Optimization of the anticonvulsant activity of 2-acetamido-N-benzyl-2-(5-methylfuran-2-yl) acetamide using QSAR modeling and molecular docking techniques

• Main Authors: Oluwaseye Adedirin, Adamu Uzairu, Gideon A. Shallangwa, Stephen E. Abechi
• Format: Article
• Language: English
• Published: SpringerOpen 2018-12-01
• Series: Beni-Suef University Journal of Basic and Applied Sciences
• Online Access: http://www.sciencedirect.com/science/article/pii/S2314853517305231

Chemometric descriptors were used to analyze quantitatively the anticonvulsant activity of ninety propanamide derivatives. Molecular geometries of the data set were optimized with B3LYP/6-31G∗∗ quantum mechanical method and chemometric descriptors were calculated from the optimized structure. Linear QSAR models were developed using genetic function algorithm. Predictive capabilities of the models were evaluated using various internal and external validation techniques. The best three models proposed were octa-parametric equation with good statistical quality: R2 (0.898–0.918); Q2 (0.865–0.893); R2pred (0.746–0.772) and F (66.657–88.036). 2-acetamido-N-benzyl-2-(5-methylfuran-2-yl) acetamide a member of the data set was chosen as scaffold for in silico design. Using the information afforded by the models, several attempts were made to optimize the scaffold by introducing various modifications. Potential derivatives with higher predicted activity values than the template were identified and a detailed analysis on the models applicability domain defined the designed compounds, whose estimations can be accepted with confidence. Some of the designed compounds docked with γ-aminobutyrate aminotransferase (PBD: 1OHV) (target) showed better binding affinity for the target when compare with 4-aminohex-5-enoic acid (vigabatrin) (a known inhibitor of the target). Keywords: Activity based clustering, Γ-Amino butyrate-aminotransferase, Genetic function algorithm, Quantitative structure activity relationship, Molecular docking