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The main document in a clinical trial is the study protocol. At large companies, floors of people use the protocol to create the study data collection forms, clinical database design, error checks, analysis code, and data transformation mappings. We use AI to automate the process of creating everything from the protocol, saving not only spend on headcount but also months of time which can translate to up to $27M in direct costs + lost revenue saved for a single phase 3 trial.

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.