分布考虑了可能存在的任何原子手性。已对一组类药物分子进行了库验证，这些分子的结构在为库贡献最新CSD条目之后发表。因此，验证给出了预测准确性的真实指示。
