A Chemoinformatic Exploration of the Chemical Space of Natural Steroids.

Chemoinformatic tools have been widely used to analyze the properties of large sets of natural compounds, mostly in the context of drug discovery. Nevertheless, fewer reports have aimed to answer basic biological questions. In this work, we have applied unsupervised machine learning techniques to assess the diversity and complexity of a set of natural steroids by characterizing them through simple topological and physicochemical molecular descriptors. As a most noteworthy result, these properties, derived from the molecular graphs of the compounds, are closely related to their biological functions and to their biosynthetic origins. Moreover, a trend paralleling diversification of the properties and metabolic evolution can be established, demonstrating the potential contribution of these computational approaches to better understanding the vast wealth of natural products.