Using Machine Learning to Parse Chemical Mixture Descriptions

Abstract

Chemical mixtures have recently come to the attention of open standards and data structures for capturing machine-readable descriptions for informatics uses. At the present time, essentially all transmission of information about mixtures is done using short text descriptions that are readable only by trained scientists, and there are no accessible repositories of marked-up mixture data. We have designed a machine learning tool that can interpret mixture descriptions and upgrade them to the high-level Mixfile format, which can in turn be used to generate Mixtures InChI notation. The interpretation achieves a high success rate and can be used at scale to markup large catalogs and inventories, with some expert checking to catch edge cases. The training data that was accumulated during the project is made openly available, along with previously released mixture editing tools and utilities.

Progress of Artificial Intelligence in Drug Synthesis and Prospect of Its Application in Nitrification of Energetic Materials

“…The continuous nitrification production technology is not as convenient and economical as an intermittent operation mode for relatively small-scale chemical reactions [ 26 ]. In recent years, artificial intelligence has made impressive research progress in the fields of drug discovery and development, exhibiting the advantages of high synthetic efficiency, reduced costs, short time and less waste [ 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Therefore, with the introduction of pathbreaking artificial intelligence into the continuous nitrification process, the development of continuous nitrification unit reaction technology and the significant reduction in the number of on-site operators or even the absence of operators on site, only remote manipulation can greatly improve the degree of “intrinsic safety” [ 34 , 35 , 36 , 37 ] to achieve a true sense of “human-machine isolation”.…”