Machine Learning and Artificial Intelligence in Phytoecdysteroid Discovery and Analysis

Phytoecdysteroids (PE) constitute a unique class of naturally occurring steroids exhibiting pronounced anabolic activity and broad pharmacological potential. While quantitative structure–activity relationship (QSAR) approaches have previously been applied to rationalize the biological effects of individual ecdysteroid series, a unified and interpretable modeling framework suitable for knowledge transfer and molecular design remains limited. In this chapter, we present an expanded QSAR-based framework for the analysis of anabolic activity in PE, integrating molecular structure curation, quantum-chemical optimization, descriptor-based feature selection, statistical modeling, and chemical-space interpretation. Building on our previously published open-access study, this work introduces a conceptual modeling framework and a descriptor-space analysis using density distributions to elucidate the physicochemical patterns underlying anabolic potency. The developed framework highlights the roles of molecular symmetry, polarizability, lipophilicity, and topological complexity in governing anabolic activity, while providing chemically interpretable design rules for future ecdysteroid analogs. This chapter aims to serve as a reference workflow for QSAR-driven analysis of natural product scaffolds and as a bridge between statistical modeling and chemically meaningful insight.