Enhancing the rate of scaffold discovery with diversity-oriented prioritization

Area of Science:

• Drug discovery
• Computational chemistry
• Bioinformatics

Background:

• High-throughput screening (HTS) identifies active molecules but often neglects scaffold diversity.
• Maximizing the number of unique active scaffolds is a key metric for HTS success.
• Existing algorithms for hit selection may not prioritize scaffold discovery effectively.

Purpose of the Study:

• To develop and validate a new extension of the diversity-oriented prioritization (DOP) framework.
• To enhance the identification of novel active scaffolds from HTS data.
• To optimize the selection of molecules for confirmatory testing to maximize scaffold discovery.

Main Methods:

• Derived an extension of the diversity-oriented prioritization (DOP) economic framework.
• Applied DOP to prioritize hits for confirmatory testing.
• Evaluated DOP's performance in retrospective and prospective experiments.
• Incorporated iterative computation of marginal cost of discovery.

Main Results:

• DOP accurately predicted scaffold discoveries in confirmatory experiments.
• DOP improved the rate of scaffold discovery by 8-17%.
• The framework demonstrated robustness across different batch sizes for confirmatory testing.

Conclusions:

• The extended DOP framework effectively maximizes the number of confirmed active scaffolds.
• DOP provides a robust and efficient method for prioritizing hits in HTS.
• The marginal cost of discovery metric aids in determining optimal batch sizes for confirmatory testing.