Chemicals Informatics: Explore Structural Factors and Potential Chemicals based on Public Literatures

Chemical industry pays much cost and long time to develop new compounds or composites that have aimed properties. The developers need to efficiently discover initial candidates before simulation, actual synthesis, optimization, and evaluation. To meet their needs, we have developed CI (Chemicals Informatics) to efficiently discover potential candidates of compounds or composites based on large number of public literatures. Our system now has the data of 117M existing compounds, and 61 properties extracted by analyzing a public chemical database linked to worldwide 33M papers and 30M patents in addition to 11M new structures generated based on existing compounds. The compounds are shown as vectors including 41 organic and 70 inorganic features. The properties are extracted from literatures using rule-based NLP (Natural Language Processing). Our system predicts 61 properties by each space of neighbor, structural crossover, and compound crossover. Users can extract structural factors and highly probable combinations of structures and compounds that contribute to good properties. Moreover, they can find potential combinations that have no patents of aimed property from 128M to the fourth power. In the biochemical use case, we explored candidates for antiCoronavirus medicine. CI extracted three candidates and we confirmed that the results matched with those of past papers about docking simulator, clinical trial, and in-vitro test. In the other case of kinase inhibitors, we could find new structures derived from existing inhibitors or like the inhibitor of past paper. In other cases, we explored structural factors and new probable combinations for optical resin lens, low dielectric and etching gas for semiconductor. CI could extract structural factors that contributed to good properties and search highly probable combinations that had a new structure replicating the same idea as the past literatures.