Self-optimization of chemical reactions using machine learning multi-objective algorithms has the potential to significantly shorten overall process development time, providing users with valuable information about economic and environmental factors. Using the Thompson Sampling Efficient Multi-Objective (TS-EMO) algorithm, the self-optimization flow chemistry system in this report demonstrates the ability to identify optimum reaction conditions and trade-offs (Pareto fronts) between conflicting optimization objectives, such as yield, cost, spacetime yield, and E-factor, in a data efficient manner. Advantageously, the robust system consists of exclusively commercially available equipment and a user-friendly MATLAB graphical user interface, and was shown to autonomously run 131 experiments over 69 hours uninterrupted.

This is a repository copy of Enhanced process development using automated continuous reactors by self-optimisation algorithms and statistical empirical modelling.

There is a notable lack of published data concerning sodium cocoyl isethionate (SCI) despite widespread application in the personal care industry. A specific homologue, sodium lauroyl isethionate (SLI) was therefore synthesized, purified by recrystallization and then subjected to a detailed physicochemical examination. Purity of 98% was achieved via repeat recrystallization in methanol. A turbidimetric solubility analysis was then executed to identify both its crystallizability and metastable zone width as a function of temperature. Thermogravimetric analysis yielded decomposition onsets of 330°C for the purified SLI. A dynamic vapor sorption study also demonstrated reversibility in the 2.3% mass gained, when exposed to sustained humidity of 87%. Surface tension measurements of purified SLI yielded a critical micellar concentration (CMC) of 5.4 mM and a plateau surface tension of 38 mN/m at 20°C. Both values were lower than the previously reported values for SLI in water, thus indicating the performance benefits of purified isethionates in personal care formulations. The single step synthesis was chlorine-, catalyst-and solvent-free, thus improving process efficiency, safety and throughput over existing SLI syntheses. The succeeding physicochemical analysis crucially provides a much needed insight into the purification, properties and performance of isethionate ester surfactants; all of which is strongly applicable to their commercial manufacture from biorenewable sources.

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C–H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations need to be executed in the presence of polar functionality necessary for fragment-protein binding. New technologies such as high-throughput experimentation and self-optimization have the potential to revolutionize synthetic approaches to challenging target molecules in FBDD. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions, however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multi-task Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected during related historical optimization campaigns to accelerate the optimization of new reactions - this was performed for Suzuki-Miyaura and C–N couplings. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions (both continuous and categorical variables) of unseen experimental C–H activation reactions with differing substrates, demonstrating up to a 98 % cost reduction when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, where efficient utilization of precious starting materials is particularly important. This work represents a step-change in the utilization of previously obtained reaction data and machine learning with the ultimate goal of accelerated reaction optimization.