Researchers have designed, built and programmed a chemical-handling robot that can screen and predict chemical reactivity using machine learning. Based on the autonomous system’s predictions, the team found four novel reactions, demonstrating its potential to discover reactions quickly.
Countless chemical reactions are known and many different pathways can lead to a desired molecule. To find the best pathways, discovering new chemical reactivity is crucial to make the processes that produce chemicals, pharmaceuticals and materials more sustainable, environmentally-friendly and efficient. However, discovering new reactions is usually an unpredictable and time-consuming process that’s constrained by a top-down approach involving expert knowledge to target a particular molecule.
Now, Lee Cronin’s lab at the University of Glasgow, UK, has created an organic synthesis robotic AI system that can quickly explore the reactivity of a set of reagents from the bottom-up with no specific target. By just performing around 10% of 969 possible reactions from a set of 18 reagents the autonomous system was able to predict with 86% accuracy the reactivity of the remaining 90% of reactions. It then automatically performed more rounds of experiments based on the reactivity data it had gathered, continually updating the database, and in doing so discovered four new reactions. These were followed up manually by the team to isolate and characterise the new compounds.
‘I’m really surprised that the system was able to discover new reactions and molecules and especially the structure of one of the molecules is really strange and unexpected,’ says Cronin. ‘This is proof of principle that target-free organic discovery and synthesis can yield really unexpected and perhaps even very novel results that could fundamentally change how we go about looking for new reactions.’
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