Chemistry in a graph: modern insights into commercial organic synthesis planning

Abstract

Across the chemical sciences, synthesis planning is a key aspect for defining synthesis routes, starting from idea generation, combining literature searches and laboratory experimentation, and including scaling-up considerations for large scale manufacturing. This iterative process, which relies heavily on information sharing, is crucial in pharmaceutical development, where drug candidates are transformed into commercially viable Active Pharmaceutical Ingredients (APIs), impacting the access to medicines for billions of people. In this work, we demonstrate that by capturing chemical pathway ideas digitally, at the point of conception, we can systematically merge these ideas with synthetic knowledge derived from predictive algorithms. This serves as a preliminary step for further route evaluation. To achieve this, we introduce a new method for storing, analysing, and displaying chemical information using graph databases and graph representations, illustrated with the commercial synthesis planning of the GLP-1 inhibitor Lotiglipron. Compared to traditional methods, graph databases naturally fit the substrate-arrow-product model traditionally used by chemists, offering a modern alternative to store and access chemical knowledge. This framework facilitates a universal chemistry approach, allowing to share and combine data from many different sources and organisations, and enabling new ways to optimise the complete route selection process.