The ‘self-driving’ system, underpinned by machine learning algorithms, can find new reactions and molecules.
This allows a digital-chemical data-driven approach to locating new molecules of interest, rather than being confined to a known database and the normal rules of organic synthesis.
The result could mean a decreased cost for discovering new molecules for drugs, new chemical products including materials, polymers, and molecules for high tech applications like imaging.
The team demonstrated the system’s potential by searching around 1000 reactions using combinations of 18 different starting chemicals. After exploring only around 100 of the possible reactions, the robot was able to predict with over 80 per cent accuracy which combinations of starting chemicals should be explored to create new reactions and molecules. By exploring these reactions, they discovered a range of previously unknown new molecules and reactions, with one of the reactions classed to within the top 1 per cent of the most unique reactions known.
The approach was designed and developed by the team lead by Professor Leroy (Lee) Cronin, the University of Glasgow’s Regius Chair of Chemistry. Professor Cronin and his team believe that this result will help pave the way for the digitisation of chemistry and developing new approaches to chemistry using a digital code which drives autonomous chemical robots.
The team’s findings were published in a paper in journal Nature this week.
Professor Cronin said: “This approach is a key step in the digitisation of chemistry, and will allow the real time searching of chemical space leading to new discoveries of drugs, interesting molecules with valuable applications, and cutting cost, time, and crucially improving safety, reducing waste, and helping chemistry enter a new digital era.”