Knowledge-Based Libraries for Predicting the Geometric Preferences of Druglike Molecules

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.