Design and prioritization of plates for high-throughput screening.

Area of Science:

• Computational Chemistry
• Cheminformatics
• Drug Discovery

Background:

• High-throughput screening (HTS) is crucial for identifying novel drug candidates.
• Efficient experimental design is essential for cost-effective and targeted HTS.
• Prioritizing and selecting compounds for screening plates requires sophisticated algorithms.

Purpose of the Study:

• To present a general algorithm for prioritizing and selecting compounds for HTS plates.
• To enable simultaneous optimization of multiple design objectives in experimental planning.
• To improve the cost-effectiveness and targeting of HTS experiments.

Main Methods:

• Utilized a simulated annealing algorithm to explore plate combinations.
• Developed a user-defined objective function to guide the optimization process.
• Implemented a novel similarity sorting algorithm based on one-dimensional nonlinear mapping for plate construction.

Main Results:

• The algorithm is robust, convergent, and capable of optimizing multiple objectives (e.g., molecular diversity, similarity to known actives, predicted activity).
• Demonstrated that compound arrangement on plates significantly impacts experimental design.
• Outlined effective schemes for constructing homogeneous and heterogeneous plates.

Conclusions:

• The presented algorithm provides a powerful tool for optimizing HTS plate selection.
• Simultaneous optimization of diverse objectives leads to more targeted and cost-effective screening campaigns.
• Novel similarity sorting algorithms enhance the construction of well-designed screening plates.