The conventional approach to developing asymmetric synthetic methods relies heavily on empirical optimization. However, the integration of artificial intelligence (AI) and highthroughput experimentation (HTE) technology presents a paradigm shift with immense potential to revolutionize the discovery and optimization of asymmetric reactions.
High-Throughput Experimentation (HTE) is a method where a large number of experiments are conducted simultaneously using automated equipment and robotics. It is now being introduced in chemistry and pharmaceutical sciences to quickly collect and analyze large amounts of data.
New data repositories and alternative journals and workshops offer routes for sharing negative results — which could help to solve the reproducibility crisis and give machine learning a boost.