METHODS: The involved approaches build molecules from fragments that are either isosteric to GSH sub-moieties (ligand-based) or successfully docked to GSH binding sub-pockets (structure-based). Compared to reference GST inhibitor of S-hexyl GSH, ligands with improved rigidity, synthetic accessibility, and affinity to receptor were successfully designed. The method involves joining fragments to create ligands. The ligands were then explored using molecular docking, Cartesian coordinate's optimization, and simplified free energy determination as well as MD simulation and MMPBSA calculations. Several tools were used which include OPENEYE toolkit, Open Babel, Autodock Vina, Gromacs, and SwissParam server, and molecular mechanics force field of MMFF94 for optimization and CHARMM27 for MD simulation. In addition, in-house scripts written in Matlab were used to control fragments connection and automation of the tools.

RESULTS: Pose prediction experiments indicate that chaos-embedded algorithms outperform AutoDock Vina and PSOVina in ligand pose RMSD, success rate, and run time. In virtual screening experiments, Singer map-embedded PSOVina[Formula: see text] achieved a very significant five- to sixfold speedup with comparable screening performances to AutoDock Vina in terms of area under the receiver operating characteristic curve and enrichment factor. Therefore, our results suggest that chaos-embedded PSOVina methods might be a better option than AutoDock Vina for docking and virtual screening tasks. The success of chaotic maps in protein-ligand docking reveals their potential for improving optimization algorithms in other search problems, such as protein structure prediction and folding. The Singer map-embedded PSOVina[Formula: see text] which is named PSOVina-2.0 and all testing datasets are publicly available on https://cbbio.cis.umac.mo/software/psovina .