onepot's manifesto

At first, no one outside a small circle realized what onepot was building. Sure, biotech watchers were intrigued by rumors of an AI system that could synthesize molecules faster than any CRO. But revolutionary claims emerged every few years—usually to fizzle in supply chain complexity.

Then came the demo: 50 distinct molecules synthesized in one week. Seasoned chemists were stunned—most labs needed two months for that work. The ripple of excitement began—like electricity before lightning strikes.

onepot launched an all-out data campaign, building the largest chemical reactivity dataset ever assembled—billions of real-world experiments using amide coupling, Buchwald-Hartwig, photoredox catalysis, and more.

Specialized microfluidic reactors and mass spectrometry tools analyzed tens of thousands of reactions daily. 90% untouched by human hands—everything automated and orchestrated by our LLM agents.

"This isn't just better synthesis. It's an entirely new engine of discovery."

We trained a colossal AI that could predict reactivity, yields, side products, and purification routes with uncanny accuracy. Fed with "black box" data capturing everything—kinetics, byproducts, real-time MS readouts.

The AI discovered new reactivity pathways no one had coded. It handled multi-step routes in a single pot, finding shorter, cheaper paths than any human chemist could devise.

"The next leap after biologics" — Pharma insiders
"The AWS of synthesis" — Business analysts
"Magic" — Everyone else

A mid-sized pharma bet their entire pipeline on onepot. They ordered tens of thousands of anti-inflammatory scaffolds. Expected timeline: 18 months for a few hundred compounds.

onepot delivered: 10,000 molecules in 3 weeks at a fraction of the cost.

By August, they'd identified promising leads and begun preclinical work. "We compressed a year into a month," their head of medchem said. The pharma giants took notice. The arms race began.

Warehouses transformed into futuristic chemical foundries. Robotic arms, microreactor arrays, mass spec units lined up like automotive assembly lines. Millions of unique molecules per month.

Beyond pharma: materials, agriculture, fragrance. Any enterprise needing small molecules realized onepot was the simplest, fastest solution. The chemical supply chain pivoted from static catalogs to on-demand synthesis.

The platform became unstoppable:

Foundation AI with near-total chemical knowledge. Massive robotics for high-speed experiments. Closed-loop ML predictions and real experiments.

Like cloud computing before it, onepot became universal infrastructure. Geopolitical tensions underscored supply chain vulnerabilities—Western governments treated "domestic molecule synthesis" as national security. No one matched our scale.