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We still can't predict much of anything in biology
Wilke (2025): We still can't predict much of anything in biology
The author writes about the limitations of even state-of-the-art computational biology tools such as AlphaFold or BindCraft. He provides three reasons why these tools do not live up to their expectations:
First, biology is really difficult. Just because something works in one system doesn't mean it'll work in another. Second, there is publication bias. The examples that work get published, the ones that don't work do not. Third, experienced PIs develop an instinctual understanding of which specific problems are amenable to their methodology. They may subconsciously or deliberately choose problems where the likelihood of success is high.
Moreover, he argues that biological systems are, in reality, too large to be modeled just by equations to be solved by the computer:
Biology is just physics and chemistry, and in principle we should be able to write down the equations of motion of any biological system and solve them numerically. In practice, however, any realistic biological system is way too large for this approach and we don't have the required compute. It's not feasible to rent an entire supercomputer for a year just to calculate the fitness effect of a single mutation. So we have to rely on shortcuts. The shortcuts that work are database lookups and interpolation.
And this argument is only about protein structure prediction. Next come protein function and other molecules that play a role. "You will encounter new challenges and complications at every new level of organization" , the author concludes.
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