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🦠🤖 We use our computational models to make next-generation antibiotics that outcompete bacterial evolution and precisely target pathogenic bacteria, without harming good microbes or human cells. ☠️ Current antibiotics stop working because bacteria evolve resistance to them. This makes drug-resistant bacteria a looming global health crisis - already killing more people than malaria and AIDS and it is getting exponentially worse 📈. 🧬 Our approach leverages co-evolutionary protein-protein interaction datasets combined with AI to forecast bacterial mutations and create ‘future-proof’ antibiotics, addressing antibiotic resistance before it develops. This changes the game for how frequently society will need to make new antibiotics and how long our new antibiotics will be able to treat patients 👩‍⚕️. We are a team of biochemists and evolutionary biologists who met at the University of Oxford.

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.