Abstract
In this study we compare the three algorithms for the generation of conformer ensembles Biovia BEST, Schrödinger Prime macrocycle sampling (PMM) and Conformator (CONF) form the University of Hamburg, with ensembles derived for exhaustive molecular dynamics simulations applied to a dataset of 7 small macrocycles in two charge states and three solvents. Ensemble completeness is a prerequisite to allow for the selection of relevant diverse conformers for many applications in computational chemistry. We apply conformation maps using principal component analysis based on ring torsions. Our major finding critical for all applications of conformer ensembles in any computational study is that maps derived from MD with explicit solvent are significantly distinct between macrocycles, charge states and solvents, whereas the maps for post-optimized conformers using implicit solvent models from all generator algorithms are very similar independent of the solvent. We apply three metrics for the quantification of the relative covered ensemble space, namely cluster overlap, variance statistics, and a novel metric, Mahalanobis distance, showing that post-optimized MD ensembles cover a significantly larger conformational space than the generator ensembles, with the ranking PMM > BEST >> CONF. Furthermore, we find that the distributions of 3D polar surface areas are very similar for all macrocycles independent of charge state and solvent, except for the smaller and more strained compound 7, and that there is also no obvious correlation between 3D PSA and intramolecular hydrogen bond count distributions.
Highlights
Molecules at ambient conditions are flexible fluctuating three-dimensional objects composed of atoms held together by electrons
The relevance of 3D conformation-based machine learning recently sees a revival triggered by so-called beyond rule of five compounds [6] and the observation that many ADMET properties of compounds rely on conformational flexibility determined inter alia by intramolecular hydrogen bonding [7]
In this work we provide a thorough investigation on the multiple parameters that determine the resulting conformer ensembles from molecular dynamics simulations and from three algorithms for the generation of conformers for seven small macrocycles resulting from a collaboration with the University of Sherbrooke
Summary
Molecules at ambient conditions are flexible fluctuating three-dimensional objects composed of atoms held together by electrons. The relevance of 3D conformation-based machine learning recently sees a revival triggered by so-called beyond rule of five compounds [6] and the observation that many ADMET properties of compounds rely on conformational flexibility determined inter alia by intramolecular hydrogen bonding [7]. One such descriptor derived for modeling solvation free energies is the MDFP by Riniker [8] which allows to transfer information from a molecular dynamics simulation in one solvent to another solvent and to derive distribution coefficients. Clever descriptions of three-dimensional features of molecules will certainly constitute one approach towards the improvement of in silico ADMET and other ML models
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