Pharmacoinformatics and Breed-Based De Novo Hybridization Studies to Develop New Neuraminidase Inhibitors as Potential Anti-Influenza Agents.

Influenza represents a profoundly transmissible viral ailment primarily afflicting the respiratory system. Neuraminidase inhibitors constitute a class of antiviral therapeutics employed in the management of influenza. These inhibitors impede the liberation of the viral neuraminidase protein, thereby impeding viral dissemination from the infected cell to host cells. As such, neuraminidase has emerged as a pivotal target for mitigating influenza and its associated complications. Here, we apply a de novo hybridization approach based on a breed-centric methodology to elucidate novel neuraminidase inhibitors. The breed technique amalgamates established ligand frameworks with the shared target, neuraminidase, resulting in innovative inhibitor constructs. Molecular docking analysis revealed that the seven synthesized breed molecules (designated Breeds 1-7) formed more robust complexes with the neuraminidase receptor than conventional clinical neuraminidase inhibitors such as zanamivir, oseltamivir, and peramivir. Pharmacokinetic evaluations of the seven breed molecules (Breeds 1-7) demonstrated favorable bioavailability and optimal permeability, all falling within the specified parameters for human application. Molecular dynamics simulations spanning 100 nanoseconds corroborated the stability of these breed molecules within the active site of neuraminidase, shedding light on their structural dynamics. Binding energy assessments, which were conducted through MM-PBSA analysis, substantiated the enduring complexes formed by the seven types of molecules and the neuraminidase receptor. Last, the investigation employed a reaction-based enumeration technique to ascertain the synthetic pathways for the synthesis of the seven breed molecules.

Pharmacophore-based virtual screening, molecular docking and molecular dynamics studies for the discovery of novel neuraminidase inhibitors.

The in silico evaluation of 27 p-aminosalicylic acid derivatives, also referred to as neuraminidase inhibitors was the focus of the current study. To search and predict new potential neuraminidase inhibitors, this study was based on the ligand-based pharmacophore modeling, 3D QSAR, molecular docking, ADMET and MD simulation studies. The data was generated from recently reported inhibitors and divided into two groups, one of these group has 17 compounds for training and the second group has 10 compounds for testing purpose. The generated pharmacophore has known as ADDPR_4 was found statistically significant 3D-QSAR model owing the high trust scores (R2 = 0.974, Q2 = 0.905, RMSE = 0.23). Morever external validation was also employed to evaluate the prediction capacity of the built pharmacophore model (R2pred = 0.905). In addition, in silico ADMET, analyses were employed to evaluate the obtained hits for drug likeness properties. The stability of formed complexes was further evaluated using molecular dynamics. Top two hits showed stable complexes with Neuraminidase based on calculated total binding energy by MM-PBSA.Communicated by Ramaswamy H. Sarma.