Open challenges in structure-based virtual screening: Receptor modeling, target flexibility consideration and active...

Area of Science:

• Computational chemistry
• Drug discovery
• Structural biology

Background:

• Structure-based virtual screening (SBVS) is a cornerstone of modern drug lead discovery.
• Significant advancements in algorithms and computational power have been achieved.
• However, persistent challenges remain in accurately modeling biological systems for SBVS.

Purpose of the Study:

• To review the current state-of-the-art in SBVS.
• To discuss critical "structural" challenges in SBVS.
• To highlight recent developments and future directions for addressing these challenges.

Main Methods:

• Review of recent literature on SBVS.
• Focus on three key structural aspects: homology modeling, protein dynamics, and active site water molecules.
• Analysis of conceptual frameworks and computational approaches.

Main Results:

• Homology modeling is crucial for binding site prediction when experimental structures are absent.
• Accounting for protein dynamics is essential for flexible biological systems.
• Active site water molecules significantly influence lead identification and optimization.

Conclusions:

• Addressing these structural challenges is vital for enhancing SBVS accuracy and efficiency.
• Further improvements in modeling protein flexibility and water interactions are needed.
• Optimized SBVS strategies will accelerate the identification of novel drug leads.