Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug candidates are usually analogous to or derived from the molecular structures of NC. In order to express the relationship physically realistically using a computer, it is essential to have a molecular descriptor set that can adequately represent the characteristics of the molecular structures belonging to the NC’s chemical space. Although several topological descriptors have been developed to describe the physical, chemical, and biological properties of organic molecules, especially synthetic compounds, and have been widely used for drug discovery researches, these descriptors have limitations in expressing NC-specific molecular structures. To overcome this, we developed a novel molecular fingerprint, called Natural Compound Molecular Fingerprints (NC-MFP), for explaining NC structures related to biological activities and for applying the same for the natural product (NP)-based drug development. NC-MFP was developed to reflect the structural characteristics of NCs and the commonly used NP classification system. NC-MFP is a scaffold-based molecular fingerprint method comprising scaffolds, scaffold-fragment connection points (SFCP), and fragments. The scaffolds of the NC-MFP have a hierarchical structure. In this study, we introduce 16 structural classes of NPs in the Dictionary of Natural Product database (DNP), and the hierarchical scaffolds of each class were calculated using the Bemis and Murko (BM) method. The scaffold library in NC-MFP comprises 676 scaffolds. To compare how well the NC-MFP represents the structural features of NCs compared to the molecular fingerprints that have been widely used for organic molecular representation, two kinds of binary classification tasks were performed. Task I is a binary classification of the NCs in commercially available library DB into a NC or synthetic compound. Task II is classifying whether NCs with inhibitory activity in seven biological target proteins are active or inactive. Two tasks were developed with some molecular fingerprints, including NC-MFP, using the 1-nearest neighbor (1-NN) method. The performance of task I showed that NC-MFP is a practical molecular fingerprint to classify NC structures from the data set compared with other molecular fingerprints. Performance of task II with NC-MFP outperformed compared with other molecular fingerprints, suggesting that the NC-MFP is useful to explain NC structures related to biological activities. In conclusion, NC-MFP is a robust molecular fingerprint in classifying NC structures and explaining the biological activities of NC structures. Therefore, we suggest NC-MFP as a potent molecular descriptor of the virtual screening of NC for natural product-based drug development.

中文翻译：

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使用天然产物字典（DNP）开发天然化合物分子指纹（NC-MFP），用于基于天然产物的药物开发

由于新候选药物的分子结构通常类似于或衍生自NC的分子结构，因此对天然化合物（NC）的分子结构与其生物学活性之间的关系进行了计算机辅助研究。为了使用计算机从物理上现实地表达这种关系，必须具有一个分子描述符集，该描述符集可以充分表示属于NC化学空间的分子结构的特征。尽管已经开发了几种拓扑描述符来描述有机分子（尤其是合成化合物）的物理，化学和生物学特性，并且已广泛用于药物发现研究，但是这些描述符在表达NC特定分子结构方面存在局限性。为了克服这个问题，我们开发了一种新颖的分子指纹，称为天然化合物分子指纹（NC-MFP），用于解释与生物活性有关的NC结构，并将其应用于基于天然产物（NP）的药物开发中。开发NC-MFP以反映NC的结构特征和常用的NP分类系统。NC-MFP是一种基于支架的分子指纹方法，包括支架，支架-片段连接点（SFCP）和片段。NC-MFP的支架具有分层结构。在这项研究中，我们在天然产物字典（DNP）中引入了NP的16个结构类别，并使用Bemis和Murko（BM）方法计算了每个类别的分级支架。NC-MFP中的支架库包含676个支架。为了比较与广泛用于有机分子表示的分子指纹相比，NC-MFP表现NC的结构特征的能力，执行了两种二元分类任务。任务I是将市售库DB中的NC二进制分类为NC或合成化合物。任务II是对在七个生物靶蛋白中具有抑制活性的NCs是有活性还是无活性进行分类。使用1-最近邻居（1-NN）方法开发了带有某些分子指纹的两项任务，包括NC-MFP。任务1的执行结果表明，与其他分子指纹相比，NC-MFP是一种实用的分子指纹，可从数据集中对NC结构进行分类。与其他分子指纹相比，具有NC-MFP的任务II的性能要好，提示NC-MFP有助于解释与生物活性有关的NC结构。总之，NC-MFP是对NC结构进行分类并解释NC结构的生物学活性的强大分子指纹。因此，我们建议将NC-MFP作为基于天然产物药物开发的NC虚拟筛选的有效分子描述符。