DNA Encoded Library Selections and Insights Provided by Computational Simulations.

Area of Science:

• Medicinal Chemistry
• Drug Discovery
• Biotechnology

Background:

• DNA encoded library (DEL) technology rapidly generates vast small molecule libraries for drug discovery.
• DEL selections identify potential drug candidates by measuring molecule enrichment against target proteins.
• Current DEL analysis assumes higher enrichment correlates with stronger binding affinity.

Purpose of the Study:

• To investigate the relationship between DEL selection enrichment and actual binding affinity.
• To develop a more reliable method for assessing small molecule binding affinities from DEL data.
• To improve the efficiency of hit identification in pharmaceutical research.

Main Methods:

• A mathematical model was developed to simulate DNA encoded library selections.
• Simulations analyzed the impact of protein concentration and library quality on enrichment accuracy.
• A novel technique was demonstrated using DEL selections across a range of protein concentrations.

Main Results:

• DEL enrichment levels may poorly correlate with equilibrium association constants, especially with high protein concentrations or low-quality libraries.
• The proposed method allows for direct qualitative assessment of binding affinities from sequencing data.
• Accounting for synthetic yield variations improves the reliability of DEL-derived binding affinity estimations.

Conclusions:

• Relying solely on enrichment levels in DEL can lead to inaccurate prioritization of drug candidates.
• Varying protein concentrations during DEL selection provides a more robust method for assessing binding affinity.
• This approach enhances the accuracy of identifying promising starting points for drug development.