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Decisions with Confidence: Application to the Conformation Sampling of Molecules in the Solid State

Accurate conformations of a molecule are critical for reliable prediction of its properties, so good predictive models require good conformations. Here we present a method for conformer sampling based on distance geometry, implemented in our conformation generator OMEGA, which we apply to both macrocycles and drug-like molecules. We validate it in the usual fashion, reproducing conformations from the solid-state, and compare its performance in detail to other methods. We find that OMEGA performs well on three key criteria: accuracy, speed and ensemble size. To support our conclusions quantitatively, particularly on accuracy, we developed a workflow for method comparison that uses parameter estimation, inference from confidence intervals, classical null hypothesis significance testing, Bayesian estimation, and effect size that is designed to be robust to the highly skewed performance data often found when validating tools in computational chemistry. In this workflow we emphasise the importance of sample size and a priori estimation of statistical power (false negative or Type II error rate), a topic uniformly ignored hitherto in computational chemistry.