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.
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