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Smart retrosynthesis simplifies reaction design

By Mary Page Bailey |

A new retrosynthesis platform enables users to work backwards from a target molecule to optimize synthesis pathways. MilliporeSigma’s (Burlington, Mass.; www.sigmaaldrich.com) Synthia software uses sophisticated algorithms backed by a database of over 72,000 coded reaction rules to provide precise pathways to novel and known organic molecules of interest, explains Sarah Trice, director and head of MilliporeSigma’s Cheminformatics Technologies business unit. After 15 years of academic development, MilliporeSigma launched Synthia commercially in April 2019. Synthia is said to be the first commercial platform of its kind on the market. “Of the other retrosynthesis tools we have seen, we are highly differentiated. Most other softwares use machine learning to analyze publically available databases, but there are inherent problems with this approach. The data sets are not clean, and a massive amount of data must be included. The resulting pathways do not consider a holistic view of the entire molecule. Given our hand-coding approach, our platform considers reactions that may have only been performed a handful of times, which is hard to do with a machine-learning approach,” says Trice. Furthermore, Synthia is highly tunable, taking into account not only complex parameters, such as stereochemistry, but also feedstock considerations. “If someone is interested in only using commodity chemicals as starting materials, they can put input price constraints for commercially available feedstocks, and the software will only look at pathways using those starting materials,” adds Trice.

In internal experiments, Synthia evaluated new pathways for eight chemicals manufactured by MilliporeSigma, each of which suffered from low yields, overly difficult pathways, or high risks. All of the resulting pathways were deemed valid in laboratory tests, and for one product, the company was able to quickly commercialize a simpler, higher-yield route and realize significant profit increases. Currently, the development team is expanding the rules database, enhancing the search capabilities, and fine-tuning the prediction of reaction conditions using machine learning and artificial intelligence.

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