Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation

Small
organic molecules are often flexible, i.e., they can adopt
a variety of low-energy conformations in solution that exist in equilibrium
with each other. Two main search strategies are used to generate representative
conformational ensembles for molecules: systematic and stochastic.
In the first approach, each rotatable bond is sampled systematically
in discrete intervals, limiting its use to molecules with a small
number of rotatable bonds. Stochastic methods, on the other hand,
sample the conformational space of a molecule randomly and can thus
be applied to more flexible molecules. Different methods employ different
degrees of experimental data for conformer generation. So-called knowledge-based
methods use predefined libraries of torsional angles and ring conformations.
In the distance geometry approach, on the other hand, a smaller amount
of empirical information is used, i.e., ideal bond lengths, ideal
bond angles, and a few ideal torsional angles. Distance geometry is
a computationally fast method to generate conformers, but it has the
downside that purely distance-based constraints tend to lead to distorted
aromatic rings and sp2 centers. To correct this, the resulting
conformations are often minimized with a force field, adding computational
complexity and run time. Here we present an alternative strategy that
combines the distance geometry approach with experimental torsion-angle
preferences obtained from small-molecule crystallographic data. The
torsional angles are described by a previously developed set of hierarchically
structured SMARTS patterns. The new approach is implemented in the
open-source cheminformatics library RDKit, and its performance is
assessed by comparing the diversity of the generated ensemble and
the ability to reproduce crystal conformations taken from the crystal
structures of small molecules and protein–ligand complexes.