Computational biology is the quiet AI win ↗
Hot take: AI hypothesis generation gets the headlines. AI lab robots get the demos. Meanwhile, computational biology/bioinformatics is quietly doing more for actual scientific output than either.
Aside from protein structure predictions, nobody set out to revolutionize computational biology. It probably happened as a side effect of teaching AI to write code. Whether you're a bench scientist who touches data occasionally or a bioinformatician who lives in it, the impact is enormous, and I think a lot of people who claim to build AI scientists haven't fully realized it yet.
I've been doing bioinformatics for over 12 years across most data types biology produces, and I haven't been THIS excited in a long time: One, speed and scale of analyses I already know have gone through the roof and second, things I always wanted to do but didn't have the bandwidth for become possible.
After a few months of vibe science-ing, I can tell that there's definitely a lot that can be done wrong and that many easy unlocks remain underused. Over the coming weeks, I'll share some of my observations and easy-but-impactful fixes: Dataset assembly at scale. Multi-agent reviewer patterns. Forcing the model to 'see' its own mistakes... (this stuff is addictive!)
If you're figuring this out too, I'd love to hear what's worked for you.
(P.S.: I'm sharing these learnings in my personal capacity, on my own machine, using non-Alphabet tools and compute.)
