A team of chemists led by Prof Frank Glorius from the University of Münster is proposing a computer-aided, bias-free method for selecting model substrates to evaluate new chemical reactions. The method aims to improve the quality and information content of chemical reaction data and close knowledge gaps.
Chemists often develop and optimise new chemical reactions using so-called model systems , i.e. simple, easily accessible substrates. They then use up to around 100 other substrates as examples to show that the reaction works. This demonstration of versatile applicability is called 'scope' in technical jargon. However, a subjective selection of substrates often results in a distorted picture of the range of applications of the newly developed reaction.
It is often unclear whether it can be used to synthesise a desired product. To address this problem, a team led by chemist Prof Frank Glorius from the University of Münster (Germany) is proposing a computer-aided, bias-free method for selecting the model substrates to evaluate new chemical reactions. The selection of substrates is based on the complexity and structural properties of real pharmaceutical compounds. 'Our method aims to improve the quality and information content of chemical reaction data in the future and to close knowledge gaps,' explains Frank Glorius. A deeper understanding of new reactions lowers the barriers to their application in both an academic and industrial context. The availability of high-quality, unbiased data also significantly facilitates the use of machine learning and paves the way for a more comprehensive use of the data
Chemists Chemical Reactions Model Systems Substrates Scope Pharmaceutical Compounds Bias-Free Method Computer-Aided Knowledge Gaps
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