Browsing by Author "Oyebamiji AK"

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    Dataset on in-silico investigation on triazole derivatives via molecular modelling approach: A potential glioblastoma inhibitors
    (2021) Oyebamiji AK; Mutiu OA; Amao FA; Oyawoye OM; Oyedepo TA; Adeleke BB; Semire B
    In this work, ten molecular compounds were optimised using density functional theory (DFT) method via Spartan 14. The obtained descriptors were used to develop quantitative structural activities relationship (QSAR) model using Gretl and Matlab software and the similarity between predicted IC50 and observed IC50 was investigated. Also, docking study revealed the non-bonding interactions between the studied compounds and the receptor. The molecular interactions between the observed ligands and brain cancer protein (PDB ID: 1q7f) were investigated. Adsorption, distribution, metabolism, excretion and toxicity (ADMET) properties were also investigated.
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    Dataset on insightful bio-evaluation of 2-(quinoline-4-yloxy)acetamide analogues as potential anti-Mycobacterium tuberculosis catalase-peroxidase agents via in silico mechanisms
    (2021) Oyebamiji AK; Josiah OM; Akintelu SA; Adeoye MD; Sabitu BO; Latona DF; Esan AO; Soetan EA; Semire B
    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 Mycobacterium tuberculosis 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. More so, the binding affinity between 2-(quinoline-4-yloxy)acetamide analogues and Mycobacterium tuberculosis catalase-peroxidase (PDB ID: 1sj2) via docking method were reported. ADMET properties of some selected compounds were also examined.
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    Dataset on Insilico approaches for 3,4-dihydropyrimidin-2(1H)-one urea derivatives as efficient Staphylococcus aureus inhibitor
    (2020) Oyebamiji AK; Abdulsalami IO; Semire B
    Series of anti- Staphylococcus aureus were studied via quantum chemical method and several molecular descriptors were obtained which were further used to develop QSAR model using back propagation neural network method using MATLAB. More so, the molecular interaction observed between 3,4-dihydropyrimidin-2(1H)-one Urea Derivatives and Staphylococcus aureus Sortase (PDB ID Code: 2kid) via docking was used as a screening tool for the studied compounds. The observed molecular compounds used in this work was also correlated to Lipinski rule of five and the developed QSAR model using selected descriptors from the optimized compounds was also examined for its predictability. Also, the observed molecular docking revealed the interaction between the studied complex.
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    Dataset on the DFT-QSAR, and docking approaches for anticancer activities of 1, 2, 3-triazole-pyrimidine derivatives against human esophageal carcinoma (EC-109)
    (2020) Adegoke RO; Oyebamiji AK; Semire B
    The investigation of the novel hybrid, 1, 2, 3-triazole moiety combined with pyrimidine derivatives against human esophageal carcinoma is an unexplored field of theoretical/computational chemistry. Also, the development of new drugs still remains a major challenge, cost-intensive and time-consuming, thus making the computational approach now a hot topic due to its ability to hasten up and aid the process of drug designs. Here, the use of the quantum chemical method via density functional theory (DFT) was employed in calculating molecular descriptors for developing the quantitative structure-activity relation (QSAR) model which predicts bioactivity of the selected 1, 2, 3-triazole-pyrimidine derivatives. Quantum chemical method implemented in Spartan 14, was used in calculating the molecular descriptors. The obtained results were imputed into Gretl and SPSS (software package for social sciences) to generate a novel QSAR model equation for human esophageal carcinoma (EC-109) through multiple linear regression. The relationship between the experimental and predicted inhibition efficiency (IC50) of 1,2,3-triazole-pyrimidine with EC-109 was calculated which gives good correlation results. QSAR was validated using CV.R2andRa2. Fitting value (R2) of 0.999 with an adjusted fitting value (Ra2)of 0.995 was obtained and the result of validating QSAR performance gave CV.R2 and Ra2value that is greater than 0.6, signifying its appropriateness and dependability. Molecular docking through simulation using Discovery Studio 4.1, Autodock Tool 1.5.6 and AutodockVina 1.1.2 was also carried out to calculate the free energy of ligand-receptor interactions as well as ligand conformation in the receptor-binding site. The results obtained revealed the presence of hydrogen bond interaction of the ligands with the amino acids residue in the binding sites of the receptor. Conformation of the ligands was essential property for binding ligand with the receptor. Critical examination and the correlations between the IC50 and binding energy showed the activeness of ligand conformation in the gouge of the receptor with binding energy greater than the 5-fluorouracil (5- Fu) that was used as the standard compound.
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    Dataset on theoretical bio-evaluation of 1,2,4-thiadiazole-1,2,4-triazole analogues against epidermal growth factor receptor kinase down regulating human lung cancer
    (2021) Oyebamiji AK; Akintelu SA; Amao OP; Kaka MO; Morakinyo AE; Amao FA; Semire B
    Data from eight 1,2,4-thiadiazole-1,2,4-triazole derivatives were used to observe the anti-epidermal growth factor receptor kinase activities of 1,2,4-thiadiazole-1,2,4-triazole analogues thereby reducing human lung cancer. The software used to achieve this work were Spartan 14, Pymol, mgltools_win32_1.5.6, Auto dock vina and biovia2019.ds2019client. Also, the developed QSAR model was developed using the screened descriptors so as to inspect the closeness between the experimental IC50 and the predicted IC50. More so, the binding affinity from 1,2,4-thiadiazole-1,2,4-triazole derivatives - epidermal growth factor receptor kinase complexes using molecular docking approach were reported. Also, the ADMET properties for selected compounds and proposed compounds with better binding affinity were reported.