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Foundation Model for Chemical Manufacturing
Yoneda Labs provides software to help chemists optimise reactions. When chemists make a drug or a material, we help them figure out the best reaction parameters such as temperature, concentration and catalyst. When Jan was working at chemical labs, he experienced the struggle of spending weeks guessing reaction conditions. We then started experimenting with ML to speed up the process. Now, as a team of three friends from the University of Cambridge, we’ve spent the last month combining our domain expertise in Computer Science, Machine Learning and Chemistry to develop state of the art models for reaction optimisation. Although ML is becoming well established in other fields, current chemical models generalise poorly and require lots of programming experience. We make our models easily accessible to chemists in the lab. Finding the right conditions quickly allows pharmaceutical companies to test more drugs, and finding better optima makes manufacturing process cheaper and more environmentally friendly.
Yoneda Labs provides AI-powered chemistry software designed to optimize chemical reactions and analytical workflows. Their platform aims to increase chemists’ productivity by predicting conditions, optimizing processes, and analyzing LCMS data, with industry adoption across agriculture, pharma, and chemical manufacturing.
Yoneda Labs offers a suite of AI-driven tools for chemistry workflow optimization: Predict, which trains AI models on large experimental datasets to forecast reliable reaction conditions and enable faster lead compound nomination; Optimize, which identifies optimal process conditions to maximize yield while reducing cost and environmental impact; and Analyze, which automates LCMS data processing by detecting peaks, linking them to masses, visualizing results on a single page, and reducing manual labor per chromatogram. The solutions are presented as software that can be scheduled for demos and integrated into bench workflows to accelerate development timelines from months to days.
Who it’s for: Chemical manufacturing companies, including sectors such as Agriculture, Big Pharma, and Chemical Manufacturing, that perform reaction optimization, process development, and LCMS data analysis.
Website shows active product demos/book-a-demo, mentions of benchmarks and client quotes, and company blog with industry applications, indicating product traction and active marketing.
Michał is the Co-Founder and CEO of Yoneda Labs. Michał studied Computer Science at the University of Cambridge. He has strong software engineering and Artificial Intelligence experience having worked at Jane Street and Berkeley AI Research.
Jan is the Co-Founder and Chief Scientist at Yoneda Labs. Jan studied Chemistry & Biology at the University of Cambridge. Has worked at Bayer and as a research chemist for institutes in Czechia and Austria. He has a lot of experience designing ML models to solve problems in Chemistry and Biology, most notably he designed and sold a model for cell segmentation. He scored 8th/10th/11th at the international Chemistry Olympiad.
Daniel is the Co-Founder and CTO at Yoneda Labs Daniel studied Computer Science at the University of Cambridge. He has strong software engineering experience having been a software engineer at Cisco, Optiver and a programming languages researcher at the University of Cambridge. He won first place at the international robotics competition Pi Wars, and ranked 5th in the Cambridge Computer Science cohort.
Enabling new chemistries with AI
Yoneda Labs builds a foundation model for chemical reactions to help chemists define conditions (temperature, concentration, catalyst) and optimize organic reactions using AI, aiming to make synthesis faster and cheaper. The product targets chemical processes in labs, with an offline app for scientists to learn from their experimental data and identify optimal conditions.
From the original launch (Jan 2024) — may be outdated.
Formerly “ReAgentAI” · why startups rename →
AI Co-Pilot for Chemical Process Optimization
Enzyme design models.
