In-silico-based lead optimization of hit compounds targeting mitotic kinesin Eg5 for cancer management

Lead optimization is vital for turning hit compounds into therapeutic drugs. This study builds upon a prior in silico research, where the hit compounds had better binding affinity and stability compared to a reference drug. Using a genetic algorithm, 12,500 analogs of the top compounds from the prior study were generated. Virtual screening was done using a quantitative structure-activity relationship (QSAR) model. Top analogs, selected based on pChembL values below 6.000nM, underwent molecular docking targeting Human Eg5. The top five analogs from this study (Compound 9794, Compound 8592, Compound 9786, Compound 2744, and Compound 3246) demonstrated strong binding energies and interactions with key amino acids (GLU 116, GLU 117, and ARG 119). MMGBSA analysis revealed comparable affinities to the co-crystallized ligand, suggesting the top analogs' potential as Human Eg5 inhibitors. Induced fit docking highlighted Compound 9786's superior efficacy. Quantum Polarized Ligand Docking indicated promising scores for Compounds 8592 and 9786. ADMET predictions offered insights into pharmacological properties, with all compounds predicted to be HIA-positive and non-carcinogenic. Further MD simulation study confirms the stability of the top compounds in the active site of Eg5. This study shows the significance of integrated strategies in drug design. However, in vitro and in vivo studies should be conducted for these promising candidates to confirm their efficacy as Eg5 inhibitors.

The online version contains supplementary material available at 10.1007/s40203-024-00300-6.