biomedical AI 从业者：解决生物学的瓶颈是数据而非算力
一位做了7年 biomedical AI 的人拆解了 AlphaFold 背后的数据积累，观点很冷静。
一位在 biomedical AI 领域工作 7 年以上的从业者发文认为，用 AI 完全解决生物学的瓶颈不在算力而在数据。他举例称，一个疾病相关蛋白可能在 0.1% 的时间里短暂采用罕见构象，模型即使精确捕捉主导构象也可能错过关键的疾病相互作用。他以 AlphaFold 为例指出其成功依赖的是 PDB 数据集数十年的实验积累，而非单纯更多算力。他主张前进路径是更好的实验、更好的数据、更好的模型之间的反馈循环，算力的作用是帮助决定下一步测量什么。
This person has blocked me (I have no clue why and for how long)
but since it's being widely discussed I thought I would share my thoughts, having worked in the field of biomedical AI for 7+ years.
"Solving" biology is not limited by compute but BY DATA.
That doesn't mean you can't do impactful bio work with more compute and existing datasets. I actually think applying AI to existing datasets we can make many advances in biology and medicine. (much of what we do by @SophontAI is along these lines)
But to "fully solve" biology, you need to capture the full complexity of biological systems and we're not even close to fully observing it.
With near-infinite compute you could probably solve it. Even then, you need a starting condition, and how would you define it if you aren't able to comprehensively characterize the system in the first place.
What about approximating biology? The challenge here is it's hard to know what parts matter and what can be approximated away.
Consider a disease-associated protein that transiently adopts a rare conformation 0.1% of the time, which enables an interaction that contributes to disease.
A model could capture the dominant conformation very accurately and still miss the interaction that matters to disease biology.
More compute could help us sample that rare state. But we also need to know the relevant cellular conditions, binding partners, concentrations, etc. Otherwise we could spend enormous compute precisely simulating a system that doesn't match the biology we're trying to understand.
As a sidenote, AlphaFold is a bad example here. AlphaFold only succeeded because of the existence of the PDB dataset, which was collected over decades. EXPERIMENTS WERE NEEDED TO MAKE ALPHAFOLD NOT JUST MORE COMPUTE.
As you all know, I'm very optimistic about AI accelerating biology. But the path forward is a feedback loop: better experiments → better data → better models → better experiments.
Compute can help us get much more out of what we've measured and decide what to measure next. It doesn't automatically tell us which unmeasured details matter.
