Pharmacophore modeling, multiple docking, and molecular dynamics studies on Wee1 kinase inhibitors

Area of Science:

• Biochemistry
• Medicinal Chemistry
• Computational Drug Discovery

Background:

• Wee1-like protein kinase (Wee1) is a key regulator of the G2 checkpoint, crucial for preventing mitosis during DNA damage.
• Wee1's role in cell cycle regulation makes it a significant target for developing novel cancer therapies.

Purpose of the Study:

• To discover potent Wee1 inhibitors with unique chemical structures.
• To identify novel drug candidates for cancer treatment by targeting Wee1 kinase activity.

Main Methods:

• Development and validation of a ligand-based pharmacophore model (AADRRR.340) using 101 known Wee1 inhibitors.
• Virtual screening of chemical databases, followed by refinement using Lipinski's rule of 5, multiple docking simulations (HTVS, SP, GOLD, XP, QPLD), and ADMET screening.
• Binding free energy calculations using Prime/MM-GBSA and MM-PBSA, complemented by 50 ns molecular dynamics simulations for top candidates.

Main Results:

• A validated pharmacophore model (AADRRR.340) with strong statistical significance (R²=0.9212, Q²=0.7457) was established.
• Virtual screening and subsequent computational analyses identified eight potential Wee1 inhibitor leads.
• Three top hits demonstrated excellent ligand-protein complex stability and high binding affinity, confirmed by molecular dynamics and binding energy calculations.

Conclusions:

• The study successfully identified three novel Wee1 inhibitors with promising therapeutic potential for cancer treatment.
• The developed pharmacophore model and computational screening strategy are effective for discovering new kinase inhibitors.
• These findings provide a strong foundation for further preclinical development of Wee1-targeted cancer therapies.