Computer-aided design process of possible antisickling agents using molecular docking, pharmacokinetic assessment,...

Sickle cell disease (SCD), a multiorgan disease that is one of the most common genetic ailments, affects about 15 million people globally. The findings for drugs that bind to hemoglobin and adjust the oxygenation condition have been a key component of SCD treatment. The goal of this study was to use computational methods to find lead compounds, design novel bioactive molecules that are strong SCD inhibitors, and gain further knowledge about their reaction process. With data demonstrating predictive properties of R ² = 0.990, [Formula: see text] = 0.980, and [Formula: see text] = 0.987, the developed QSAR model is statistically reliable and highly predictive. It also satisfies the established standards for sound models, which are recommended by numerous institutions. According to the study, the designed molecules (DM) exhibit predicted biological activity (pIC₅₀) of 6.843, 6.671, and 6.912 in comparison to pIC₅₀ of 5.403 for the TM and 4.956 for the SD. Additionally, molecules DM1, DM2, and DM3 exhibit better drug ratings of 0.67, 0.56, and 0.91, respectively, compared to drug scores of 0.44 for TM and 0.49 for SD, suggesting better pharmacokinetic properties than TM and SD. The protein-DM3 complex showed higher binding free energies than the protein-L-glutamate complex, indicating that it is more stable, according to MD simulation, which was used to determine the stability of the proposed molecule.