Development of Natural Compound Molecular Fingerprint (NC-MFP) with the Dictionary of Natural Products (DNP) for natural product-based drug development

Abstract

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

Profiling and analysis of chemical compounds using pointwise mutual information

“…Later, we will also investigate the utility of hybrid feature vectors containing interrelation profiles concatenated with, for example, QAFFP biological fingerprints [63,64] or with other features of interest. We plan to use interrelation profiling in various cheminformatics applications, such as in biological activity classification or potency prediction, focused chemical library construction, diversity data selection or ensemble modeling using RFT together with domain-specific models for, e.g., natural product likeness assessment [65][66][67]. Given that Correlation between ZRFT, SYBA and SAScore.…”

Natural Product Scores and Fingerprints Extracted from Artificial Neural Networks.

“…Datasets For the external validation of the neural fingerprints, three different additional datasets were used. First, the fingerprints were validated on the data provided by Seo et al [11], which consists of two separate tasks. One dataset (in the original work named "Task 1") is concerned with differentiating between synthetic and natural products.…”

“…[9] However, due to the above-mentioned characteristics of natural products many commonly used fingerprints can struggle with the complexity of natural products. [10] Seo and colleagues [11] addressed this issue by creating a natural productspecific fingerprint, which outperforms most regular fingerprints in the NP space. The fingerprint is build upon fragments frequently found in natural products.…”

Profiling and analysis of chemical compounds using pointwise mutual information

“…Later, we will also investigate the utility of hybrid feature vectors containing interrelation profiles concatenated with, for example, QAFFP biological fingerprints [64,65] or with other features of interest. We plan to use interrelation profiling in various cheminformatics applications, such as in biological activity classification or potency prediction, focused chemical library construction, diversity data selection or ensemble modeling using RFT together with domain-specific models for, e.g., natural product likeness assessment [66][67][68]. Given that interrelation profiles are matrices of numeric values, they can also be used to train machine learning models and to identify and leverage specific feature interrelations that provide most information about the estimated property.…”