Molecular Simulation of the Interaction Between Natural Anticancer Compounds and Their Biological Targets

Abstract

Cancer remains a major public health challenge due to its high mortality rate, complex biological mechanisms, and the limitations of conventional therapies, including toxicity, low selectivity, and drug resistance. In this context, natural compounds represent an important source of potential anticancer agents because of their structural diversity and ability to modulate several cancer-related pathways. This study focuses on the molecular simulation of the interaction between selected natural anticancer compounds and key biological targets involved in tumor progression, namely VEGFR2, androgen receptor, BCL-2, PARP1, and CDK4. The objective of this work was to identify promising natural molecules with potential multi-target anticancer activity using computational drug discovery approaches. Protein structures were obtained from the Protein Data Bank, while natural compounds were collected from chemical databases and literature sources. The selected ligands were subjected to ADME/Tox prediction in order to evaluate their pharmacokinetic properties, drug-likeness, and potential toxicity. Molecular docking and cross-docking studies were then performed to estimate ligand–target binding affinities. Post-docking analysis was used to identify the main interactions involved in ligand binding, including hydrogen bonds, hydrophobic interactions, π–π stacking, and key amino acid residues. The results of this in silico study provide valuable insights into the binding behavior of natural compounds toward several validated anticancer targets. This work highlights the usefulness of molecular simulation as a rapid and cost-effective tool in the early stages of anticancer drug discovery. The identified promising compounds may serve as starting points for further molecular dynamics simulations, structural optimization, and experimental validation through in vitro and in vivo studies