4 ms·

That’s just minimising the accomplishment, predicting structure need not be the end of the process for it to be a significant advance.

• natechols 8y ago
  It's not minimizing the accomplishment - it's an impressive technical achievement, but every new advance in computational modeling for the last several decades has been touted as a revolutionary step forward for drug discovery, and every time it turns out to be an incremental improvement at best. Actually producing a marketable drug is far more difficult than simulating how two molecules fit together.
• timr 8y ago
  No, you're exaggerating. I work in the field. Ab initio structure prediction (what this is) is an interesting technical challenge, but has little to no direct impact on biologics (or any other kind of) drug discovery. The tools and technologies sometimes end up translating but that's a long-term process.
  • jf- 8y ago
    I think you’re suffering from a lack of imagination. It’ll be interesting to see where closed loop directed evolution[0], which is what I was alluding to above, ends up in five years with these kinds of advances. [0]https://en.m.wikipedia.org/wiki/Directed_evolution https://en.m.wikipedia.org/wiki/Directed_evolution
    • timr 8y ago
      Ab initio structure prediction has nothing to do with directed evolution. It doesn't enable it or make it better. Directed evolution is a laboratory technique that depends on scale and cycle speed -- huge numbers of variants are screened rapidly against an assay during each round. Ab initio structure prediction adds nothing to the process. You might argue that you could then predict the structure of the best variants, but predicted structures are all but useless for drug discovery.
      • jf- 8y ago
        Think of this: You have a digital library of sequences that are fed into an AI that predicts structure. The structures are fed into an AI that predicts the desired function based on that structure. You use an optimisation algorithm to identify candidates. You synthesise the most promising candidates and use those as the basis for generating a physical library, and then screen as usual. You now have a way of navigating sequence space far more effectively, so you can explore more of it. You could also potentially use the results to feed back into the system regarding function, so it could become smarter over time.
        • natechols 8y ago
          You're describing what protein design researchers have been doing already for years already. Except for predicting the desired function, which AlphaFold doesn't do either - as the structural genomics projects of the 2000s found out, having the protein structure doesn't magically tell you what it does in vivo.
          • jf- 8y ago
            > You're describing what protein design researchers have been doing already for years already. If that’s so, can you link to any supplementary material about it? Particularly with respect to how machine learning is being used, how the candidate selection process works etc. I’m curious about the subject. > Except for predicting the desired function, which AlphaFold doesn't do either - as the structural genomics projects of the 2000s found out, having the protein structure doesn't magically tell you what it does in vivo. Protein function prediction is a real thing, and it requires knowing the structure. Good structure prediction is a step towards this.
            • timr 8y ago
              Protein function prediction does not require knowing the structure. In fact, nearly all function prediction is done on protein sequence alone, using alignment algorithms.