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

Abstract

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.

A sobering assessment of small‐molecule force field methods for low energy conformer predictions

“…Most organic molecules with four or more atoms have some level of conformational flexibility, and even small molecules possess multiple thermally accessible conformer geometries . Although classical force fields are widely used to identify low energy conformers, recent studies have questioned the reliability of classical force field methods . Kaminský and Jensen have also reported detailed benchmarking studies of conformational energies of amino acids, showing limitations of force fields with fixed charges for biomolecular applications .…”

Reactants, Products, and Transition States of Elementary Chemical Reactions Based on Quantum Chemistry

“…All molecules with six or fewer heavy atoms were selected from GDB-7 (∼770) and a random selection of ∼430 molecules were selected from the set with seven heavy atoms. Starting from the SMILES strings, we embedded several hundred conformers for each molecule using the RDKit 25 with the ETKDG distance geometry method 26 and relaxed their geometries using the MMFF94 force field implemented in RD-Kit. The lowest energy structure was selected for each molecule and optimized at both the B97-D3/def2-mSVP with Becke-Johnson damping level of theory 27 and the ωB97X-D3/def2-TZVP 28 level of theory with Q-Chem 5.1 29 .…”

(2R,3S)‐3,4,4,4‐Tetrafluorovaline: A Fluorinated Bioisostere of Isoleucine

“…The solvent accessible surface areas (SASAs) and 1-octanol/water partition coefficients (log Ps) were selected as the descriptors. The conformations for the SASA calculations were generated using the experimentaltorsion with basic knowledge distance geometry (ETKDG) method, [33] and the log P values were estimated by Crippen's atom-based method. [34] The data summarized in Figure 5 indicates that fluorinated amino acid 4 resembles Ile more closely than Val with respect to both SASA and log P. HPLC retention times have previously been used as a convenient and reliable means to quickly estimate the lipophilicities of organic molecules.…”