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
Global regulations of biocides have been continuously enhanced for protecting human health and the environment from potentially harmful biocidal products. Such regulations consider the combined toxicity caused by mixture components in a biocidal product of which approval and authorization are to be enhanced. Although the combined exposure scenarios of components in mixtures are firstly needed to conduct the mixture risk assessment, systematic combined exposure scenarios are still lacking. In this study, combined inhalation exposure scenarios of biocides in household chemical and biocidal products marketed in South Korea were investigated based on the European Union (EU) and Korean chemical product databases and various data sources integration. The information of 1058 biocidal products and 675 household chemical products that are likely to cause inhalation exposure with two or more biocides was collected, and mixture combination patterns were investigated. Binary mixtures occupied 72% in biocidal products. The most frequently appearing binary mixture was phthalthrin and d-phenothrin. Based on the frequency of use, we suggested a priority list of biocide mixture combinations which need to be firstly evaluated for identifying their combined toxicity for the mixture risk assessment. This study highlights that the derived combined inhalation exposure scenarios can support and facilitate further studies on priority settings for mixture risk assessment and management of potentially inhalable biocides.
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