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 sp(2) 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.