It was great to see our HTE chemist, Kaiman Cheung, back at his alma mater last week, speaking on a careers panel at the University of Bristol.
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It was great to see our HTE chemist, Kaiman Cheung, back at his alma mater last week, speaking on a careers panel at the University of Bristol.
At ReactWise, Kaiman focuses on the generation of large experimental high-quality datasets that feed our zero-shot prediction models, including HTE plate design, execution, and quantification.
The panel covered a lot of ground, and Kaiman's contributions centred on AI's role in chemistry.
AI won't “solve chemistry”, but will become part of how chemistry is done. The question is in getting the balance right - where can different algorithms and models actually support chemists - so we have to be honest about the trade-offs: AI has a real footprint, but so does running unnecessary reactions, wasting solvents, and scaling bad processes.
He also spoke about what it is like to work at ReactWise. When generating large datasets, the value added lies in choosing the right experiments, conditions, and structure to make the result useful for our clients. This range of responsibilities and ownership is just part of how a startup works.
It's always good to see our team sharing insights into working at a chemistry-first software startup.
