#11thICCS Christos Nicolaou on Advancing automated syn via rxn data mining and reuse
The automated synthesis and purification labs (ASL and ASP). "Pretty cool robots". Scientists come up with workflows, click a button and submit, and if all goes well you get the end product at the other side.
The proximal Lilly collection (PLC). Given our cmpd collection, how can we create a database to cover chemical space more fully. Virtual syn engine-->PLC--->in silico design and selection. "Old news"
Use our ELN as the source of reaction information. Info just sitting there. We fished it out of ELN and other databases to create a single homogenous data repo: Synthory, the Lilly Rxn Repo.
Classify with @nmsoftware NameRxn into RXNO reaction ontology.
What did we find? Overall similar to other papers. Top 20 cover 50%. 88% of reactions in ASL are recognized.
Want to use this info for structural design. Synthetic route pred (ChemoPrint). The training is done as follows. Many steps but straightforward. Reverse rxns. Calc rxn signatures. Define rxn classes based on those signatures (uniqification). Take a representative from each class
Signatures differ on atom typing. Want to take into account charge?, aromaticity? etc.
RRT (reverse rxn template) repo. Can now use it to carry out rxns. For a hypothesised structure, search for match, checking building block availability, and then...
Typically we end up with many many routes. Everything can be debrominated if it has a bromine. Need to assess synthetic feasibility of a hypothesis and then make/test the cmpd.
Which route should I push onto the robot? We have not looked at other functional groups the building block might have, or duplicate functional groups.
How do we assess routes? Synthex. We have synthesis execution templates - all those rxns that have been run on the robot already. Start by asking the experts: heuristic rules - how often have they been done, how easy, how successful.
Forward rxn check, brute force. Try whether the reactants match the templates. If more than one matches, then they might be undesirable products.
That's slow, so we prefer neural nets to make a prediction. Give it the two reactants and it tells you which are going to fire.
Where are we now? The Big Picture. Everything is done via a web interface that is presented back to the chemist and they make the final decision. This is being implemented, and lots of data is starting to come in. There will be lots of learning to do done.
Example on public data for testing. USPTO reactions from @dan2097. Shows some example routes sorted by support. Everything looks reasonable. Of course there are others not so reasonable.
Now on Lilly Data. Identify route for 10K latest Lilly numbers. Focussed on single-step rxns. Route found in 49.9% of cases. 255 rxn types. ASL feasible: 35.8%. In short, a big chunk of the chemistry the chemists are doing could be done on the robot.
We are reusing Lilly synthetic knowledge. Connecting theoretical syn routes to actionable items.
Are we there yet? The Robo-Chemist.
Are we there yet? The Robo-Chemist.
Routine reactions are/will be feasible via automation, but not complex, more challenging chemistries. But the "routine" set will grow to support synthetic chemists. And the future chemist will increasingly lean on machines for these chemisties.
We are open sourcing it. Either today or tomoroww. It's going out on GitHub. LillyMol project on EliLillyCo GitHub page. Expanding on code previously released.
