Expanding accessible chemical space through automated high-throughput experimentation
Dr Sam Liver, Manager of the High-Throughput Molecular Discovery Laboratory at the Rosalind Franklin Institute, explains how lab automation in the form of machine learning and high‑throughput experimentation (HTE) can be implemented to enhance productivity in autonomous molecular discovery.
Automation is harnessed routinely at individual stages within design-make-purify-test cycles, yet adjacent stages are rarely fully automated and integrated within drug discovery systems. Recently, however, progress has been made towards realising fully integrated molecular discovery workflows with matched‑ and high-throughput throughout. The combination of machine learning algorithms and automated HTE may deliver a step-change in both the efficiency and effectiveness of molecular discovery, ultimately helping to address the pharmaceutical sector’s grand challenge of increasing productivity. This article considers the prospect of integrating these capabilities to realise fully autonomous molecular discovery.