Analysis of structure-based virtual screening studies and characterization of identified active compounds

Area of Science:

• Computational chemistry
• Drug discovery
• Structural biology

Background:

• Structure-based virtual screening (SBVS) leverages 3D target structures to identify novel active compounds.
• It is a dominant computational approach for hit identification in drug discovery.
• Analysis of nearly 300 applications characterizes the current state-of-the-art in SBVS.

Purpose of the Study:

• To analyze the state-of-the-art of structure-based virtual screening.
• To characterize the success rates and limitations of current SBVS applications.
• To identify trends and biases in SBVS compound identification.

Main Methods:

• Analysis of approximately 300 original structure-based virtual screening applications.
• Evaluation of compound selection criteria and potency of identified hits.
• Comparison of docking results with ligand-based methods.

Main Results:

• Docking calculations frequently identify active compounds, with some achieving submicromolar potency.
• The majority of docking hits exhibit weak potency, and selection is often subjective.
• A small fraction of docking hits are reproducible by ligand-based methods.
• Potent hits often come from specialized libraries and show bias towards kinase targets.
• Limited accuracy in compound scoring and ranking remains a significant challenge.

Conclusions:

• Structure-based virtual screening is a powerful tool for hit identification, but its effectiveness is hampered by scoring limitations.
• Chemical intuition and expert knowledge are crucial for validating and refining docking hits.
• Future improvements in scoring functions and compound selection strategies are needed to enhance SBVS efficacy.