Machine learning made easy for optimizing chemical reactions

The optimization of reactions used to synthesize target compounds is pivotal to chemical research and discovery, whether in developing a route for manufacturing a life-saving medicine1 or unlocking the potential of a new material2. But reaction optimization requires iterative experiments to balance the often conflicting effects of numerous coupled variables, and frequently involves finding the sweet spot among thousands of possible sets of experimental conditions. Expert synthetic chemists currently navigate this expansive experimental void using simplified model reactions, heuristic approaches and intuition derived from observation of experimental data3. Writing in Nature, Shields et al.4 report machine-learning software that can optimize diverse classes of reaction with fewer iterations, on average, than are needed by humans. Machine learning has emerged as a useful tool for various aspects of chemical synthesis, because it is ideally suited to extrapolating predictive models that are used to solve synthetic problems by recognizing patterns in multidimensional data sets5.