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.

中文翻译：

---

有信心的决策：应用于固态分子的构象抽样。

分子的正确构象对于可靠地预测其性质至关重要，因此好的预测模型需要好的构象。在这里，我们提出了一种基于距离几何的构象体采样方法，在构象生成器OMEGA中实现，我们将其应用于大环化合物和类药物分子。我们以通常的方式对其进行验证，从固态复制构象，然后将其性能与其他方法进行详细比较。我们发现OMEGA在三个关键标准上表现良好：准确性，速度和合奏大小。为了定量地支持我们的结论，尤其是关于准确性的结论，我们开发了一种方法比较的工作流程，该工作流程使用参数估计，从置信区间进行推断，经典的假设假设显着性检验，贝叶斯估计和效应大小。该工作流旨在对验证计算化学工具时经常发现的高度偏斜的性能数据具有鲁棒性，并提供可靠，易于解释的结果。在此工作流程中，我们强调了自信地区分方法的重要性，特别是涉及样本大小和统计功效（假阴性或II型错误率）的先验估计，这是迄今为止在计算化学领域几乎完全被忽略的话题。