Emerging Trends in Multi-Objective Optimization of Organic Synthesis Leveraging High-throughput Tools and Machine Learning Methods

The discovery of optimal conditions of chemical reactions is a labor-intensive, time-consuming task that requires exploring a high-dimensional parametric space. Historically the optimization of chemical reactions has been performed by manual experimentation guided by human intuition and Design of Experiments where one reaction variable is modified at any one time to find optimal conditions for one specific reaction outcome. Recently a change of paradigm in chemical reaction optimization procedures has been enabled by the advances in lab automation and the introduction of machine learning algorithms, where multiple reaction variables can be synchronously optimized to obtain optimal reaction conditions requiring a shorter amount of experimental time and minimal human intervention. Herein, we review the state-of-the-art of high-throughput automated chemical reaction platforms and machine learning algorithms that are currently used to drive the optimization of chemical reactions, highlighting the limitations and future opportunities that this new field of research encounters.