High-throughput experimentation (HTE) has revolutionized the pharmaceutical industry, most notably allowing for rapid screening of compound libraries against therapeutic targets. The past decade has also witnessed the extension of HTE principles toward the realm of small-molecule process chemistry. Today, most major pharmaceutical companies have created dedicated HTE groups within their process development teams, invested in automation technology to accelerate screening, or both. The industry's commitment to accelerating process development has led to rapid innovations in the HTE space. This review will deliver an overview of the latest best practices currently taking place within our teams in process chemistry by sharing frequently studied transformations, our perspective for the next several years in the field, and manual and automated tools to enable experimentation. A series of case studies are presented to exemplify state-of-the-art workflows developed within our laboratories.

The combination of nickel metallaphotoredox catalysis, hydrogen atom transfer catalysis, and a Lewis acid activation mode, has led to the development of an arylation method for the selective functionalization of alcohol α-hydroxy C-H bonds. This approach employs zinc-mediated alcohol deprotonation to activate α-hydroxy C-H bonds while simultaneously suppressing C-O bond formation by inhibiting the formation of nickel alkoxide species. The use of Zn-based Lewis acids also deactivates other hydridic bonds such as α-amino and α-oxy C-H bonds. This approach facilitates rapid access to benzylic alcohols, an important motif in drug discovery. A 3-step synthesis of the drug Prozac exemplifies the utility of this new method.

Autonomous process optimization involves the human intervention-free exploration of a range process parameters to improve responses such as product yield and selectivity. Utilizing off-the-shelf components, we develop a closed-loop system for carrying out parallel autonomous process optimization experiments in batch. Upon implementation of our system in the optimization of a stereoselective Suzuki-Miyaura coupling, we find that the definition of a set of meaningful, broad, and unbiased process parameters is the most critical aspect of successful optimization. Importantly, we discern that phosphine ligand, a categorical parameter, is vital to determination of the reaction outcome. To date, categorical parameter selection has relied on chemical intuition, potentially introducing bias into the experimental design. In seeking a systematic method for selecting a diverse set of phosphine ligands, we develop a strategy that leverages computed molecular feature clustering. The resulting optimization uncovers conditions to selectively access the desired product isomer in high yield.

Automation has become an increasingly popular tool for synthetic chemists over the past decade. Recent advances in robotics and computer science have led to the emergence of automated systems that...