Discovery Teams
Ideation Beyond Search
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ChemAIRS® combines machine learning and chemical intuition to generate feasible, diverse synthetic routes for both existing and novel molecules, offering multiple reaction strategies so teams can optimize for yield, cost, complexity, or route length.
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ChemAIRS® integrates tens of millions of building block data points to identify shorter, more efficient routes, reducing synthesis steps by 20 to 70 percent. By accessing a team's own ELN (Electronic Lab Notebook) entries, it also identifies synthetic routes with a track record of experimental success, improving reproducibility across a program rather than starting from zero on every target.
Retrosynthesis in Practice:
Route Design for Daraxonrasib (RMC-6236)
RAS(ON) inhibitor
Read the case study AgrochemicalRoute Design for Corteva's New Insecticide
New ISO common name granted
Read the case study 9 Steps → 4Route Design for Sonrotoclax (BGB-11417)
Late-stage head group installation
Read the case studyForward Synthesis for Discovery Teams
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Forward Synthesis is a computational tool that lets teams create and evaluate molecular libraries before running synthesizability screenings, working across two dimensions: the core skeleton that defines a molecule's structure, and the synthetic route that determines how it can be built.
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ChemAIRS® helps teams rapidly generate large-scale molecular libraries while ensuring every candidate is actually feasible to synthesize, filtering out structures prone to instability, degradation, or toxicity risk before they ever reach a chemist's bench.
SA Score for Discovery Teams
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SA Score evaluates synthesizability with AI-driven precision, helping teams assess feasibility and prioritize the most accessible compounds by analyzing building block availability, reaction complexity, and cost.
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ChemAIRS® evaluates hundreds of thousands of molecules in hours, ranking them by synthetic accessibility so a full program's candidate pool can be triaged overnight. In a 1,346-molecule validation study, ChemAIRS® matched Merck's expert synthesis team, with its AI-driven assessments identifying viable intermediates that human experts had overlooked.
SA Score in Practice:
Discovery programs, whether in pharma, agrochemicals, or materials, generate years of internal synthesis data: ELN records, past routes, prior successes and failures, that rarely gets reused systematically across a portfolio. ChemAIRS® combines AI-generated route design with your own experimental history, so route proposals reflect not just published chemistry, but what your team has already learned, across every target in the pipeline.
How AI Connects Route Design to Your Team's Own Data
Retrosynthesis for Discovery Teams
Discovery Case Studies
Examples spanning route design, synthesizability, and internal data reuse across real discovery programs.
See ChemAIRS® on Your Own Program
Route design that learns from what your team already knows.