Evolutionary algorithm generates tailored ’molecular fingerprints’

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Team at the University of Münster develops an improved method for explaining machine predictions of chemical reactions

Using an evolutionary algorithm based on evolutionary processes (shown figurativ
Using an evolutionary algorithm based on evolutionary processes (shown figuratively in the middle), the most important structural features of the molecules are identified in a data set (left) and summarised in a digital ’molecular fingerprint’ (right). On this basis, predictive models can be trained and used, for example, in the search for new drugs (bottom right). © Felix Katzenburg, Glorius Group
Artificial intelligence and machine learning are becoming more and more relevant in everyday life - and the same goes for chemistry. Organic chemists, for example, are interested in how machine learning can help discover and synthesise new molecules that are effective against diseases or are useful in other ways. A team led by Prof Frank Glorius from the Institute of Organic Chemistry at the University of Münster has now developed an evolutionary algorithm that searches for optimal molecular representations based on the principles of evolution, using mechanisms such as reproduction, mutation and selection. ...
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