Partitioning Pattern of Natural Products Based on Molecular Properties Descriptors Representing Drug-Likeness

A cheminformatics procedure for a partitioning model based on 135 natural compounds including Flavonoids, Saponins, Alkaloids, Terpenes and Triterpenes with drug-like features based on a descriptors pool was developed. The knowledge about the applicability of natural products as a unique source for the development of new candidates towards deadly infectious disease is a contemporary challenge for drug discovery. We propose a partitioning scheme for unveiling drug-likeness candidates with properties that are important for a prompt and efficient drug discovery process. In the present study, the vantage point is about the matching of descriptors to build the partitioning model applied to natural compounds with diversity in structures and complexity of action towards the severe diseases, as the actual SARS-CoV-2 virus. In the times of the de novo design techniques, such tools based on a chemometric and symmetrical effect by the implied descriptors represent another noticeable sign for the power and level of the descriptors applicability in drug discovery in establishing activity and target prediction pipeline for unknown drugs properties.

Automated summary

This study focuses on utilizing descriptors to build a partitioning model for the application of natural compounds in drug discovery, especially in dealing with severe diseases. The research emphasizes the importance of matching descriptors to natural compounds with diverse structures and complex actions towards deadly infectious diseases like the SARS-CoV-2 virus.