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Scientists from the Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) and Helmholtz Munich Center for Environmental Health (HMGU, Germany) have created a neural network for visualizing the chemical space of compounds that can be of potential value for the pharmaceutical industry. The new method will help to create new chemical compounds and navigate in the space of the existing chemicals. The results of the study were published in RSC Advances.
Chemists often have to toil through huge databases containing tens or even hundreds of thousands of chemical structures to select the best candidates. To do so, they need to know what classes of compounds the database contains. However, going through thousands of molecules is a laborious task, which would be much easier if the molecules were pictured as dots and placed on a plane or in space, with similar molecules huddled together. This would enable studying the chemical space using a simple tool in much the same way as the geographer uses digital maps of different scales to view a bigger picture or zoom in on a particular area. But here's the rub: How would the algorithm know where to place the molecules if the tool has no knowledge of chemistry?
Group - Researchers - CDISE - Dmitry - Karlov
A joint group of researchers from CDISE (Dmitry Karlov, Sergey Sosnin and Maxim Fedorov) and HMGU (Igor Tetko) applied AI methods to extract information...
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