System and method for automated retrosynthesis

A system for recursive retrosynthesis of molecules, comprising:

• a computing device comprising a memory, a processor, and a non-volatile data storage device;
• a data curation platform, comprising a first plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
• scrape data from a plurality of data sources accessible via the internet;
• parse the scraped data into a format that may be stored in a relational database;
• construct a knowledge graph from the information in the scraped data, the knowledge graph representing informational entities as vertices of a graph and relationships between the entities as edges of the graph;
• a machine learning algorithm operating on the computing device, the machine learning algorithm having been trained to predict valid chemical reactions;
• a commercial availability database stored on the non-volatile data storage device, the commercial availability database comprising a list of commercially available molecules;
• a disconnection rules library stored on the non-volatile data storage device, the disconnection rules library comprising a set of rules for breaking of molecules into precursors based on the knowledge graph;
• a recursion engine comprising a second plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
• track stages of recursive retrosynthesis;
• for each stage of recursive retrosynthesis;
• select a molecule for retrosynthesis;
• send chemical notation for the molecule to a retrosynthesis engine;
• receive a pre-determined number of sets of possible precursors for the molecule;
• create a hypernode of a hypergraph for each of the sets of possible precursors;
• perform forward reaction prediction of the possible precursors in each hypernode using the machine learning algorithm;
• assign a reinforcement learning reward to the hypernode based on the forward reaction prediction;
• backpropagate the reinforcement learning reward through previous stages of the recursive retrosynthesis;
• search the commercial availability database to determine whether the precursors are commercially available;
• end the recursive retrosynthesis when a set of precursors has been found which is commercially available;
• and
• a retrosynthesis engine comprising a third plurality of programming instructions stored in the memory which, when operating on the processor, causes the computing device to:
• receive the chemical notation for the molecule from the recursion engine;
• using the disconnection rules library, generate the pre-determined number of sets of possible precursors for the molecule;
• and
• send the sets of possible precursors back to the recursion engine.