Join Date
October 2024
Country
Gender
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Age
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About Jan Oboril
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.
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.
Companies & Work
Yoneda Labs
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.
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.
