Hit identification and optimization in virtual screening: practical recommendations based on a critical literature...

Area of Science:

• Computational Chemistry
• Drug Discovery
• Medicinal Chemistry

Background:

• Virtual screening (VS) is a key computational method in drug discovery for identifying potential drug candidates.
• Analysis of VS results from 2007-2011 reveals inconsistencies in hit identification and compound quality.
• Over 400 studies were analyzed to benchmark current VS practices.

Purpose of the Study:

• To critically analyze published virtual screening results and identify best practices for hit identification and optimization.
• To compare hit rates and ligand efficiencies across studies with varying virtual library sizes and testing numbers.
• To develop practical recommendations for improving the quality and success rate of virtual screening campaigns.

Main Methods:

• Comparative analysis of hit compounds' activity against their identification criteria.
• Calculation of hit rates and ligand efficiencies.
• Application of promiscuity, drug-likeness, and ADMET filters to assess hit quality.
• Analysis of hit optimization strategies in a subset of studies.

Main Results:

• Hit rates and ligand efficiencies vary significantly based on virtual library size and screening approach.
• Promiscuity, drug-likeness, and ADMET filters highlight quality issues in reported hits.
• Studies with focused optimization strategies showed better outcomes.
• Size-targeted ligand efficiency emerged as a critical metric for effective hit identification.

Conclusions:

• Current virtual screening hit criteria often lead to suboptimal compound selection.
• Implementing size-targeted ligand efficiency criteria enhances the reliability of hit identification.
• Recommendations are provided for selecting compounds for experimental testing and defining robust hit criteria to improve hit optimization success.