Abstract
Addressing the high mortality rate and limited treatment options for oral cancer is a global public health challenge. To enhance therapeutic approaches, a comprehensive understanding of oral cancer stem cells (OCSCs) is essential. This review employs advanced bio-inspired methods to identify unique pharmacological targets within OCSCs, considering their heterogeneity and resistance to conventional treatments. The Evolutionary Computation Network for Drug Repositioning (ECN-DR) dissects the intricate signaling pathways and molecular networks within OCSCs using bio-inspired techniques. By integrating machine learning, network analysis, and molecular dynamics simulations, this approach identifies potential targets for both new and existing anticancer drugs. Recognizing the key molecular players in OCSCs enables the design of tailored medicines to disrupt these cells, offering more potent and targeted therapy options with fewer side effects. Molecular dynamics simulations, protein-ligand docking studies, and in silico drug screening predict the binding affinity and therapeutic potential of prospective medications against selected OCSC targets. These simulations contribute to a better understanding of targeting specific proteins in oral cancer therapy. Utilizing bio-inspired methods and computational simulations enhances our knowledge of OCSC biology, advancing the prospects of personalized cancer treatment.
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