High-throughput experimentation (HTE) accelerates chemical discovery by shortening the lead times for molecule synthesis. The choice of initial reaction conditions directly influences the outcome and length of any reaction optimization. But human involvement in plate design and data analysis remains a significant cost factor and is accompanied by biases. Therefore, making the most out of past reaction outcomes is crucial. While advances in machine learning allow us to generate promising reaction conditions, this approach is often not suitable because not enough relevant reaction data are available or it is of insufficient quality. Herein we introduce a robust statistical method using z-scores to analyze 66,000 internal HTE reactions on complex molecules. Additionally, we publish the underlying data as well as a tool to analyze and draw actionable conclusions from this data set. We exemplify the power of this method for the widely employed Buchwald-Hartwig and Suzuki-Miyaura cross-coupling reactions. The results reveal optimal conditions that differ significantly from literature-based guidelines. These data-driven insights provide high-quality starting points for optimization campaigns, improving their overall efficiency.
Ahlbrecht et al. (Thu,) studied this question.