Identification of Natural Flavonoids Targeting PLK-1 as Potential Anti-Metastatic Agents: A Computational Approach.

This study combines ligand- and structure-based in silico strategies to predict the inhibitory activity of natural flavonoids on the Polo-Like Kinase-1 (PLK-1) enzyme as candidate anticancer agents. This enzyme participates in mitosis and is overexpressed in cancer cells. Furthermore, it has been shown to have important implications for tumor metastasis, and its inhibitors are attractive starting points for drug development. First, classification models are developed using linear discriminant analysis and a multilayer perceptron neural network. Models with accuracy greater than 80%, validated using standard statistical performance metrics and applicability domain, are used for virtual screening identifying four compounds as potential antitumor drugs. Subsequently, the identified compounds are evaluated using a molecular docking methodology to verify their binding mode and interactions with the catalytic domain of PLK-1. Finally, the integration of molecular dynamics simulations, at 300 ns, with Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) thermodynamic calculations demonstrates that the hydroxylation pattern of ring B in the flavonol scaffold is the fundamental chemical-structural determinant of electrostatic interactions and the architecture of water-mediated networks. Among the evaluated flavonoids, myricetin showed the most favorable overall computational profile, including the highest virtual-screening score and the most favorable mean MM/GBSA estimate, supporting its prioritization for experimental evaluation as a potential PLK-1 inhibitor. The integration of these approaches offers a robust methodological framework for proposing candidates with a higher probability of success, in subsequent stages of experimental validation, reducing time and costs in the early stages of drug development.