使用机器学习方法预测毛茛科的分类分类,New Journal of Chemistry

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School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China

Institute of Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China

毛茛科植物 是多种药用活性化合物的植物来源，在中药中普遍使用。 对毛茛科 药物资源的兴趣日益增加，导致对该科进行分类学研究，这可能为了解其多样性、关系和系统发育位置提供新的见解，并进一步寻找新的药用资源和有前景的化合物。在这项研究中，我们使用机器学习方法来探索药用 毛茛科 的分类。 毛茛科 17属204种家族从 TCMID 中收集到，其 1280 种活性化合物由基于结构的指纹组成。在构建了种-化合物和属-化合物矩阵后，CNNs和Ext指纹分别被确定为最佳机器学习方法和指纹类型，分别使用ACC和 F- score作为聚类标准。我们发现 毛茛科 内的分类学分类可以准确预测，特别是在最高 ACC 为 0.86 和 F- score 为 0.85 的属水平上。还确定了对 17 个属的分类很重要的化合物的主要特征，因此一些具有高药用价值的属与特征 cis 和 (or) 反式特征。据我们所知，这是首次发现某些属与化合物的结构特征有关。 Ranunculaceae is a botanical source for various pharmaceutically active compounds, which has been commonly utilized in traditional Chinese medicine. Increasing interest in Ranunculaceae pharmaceutical resources has led to a taxonomical study of this family, which might provide new insight to understand its diversification, relationship and phylogenetic position, and further to find new medicinal resources and promising compounds. In this study, we used the machine learning method to explore the classification of the medicinal Ranunculaceae family. 204 species representing 17 genera of the Ranunculaceae family were collected from the TCMID with their 1280 active compounds composed of structure-based fingerprints. After the construction of species-compound and genus-compound matrices, CNNs and Ext fingerprints were determined as the best machine learning method and fingerprint type using ACC and F -score as clustering criteria, respectively. We found that taxonomical classification within the Ranunculaceae family could be accurately predicted, especially at the genus level with a top ACC of 0.86 and an F -score of 0.85. The top features of compounds that were important for the classification of 17 genera were also identified, and thus some genera with high medicinal values were associated with characteristic cis and (or) trans features. As far as we know, this is the first time that some genera are found to be associated with the structural features of compounds.