Bayesian optimization for conformer generation
Bayesian optimization for conformer generation
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DOI：
10.1186/s13321-019-0354-7
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发表时间：
2019-05-21
影响因子：
8.6
通讯作者：
Morris, Garrett M.
中科院分区：
文献类型：
--
作者：
Chan, Lucian;Hutchison, Geoffrey R.;Morris, Garrett M.
生成低能分子构象异构体是计算化学、分子建模和化学信息学许多领域的一项关键任务。目前大多数构象异构体生成方法主要侧重于生成几何上不同的构象异构体，而不是寻找最可能或能量最低的最小值。在这里，我们提出了一种新的随机搜索方法，称为贝叶斯优化算法（BOA），用于寻找给定分子的最低能量构象。我们将 BOA 与均匀随机搜索以及 Confab 中实现的系统搜索进行比较，以确定哪种方法找到最低能量。能量差、均方根偏差和扭转指纹偏差用于量化构象搜索算法的性能。一般来说，我们发现 BOA 需要比系统或均匀随机搜索少得多的评估来找到低能量最小值。对于具有四个或更多可旋转键的分子，Confab 通常在搜索中评估 104 个（中值）构象异构体，而 BOA 只需要 102 个能量评估即可找到最佳候选者。尽管使用评估的构象异构体较少，但与系统性 Confab 搜索具有四个或更多可旋转键的分子相比，BOA 有 20-40% 的时间发现了较低能量的构象。
Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method called the Bayesian optimization algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation, and torsion fingerprint deviation are used to quantify the performance of the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates 104(median) conformers in its search, while BOA only requires 102 energy evaluations to find top candidates. Despite using evaluating fewer conformers, 20-40% of the time BOA finds lower-energy conformations than a systematic Confab search for molecules with four or more rotatable bonds.