Dataset Information

Exploration of electronic properties, radical scavenging activity and QSAR of oxadiazole derivatives by molecular docking and first-principles approaches

ABSTRACT: Eight new oxadiazole derivatives were designed then geometries for ground state were optimized through Density Functional Theory (DFT) at B3LYP/6-31G** level. Single electron transfer mechanism has been studied to understand the antioxidant ability of the oxadiazole derivatives. Then molecular electrostatic potential and quantitative structure–activity relationship (QSAR) was probed. Additionally, we shed light on different molecular descriptors, e.g., electrophilicity(ω), electronegativity(χ), electrophilicity indices(ωi), hardness(η), softness(S) and chemical potential(μ).The smaller value of ionization potential for 5a is showing that it might be efficient antioxidant candidate. The electrophilic reactive sites in 2a, 3a, 4a, 5a and 7a derivatives might be a good choice for reactivity that would be advantageous to improve the biological activity. The polar surface area of 3a, 4a and 5a derivatives was found < 60 A2 which is enlightening that these drugs might be suitable as orally active and for brain penetration. First-principles calculations and molecular docking results revealed that 5a would lead to superior antioxidant activity.

SUBMITTER: Irfan A

PROVIDER: S-EPMC8626296 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma. Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents. Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity. Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2 cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as "Min exchange energy for a C-N bond" (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities. Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.

Dataset Information

Exploration of electronic properties, radical scavenging activity and QSAR of oxadiazole derivatives by molecular docking and first-principles approaches

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets