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 druglike 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. The workflow is designed to
be robust to the highly skewed performance data often found when validating
tools in computational chemistry and to provide reliable, easy to
interpret results. In this workflow, we emphasize the importance of
confidently distinguishing between methods, with particular reference
to a priori estimation of sample size and statistical power (false
negative or Type II error rate), a topic almost completely ignored
hitherto in computational chemistry.