korrents

protein structure prediction

Predicting the three-dimensional shape a protein folds into from its sequence, a problem structural biology worked on for decades before AlphaFold.

FilterEveryone, all time9 from 4 people
Who Everyone DHDemis Hassabis 5 EHElliot Hershberg 2 DLDerek Lowe 1 JDJeff Dean 1
When All time Past 1 yearPast 3 yearsPast 5 years
  1. JD

    Jeff Dean quoted

    Chief Scientist at Google DeepMind and Google Research

    Our readingA narrow, highly accurate model can beat a general one inside its own domain, as AlphaFold did, and materials science and chip design are next.

    design

    Their wordsI mean I think like if you look at uh my colleagues work on say alpha fold that was a very specific model for uh protein folding and it was highly successful um and was able to really handle that domain quite well so that all of a sudden you now have this amazing tool and model that can give you answers to questions about proteins and their structure um really effectively um but it's not a general model it's a very specific one and there are other I domains where that kind of approach can work really well. Uh maybe in material science or chip design or things like that that uh will enable you to leverage the capabilities of a very accurate but but niche model uh to do things that are hard today.

    ↗Jeff Dean: The 1% Rule for Building in AIyoutube.com 15th of 22 in this recording

  2. 8 months earlier
  3. EH

    Elliot Hershberg quoted

    Genomics researcher and investor

    Our readingAI models can be more accurate than physical experiments by averaging out noise, incorporating priors from large datasets, and interpolating between data points.

    Their wordsCompared to experiments, AI models have a few advantages: They can average out background experimental noise, incorporate priors from massive data sets like the PDB, and interpolate between nearby data points.

    ↗On AI Infrastructure in Biologycenturyofbio.com 3rd of 3 in this piece

  4. 4 months earlier
  5. DH

    Demis Hassabis quoted

    Co-founder and CEO of Google DeepMind

    Our readingWe have not scratched the surface of what classical, non-quantum computers can do; they can already go far further than anyone expected.

    Their wordsI think we've proven, and the AI community in general that classical systems, Turing machines can go a lot further than we previously thought. They can do things like model the structures of proteins and play go to better than world champion level. And a lot of people would've thought maybe 10, 20 years ago that was decades away, or maybe you would need some sort of quantum machines to quantum systems to be able to do things like protein folding. And so I think we haven't really even sort of scratched the surface yet of what classical systems so-called could do.

    ↗Demis Hassabis: Future of AI, Simulating Reality, Physics and Video Games | Lex Fridman Podcast #475youtube.com 3rd of 20 in this recording

  6. 3 weeks earlier
  7. EH

    Elliot Hershberg quoted

    Our readingDeep learning has effectively solved the problem of predicting a protein's 3D structure from its sequence.

    Their wordsAfter decades of research-including lots of attempted math and software 1.0-the problem of 3D protein structure prediction was effectively solved by deep learning.

    ↗What Are Virtual Cells?centuryofbio.com 2nd of 3 in this piece

  8. 9 months earlier
  9. DL

    Derek Lowe quoted

    Medicinal chemist

    Our readingAI will not produce a big breakthrough in biological understanding any time soon, because biology has nothing like the clean, complete, high-quality data that made protein-structure prediction work.

    Their wordsSo for these reasons, I (unfortunately) do not expect any big AI-driven breakthroughs in biological understanding any time soon.

    ↗AI and Biologyweb.archive.org 4th of 4 in this piece

  10. 3 years earlier
  11. DH

    Demis Hassabis quoted

    Our readingAccurate computational approaches are needed to close the protein structural coverage gap and enable large-scale structural bioinformatics.

    Their wordsAccurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.

    ↗Highly accurate protein structure prediction with AlphaFold (with 33 co-authors)doi.org All 4 from this paper

  13. DH

    Demis Hassabis quoted

    Our readingExisting protein structure prediction methods fall far short of atomic accuracy when no homologous structure is available.

    Their wordsDespite recent progress10-14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available.

    ↗Highly accurate protein structure prediction with AlphaFold (with 33 co-authors)doi.org All 4 from this paper

  15. DH

    Demis Hassabis quoted

    Our readingA computational method can regularly predict protein structures with atomic accuracy even when no similar structure is known.

    Their wordsHere we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known.

    ↗Highly accurate protein structure prediction with AlphaFold (with 33 co-authors)doi.org All 4 from this paper

  17. DH

    Demis Hassabis quoted

    Our readingContemporary physical and evolutionary structure-prediction approaches fall far short of experimental accuracy without a close experimental homologue, limiting biological utility.

    Their wordsDespite these advances, contemporary physical and evolutionary-history-based approaches produce predictions that are far short of experimental accuracy in the majority of cases in which a close homologue has not been solved experimentally and this has limited their utility for many biological applications.

    ↗Highly accurate protein structure prediction with AlphaFold (with 33 co-authors)doi.org All 4 from this paper