Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.

Area of Science:

• Computational chemistry
• Drug discovery
• Molecular modeling

Background:

• Structure-based virtual screening (VS) is crucial for identifying lead compounds.
• Predicting binding mode and affinity is essential for understanding molecular interactions.
• Empirical scoring functions are standard for pose and affinity prediction in VS.

Purpose of the Study:

• To review recent advances in structure-based virtual screening.
• To highlight strategies for improving binding affinity prediction accuracy.
• To discuss future directions for developing more effective scoring functions.

Main Methods:

• Review of recent literature on virtual screening methodologies.
• Discussion of strategies including ligand entropy, solvent effects, and machine learning.
• Exploration of quantum mechanics applications in scoring function development.

Main Results:

• Pose prediction in VS is generally accurate.
• Accurate binding affinity prediction remains a significant challenge.
• Novel approaches show promise for enhancing VS efficacy.

Conclusions:

• Accurate binding affinity prediction is critical for successful structure-based virtual screening.
• Methodological advancements in ligand entropy, solvent effects, ML, and QM are advancing the field.
• Further development of empirical scoring functions is essential for future drug discovery efforts.