Molecular Insight into Anti-Cervical Cancer Drugs by Using Graph Topological Descriptors and Their Predictive Mathematical Modeling

This study explores the molecular characteristics of anti-cervical cancer drugs including Gefitinib, Erlotinib, Afatinib, Lapatinib, Icotinib, Osimertinib, Avitinib, Nazartinib, Dacomitinib and Neratinib. A python program is developed to compute topological indices of these drugs, while their physicochemical properties were examined using supervised machine learning algorithms. The study also examines quantitative structure property relationship analysis, which explains the connections between molecular structure and pharmacological effects. This comprehensive approach elucidates the molecular characteristics of anti-cervical cancer drugs, establishing the foundation for a deeper understanding of their pharmacological effects and therapeutic capacity.