Abstract

Cancer has become a great concern in public health. The harmful side effects and multidrug resistance of traditional chemotherapy prompt urgent needs for novel anticancer drugs or therapeutic approaches. Anticancer peptides (ACPs) have become promising molecules as new anticancer agents due to the unique mechanism and several extraordinary properties. Most α-helical ACPs target on cell membrane and the interactions between ACPs and cell membrane components are believed to be a key factor in the selective killing of cancer cells. As a result we discovered for the first time the GENEA-Alphecanitir-4846, an Alpha-Helical Cationic Anticancer Peptide-mimetic Pharmacophore as a promising candidate novel anticancer drug like scaffold utilizing α Survey of Quantum Lyapunov Control maximum common substructure-based support vector machine algorithmic methods for the Fragment based drug discovery of drug like optimized Alpha-Helical Cationic Anticancer Peptide-mimetic annotated Pharmacophore.

Keywords

A Survey of Quantum Lyapunov Control maximum common substructure-based support vector machine algorithmic Methods for the Fragment based drug discovery of drug like optimized Alpha-Helical Cationic Anticancer Peptide-mimetic annotated Pharmacophore.