A key challenge in conformer sampling is finding low-energy conformations with a small number of energy evaluations. We recently demonstrated the Bayesian Optimization Algorithm (BOA) is an effective method for finding the lowest energy conformation of a small molecule. Our approach balances between exploitation and exploration, and is more efficient than exhaustive or random search methods. Here, we extend strategies used on proteins and oligopeptides (e.g. Ramachandran plots of secondary structure) and study correlated torsions in small molecules. We use bivariate von Mises distributions to capture correlations, and use them to constrain the search space. We validate the performance of our new method, Bayesian Optimization with Knowledge-based Expected Improvement (BOKEI), on a dataset consisting of 533 diverse small molecules, using (i) a force field (MMFF94); and (ii) a semi-empirical method (GFN2), as the objective function. We compare the search performance of BOKEI, BOA with Expected Improvement (BOA-EI), and a genetic algorithm (GA), using a fixed number of energy evaluations. In more than 60% of the cases examined, BOKEI finds lower energy conformations than global optimization with BOA-EI or GA. More importantly, we find correlated torsions in up to 15% of small molecules in larger data sets, up to 8 times more often than previously reported. The BOKEI patterns not only describe steric clashes, but also reflect favorable intramolecular interactions such as hydrogen bonds and pi-pi stacking. Increasing our understanding of the conformational preferences of molecules will help improve our ability to find low energy conformers efficiently, which will have impact in a wide range of computational modeling applications.
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机译：适系采样中的一个关键挑战在少量能量评估中找到低能量符合。我们最近展示了贝叶斯优化算法（BOA）是寻找小分子的最低能量构象的有效方法。我们的方法在开发和探索之间的平衡，比穷举或随机搜索方法更有效。在此，我们扩展了蛋白质和寡肽（例如次结构的ramachandran曲线图）的策略，并研究小分子中的相关扭转。我们使用Bivariate Von MISES分发来捕获相关性，并使用它们来限制搜索空间。我们验证了我们的新方法，贝叶斯优化与基于知识的预期改进（Bokei）的性能，在由533种不同的小分子组成的数据集上，使用（i）力场（MMFF94）; （ii）半经验方法（GFN2），作为目标函数。我们使用固定数量的能量评估进行比较Bokei，BoA的搜索性能，以及预期的改进（Boa-EI）和遗传算法（GA）。在60％以上的案例中检查，Bokei发现与Boa-EI或GA的全球优化的能量构象较低。更重要的是，我们发现在较大数据集中高达15％的小分子中的相关扭转，比以前报道的频率高达8倍。 Bokei模式不仅描述了空间冲突，而且还反映了有利的分子内相互作用，例如氢键和PI-PI堆叠。增加我们对分子构象偏好的理解将有助于提高我们有效地找到低能量符合特派子的能力，这将对各种计算建模应用产生影响。
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