An approach for parallel and adaptive screening of discrete compounds followed by reaction optimization using an automated chemistry workstation

Abstract

The challenge of finding appropriate reagents, catalysts, or cocatalysts for chemical reactions is typically met with a strategy of surveying broadly to identify a good starting point (i.e., a hit) from which an in-depth optimization can be performed. We have developed an approach for experiment planning that enables this two-tiered strategy to be implemented on an automated chemistry workstation. One experiment-planning module (Parascreen) for screening discrete substances to identify hits is linked to a second module (Multidirectional search, MDS) for optimization of each hit compound. The screening module has been constructed through modification of an experiment-planning module for performing grid searches. Each candidate is examined over a range of concentrations. Two levels of decision-making are performed. (1) Local evaluation: Yield-versus-time data from a given reaction are examined in a pattern-matching procedure to assess whether monitoring should be continued or terminated. (2) Global evaluation: When a user-defined threshold (e.g., yield) is reached, the candidate is flagged as a hit; any experiments at higher concentration of the same candidate are deleted. Each of the hit compounds is subjected to identification of refined conditions by means of an MDS optimization. Each module alone enables parallel adaptive experimentation. Several issues for automated use of two different modules in succession have been addressed. This two-tiered approach of breadth-first screening followed by in-depth optimization of hits enables autonomous experimentation using an automated chemistry workstation.