Improving Conformer Generation for Small Rings and Macrocycles Based on Distance Geometry and Experimental Torsional-Angle Preferences

The
conformer generator ETKDG is a stochastic search method that
utilizes distance geometry together with knowledge derived from experimental
crystal structures. It has been shown to generate good conformers
for acyclic, flexible molecules. This work builds on ETKDG to improve
conformer generation of molecules containing small or large aliphatic
(i.e., non-aromatic) rings. For one, we devise additional torsional-angle
potentials to describe small aliphatic rings and adapt the previously
developed potentials for acyclic bonds to facilitate the sampling
of macrocycles. However, due to the larger number of degrees of freedom
of macrocycles, the conformational space to sample is much broader
than for small molecules, creating a challenge for conformer generators.
We therefore introduce different heuristics to restrict the search
space of macrocycles and bias the sampling toward more experimentally
relevant structures. Specifically, we show the usage of elliptical
geometry and customizable Coulombic interactions as heuristics. The
performance of the improved ETKDG is demonstrated on test sets of
diverse macrocycles and cyclic peptides. The code developed here will
be incorporated into the 2020.03 release of the open-source cheminformatics
library RDKit.