Researchers have developed a platform that uses artificial neural networks to translate chemical structural formulae into machine-readable form. With this platform, they have created a tool with which this information from scientific publications can be automatically fed into databases. Until now, this had to be done literally by hand and was time-consuming.
Structural formulae show how chemical compounds are constructed, i.e., which atoms they consist of, how these are arranged spatially and how they are connected. Chemists can deduce from a structural formula, among other things, which molecules can react with each other and which cannot, how complex compounds can be synthesised or which natural substances could have a therapeutic effect because they fit together with target molecules in cells.
"First, the entire document is searched for images," explains Steinbeck. The algorithm then identifies the image information contained and classifies it according to whether it is a chemical structural formula or some other image. Finally, the structural formulae recognised are translated into the chemical structure code or displayed in a structure editor, so that they can be further processed."This step is the core of the project and the real achievement," adds Steinbeck.
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