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Chemical Sciences
AI applied for synthesis planning, structure-property prediction, and chemical data analysis can help chemists be more productive in their research and daily work.
Synthesis planning
Synthesis of molecules remains one of the most important challenges in organic chemistry, and the standard approach involved by a chemist to solve a problem is based on experience and constitutes a repetitive, time-consuming task, often resulting in non-optimal solutions. AI dramatically speeds up the discovery of a reliable reaction pathway that leads to the target compound from a set of commercially available compounds and constitutes the future of chemistry.
Molecular design and chemical properties prediction
Classical molecular design involves multiple iterations of synthesis for experimental verification that the molecule possesses the right properties. This process takes a lot of money and time. AI allows molecular structures to be generated along with predefined properties like solubility, toxicity, bioactivity, and many others. Generated drug candidates with predicted properties enable researchers to synthesize only the most promising ones and to avoid synthesizing and testing many molecules that don't have the desired properties.
Chemical data analysis
The chemical industry works with chemical structures, niche literature, crystallography, NMR spectra, and thermophysical data. NMR and LC-MS data, for example, correlate chemical structures with spectrums. Usually, compounds are detected semi-automatically by software first and verified by human experts later. AI offers improved automatic compounds detection from spectrums. A combination of NLP and image processing is used for the automatic extraction of chemical reactions and conditions from academic papers and patents.
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