Design of a compound screening collection for use in high throughput screening

Area of Science:

• Computational chemistry
• Cheminformatics
• Drug discovery

Background:

• High-throughput screening (HTS) is crucial for identifying lead compounds.
• Optimizing screening collection composition is key to efficient drug discovery.
• Understanding the relationship between chemical structure and biological activity is fundamental.

Purpose of the Study:

• To introduce a quantitative model relating chemical structural similarity to biological activity.
• To derive optimal screening collection strategies for maximizing lead series discovery.
• To develop a diversity function for assessing compound acquisition and library synthesis.

Main Methods:

• Development of a quantitative model linking chemical structure to biological activity.
• Derivation of optimal screening collection composition based on the model.
• Formulation of a diversity function to assess compound complementarity.
• Analysis of the relationship between collection size and lead discovery probability.

Main Results:

• The model provides optimal screening collection compositions for given sizes.
• Identified conditions favoring diverse versus focused compound collections.
• The derived diversity function aids in selecting compounds to complement existing collections.
• Demonstrated how collection size impacts lead discovery probabilities in HTS.

Conclusions:

• The quantitative model effectively relates chemical similarity to biological activity.
• The model guides the strategic design of screening collections for improved lead discovery.
• The diversity function is a valuable tool for optimizing compound acquisition and library design.
• This approach enhances the efficiency and success rate of high-throughput screening campaigns.