Many chemistry researchers treat access to literature as the only key to discovery. But as research becomes increasingly data centric, literature access by itself isn’t enough. Without curated and structured data, researchers can miss trends and lose time building predictive models and hypotheses. Comprehensive, expertly curated datasets combined with AI-powered tools provide researchers with a foundation for data-driven discovery and empower them to make informed decisions before beginning their experiments.

In this webinar, we discuss how Large Language Model (LLM) based search tools significantly simplify database search process for the researchers; how chemistry databases can also be used to predict physicochemical properties and identify patterns within reaction data; finally, how predictive retrosynthetic tools help to eliminate the current time-consuming process of the synthesis route design and reaction mechanism selection. These workflows enable chemists to use modern informatics, bridging the gap between traditional chemistry and the future of computational research.