Abstract

The prediction of binding modes (BMs) occurring between a small molecule and a target protein of biological interest has become of great importance for drug development. The overwhelming diversity of needs leaves room for docking approaches addressing specific problems. A Fast docking using the CHARMM force field with EADock DSS for the Implementation of the Hungarian algorithm to account for ligand symmetry and similarity in structure-based design of drug-like molecules by a fragment-based molecular evolutionary approach. HIV-1 P24-derived peptides were examined to predict anti-HIV-1 activity among them. The efficacy of the prediction has already been validated in vitro. Our in silico experimental studies performed on the mentioned peptides, which may lead to new anti-HIV-1 peptide-mimotopic therapeutics candidates. In this research study we presented for the first time a computational approach and a combined molecular docking-based and pharmacophore-based target prediction strategy with a probabilistic fusion method for Quantum Biology on the Edge of Quantum Chaos Computational mining approach, a combined molecular docking-based and pharmacophore-based target prediction strategy through a probabilistic fusion method for target ranking of anti-HIV-I P24-derived peptide mimic promising pharmacophores.

Keywords

Computational prediction, anti-HIV-1 peptide-mimic. Pharmastructures, HIV-1, P24-derived, peptides Quantum Biology on the Edge of Quantum Chaos Computational mining approach, a combined molecular docking-based and pharmacophore-based target prediction strategy through a probabilistic fusion method for target ranking of anti-HIV-I P24-derived peptide mimic promising pharmacophores.