Machine Learning Predicts Degree of Aromaticity from Structural Fingerprints

Prediction of whether a compound is “aromatic” is at first glance a relatively simple taskdoes it obey Hückel’s rule (planar cyclic π-system with 4n + 2 electrons) or not? However, aromaticity is far from a binary property, and there are distinct variations in the chemical and biological behavior of different systems which obey Hückel’s rule and are thus classified as aromatic. To that end, the aromaticity of each molecule in a large public dataset was quantified by an extension of the work of Raczyńska et al. Building on this data, a method is proposed for machine learning the degree of aromaticity of each aromatic ring in a molecule. Categories are derived from the numeric results, allowing the differentiation of structural patterns between them and thus a better representation of the underlying chemical and biological behavior in expert and (Q)­SAR systems.判断某化合物是否具有芳香性，乍看之下是一项相对简单的任务——仅需验证其是否符合休克尔规则（Hückel's Rule），即是否拥有由4n+2个电子构成的平面环状π体系。然而，芳香性绝非二元属性；即便同为符合休克尔规则、因而被归类为芳香性的体系，不同体系间的化学与生物学行为也存在显著差异。为此，本研究通过拓展Raczyńska等人的研究方法，对大型公开数据集中每一个分子的芳香性进行了量化表征。基于上述量化数据，本研究提出了一种机器学习方法，用于预测分子中每个芳香环的芳香性程度。从量化结果中衍生出分类体系，借此可区分不同类别间的结构模式，进而能够在专家系统及(Q)SAR（定量结构-活性关系）系统中更精准地展现其内在的化学与生物学行为特征。