Kim Branson, Global Head of AI/ML at GSK, discusses why high-quality human clinical outcome data represents the ultimate bottleneck and competitive moat in AI-driven drug discovery.
Insight
Branson warns biological models showing 0.9 accuracy usually contain underlying errors
“In biology, as you know, like, the best model you can build has, like, a figure of merit of accuracy, like.6, if it's .9, anything that looks great, there's something wrong.”
Prediction Not checkable as stated
Branson predicts every drug launched by GSK will have accompanying software
“So every, every software, every drug VSK will launch will have software that sits around it. I think that's gonna be true of everybody, and there's, we're going to see, there's software that will sit around every drug, and we'll say, you know, who should use i…”
Assertion Not checkable as stated
GSK's active learning target discovery is 20% faster than random screening
“That's about 20% faster we've shown than doing a random screen.”
Insight
Branson notes simple ML models yield most gains over complex ones
“Basically the simple stuff gets you there, and for the extra five percent gained, you need all the extra complexity. I think that is still true now.”
Assertion Supported
GSK made AI core to its strategy via in-house functional genomics
“They're fully investing in the things I thought were very important at the time, which was, you know, these large genetic databases coming online and functional genomics. So he was way ahead in the, in sort of the thinking about CRISPR and like the gene pertur…”
Insight
Branson applies Amdahl's law to companies balancing productive work and communication
“Armdahl's Law isn't just for CPUs, it's for companies, so that ratio of, like, computation to communication, right, is really important.”