Reasoning from
chemistry's first principles.
Chemstack combines molecular structure, reaction physics, and experimental feedback to make decisions about chemistry, not predictions about text.
Chemistry cannot be solved by pattern matching alone.
General-purpose AI is trained to recognise patterns in language and generate plausible responses.
Chemistry requires something different: reasoning about molecular structure, transformations, and reaction conditions to determine what will actually work. Chemstack is built around this distinction.
General AI
Pattern recognition
Learns relationships in language and existing information.
Chemstack AI
Scientific reasoning
structure · electronic structure
mechanism · disconnections · reaction classes
kinetics · thermodynamics · quantum descriptors
observed outcomes · scale-up physics
conditions · regio- & chemoselectivity
Reasons across chemical structure, reaction behaviour, scientific evidence, and experimental outcomes.
General AI can tell you what has been written about a reaction.
Chemstack is designed to reason about whether that chemistry makes sense for the molecule in front of you.
A deeper representation of chemistry.
A reaction tells us what transformed. A procedure tells us how it was performed. Molecular interactions help explain why a substrate behaves the way it does. Chemstack brings these layers together into a scientific representation of applied chemistry.
One shared scientific context for every reasoning system in Chemstack.
These aren’t separate databases. They are connected representations of the same chemistry, each layer giving meaning to the one beneath it. Hover a layer to lift it out.
From chemical understanding to scientific decisions.
Understanding chemistry is only the beginning. Different scientific questions require different kinds of reasoning. Chemstack uses specialized intelligence systems to explore possibilities, predict outcomes, and learn from experimental reality.
Question 01
What is scientifically possible?
Deep ResearchQuestion 02
What is most promising?
Reaction VirtualisationQuestion 03
Why did reality differ?
OptimizationSearch the chemistry space, not just the literature.
Deep Research works the way a chemist does: backwards from the target, breaking it at the bonds most likely to come apart. It generates every viable disconnection, then ranks them on the chemistry that decides whether a route survives: reactivity, selectivity, and the features that make a step work or fail. An ensemble of retrosynthetic, functional-group-aware models does the generating; a chemistry-first ranking model does the scoring.
The goal isn’t the first plausible synthesis. It’s to construct and rank the full chemical search space, so the route you run is the best one, not the first one found.
Under the hood
Visualise the experiment before running it.
Reaction Virtualisation takes the most promising pathways and explores the experimental space around them. It weighs yield, process constraints, cost, and known reactivity to generate candidate DOE conditions.
The Virtual Reactor then reasons about reaction behaviour using molecular structure and quantum descriptors to estimate how those conditions are likely to perform.
Under the hood
Top routes
Evaluate
DOE space · candidate conditions
Top DOE candidates
Virtual Reactor
Predicted outcome
91%
Predicted yield
94%
Predicted purity
+12°C
Temperature Δ
Turn experimental deviations into the next experiment.
Chemistry rarely behaves exactly as predicted. This analyses the gap between expected and observed outcomes, examining reaction conditions, substrate behaviour, and impurity patterns to identify the likely cause.
The result isn’t just an explanation. It’s the next experiment.
Under the hood
Δ −28 pts · the gap is information
Optimization analysis
Root cause
Next experiment
Optimized condition
Discovery, prediction, and learning are connected.
Deep Research expands the possibilities. Reaction Virtualisation evaluates which possibilities are most promising. Optimization learns from what actually happened. Together, they create a continuous scientific feedback loop.
Chemistry World Model
Deep Research
Possibilities
Reaction Virtualisation
Prediction
Optimization
Explanation
Laboratory
Reality
Experimental Evidence
An experiment never simply ends a workflow. It makes the next decision better.
Built around chemistry, not a language model.
01
Chemical context
Reasons about molecular structure, reactions, conditions, and behaviour together. Not text.
02
Physics-informed prediction
The Virtual Reactor reasons from thermodynamic, kinetic, and molecular signals to evaluate how a reaction will behave.
03
Experimental feedback
Lab results feed back in, sharpening the next prediction.
04
Specialised reasoning
Each scientific question goes to a purpose-built reasoner, not one general-purpose agent.
From chemical possibility to scientific confidence.
Three reasoners, one loop. Deep Research finds the routes, Reaction Virtualisation predicts them, Optimization learns from what the bench actually did, and every result sharpens the next prediction.