Computational screening of phytochemicals present in some Nigerian medicinal plants against sickle cell disease.

Four hundred Phytochemical (bio-active) compounds having predictive activity for treating Sickle Cell Anemia were screened, using PASS online computational resource. Twenty-six compounds out of the four hundred compounds which showed high probability for treating sickle were further screened for pharmacokinetics profiles (ADMET properties) using SwissAdmet, AdmetSAR 2 and Pro-tox II online resources. Only thirteen compounds that displayed good ADMET properties from the twenty-six were further used for DFT calculations and molecular docking against carbonmonoxy sickle hemoglobin (PDB ID: 5E6E).

Molecular properties and In silico bioactivity evaluation of (4-fluorophenyl)[5)-3-phen-(4-nitrophenyl yl-4,5-dihydro-1-pyrazol-1-yl]methanone derivatives: DFT and molecular docking approaches.

Objectives: Molecular structures, spectroscopic properties, charge distributions, frontier orbital energies, nonlinear optical (NLO) properties and molecular docking simulations were analyzed to examine the bio-usefulness of a series of (4-fluorophenyl)[5-(4-nitrophenyl)-3-phenyl-4,5-dihydro-1H-pyrazol-1-yl]methanone derivatives.

Methods: The compounds were studied through computational methods. Equilibrium optimization of the compounds was performed at the B3LYP/6-31G(d,p) level of theory, and geometric parameters, frequency vibration, UV-vis spectroscopy and reactivity properties were predicted on the basis of density functional theory (DFT) calculations.

Dataset on insightful bio-evaluation of 2-(quinoline-4-yloxy)acetamide analogues as potential anti- catalase-peroxidase agents via mechanisms.

The continuous havoc wrecked by tuberculosis among humans worldwide remains colossal. In this work, twenty-one (21) 2-(quinoline-4-yloxy)acetamide analogues were observed against catalase-peroxidase (This enzyme shields bacteria from poisonous drug-like molecules) (PDB ID: 1sj2) using density functional theory method, QSAR study using material studio software and docking method via PyMol, AutoDock Tool, AutoDock Vina and Discovery studio 2017 as well as ADMET study via admetSAR2. Twelve descriptors were obtained from the optimized compounds which were used to develop valid QSAR model.