Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments

Area of Science:

• Chemical synthesis
• Reaction optimization
• Data analysis

Background:

• High-throughput experimentation (HTE) accelerates chemical discovery but relies heavily on initial conditions.
• Human involvement in HTE plate design and data analysis is costly and prone to bias.
• Machine learning for reaction condition generation is limited by insufficient or low-quality data.

Purpose of the Study:

• To develop a robust statistical method for analyzing large HTE reaction datasets.
• To provide data-driven insights for optimizing chemical reactions.
• To publish underlying data and analysis tools for broader use.

Main Methods:

• A statistical method using z-scores was developed to analyze 66,000 internal HTE reactions.
• The method was applied to complex molecules, focusing on Buchwald-Hartwig and Suzuki-Miyaura cross-coupling reactions.
• Underlying data and an analysis tool were published.

Main Results:

• The z-score method effectively analyzed a large HTE dataset.
• Optimal reaction conditions were identified that significantly differ from literature guidelines.
• Data-driven insights were generated for specific cross-coupling reactions.

Conclusions:

• The developed statistical method provides a robust approach to HTE data analysis.
• Data-driven insights offer superior starting points for reaction optimization compared to traditional guidelines.
• This approach enhances the efficiency of chemical discovery and optimization campaigns.