Design molecules.Discover medicine.
Design a compound, dock it into a target, follow it through the body, and let an AI pharmacologist find the relationship behind the results. Every part of every experiment is labelled.
Browse the experimentsLet the agent screen.
Eight simulated runs. One fit: the relationship that best explains them.
- 1Hypothesis
- 2Design
- 3Simulate
- 4Analyse
- 5Refine
What if you started from a target?
Describe a disease target in plain words. The research notebook proposes a candidate compound, the properties to aim for, and the experiment that would test it.
Open research notebookFour stages.One pipeline.
Design, make, test, dose. Each experiment is a stage of early discovery, and each hides a relationship the research agent can rediscover from simulated data.
Values come from computational models, not laboratory measurements.
Start with a target.Design a candidate.
Every answer here is a hypothesis to test, not a medicine. Any candidate would still need laboratory testing, preclinical studies and clinical trials.
Your target AI molecule generator
Describe a disease target or a therapeutic goal. The AI proposes a candidate design, the properties to aim for, and the experiment that would test it.
Every medicine starts as a guess.
A good candidate names what it must bind, what it must avoid, and the property most likely to sink it. The simulation is where the guess meets the numbers.
Candidate
What should the molecule look like?
Biggest risk
What would sink it first?
Design the compounds.Reward the discoveries.
$SYNTH is the proposed utility token for Synthesize: spent on advanced molecular generation, screening runs and AI research agents, with a proposed share of platform fees cycling back to researchers who produce reproducible computational results.
Discovery, in a loop.
Design compounds
Users generate and customise experimental molecules.
Spend $SYNTH
Access advanced simulations, screening tools and AI agents.
Generate research
Experiments produce candidate compounds and computational findings.
Reward discoveries
Proposed platform fees fund buybacks and research incentives.
Step 04 feeds back into step 01. Fee shares and buybacks are proposals, not commitments.
How a candidate is earned.
The research agent in the lab follows these five steps on every run.
Identify the target
Select a protein, receptor or disease pathway.
Generate candidates
AI proposes molecular structures for screening.
Simulate interactions
Predict binding, selectivity and pharmacology.
Optimise compounds
Agents refine designs using model feedback.
Publish research
Share reproducible results and candidate hypotheses.
Eight instruments, one laboratory.
AI molecule generator
Turn a therapeutic goal into candidate structures with predicted properties.
Virtual synthesis lab
Run reagents through a flask and see how heat, concentration and catalysts change yield.
Drug interaction simulator
Dock compounds into a target pocket and watch occupancy build and fall.
Pharmacology engine
Follow absorption, distribution, metabolism and excretion through the body.
Disease target explorer
Compare on-target and off-target binding to see where side effects come from.
Autonomous research agents
Agents design runs, screen compounds, analyse results and refine designs.
Compound marketplace
Publish, share and license molecular models, datasets and experiment setups.
Discovery leaderboard
Compete on predicted affinity, selectivity, drug-likeness and reproducibility.