论文标题
有效的半胱氨酸构象与贝叶斯优化的搜索
Efficient Cysteine Conformer Search with Bayesian Optimization
论文作者
论文摘要
由于搜索空间的高维度以及用于确定构象异构体结构和能量的精确量子化学方法的计算成本，因此找到低能分子构象体的计算成本是有挑战性的。在这里，我们将主动学习的贝叶斯优化（BO）算法与量子化学方法相结合，以应对这一挑战。以半胱氨酸为例，我们表明我们的过程既有效又准确。经过一千个单点计算和大约30个结构弛豫（当前最快方法的计算成本少于10％），我们发现低能构象体与实验测量和参考计算非常吻合。
Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate. After only one thousand single-point calculations and approximately thirty structure relaxations, which is less than 10% computational cost of the current fastest method, we have found the low-energy conformers in good agreement with experimental measurements and reference calculations.
