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> has since its release been an ongoing fountain of innovation for biomedical (and particularly pharmacological) application. [Citation Needed] Last time I lo

> has since its release been an ongoing fountain of innovation for biomedical (and particularly pharmacological) application. [Citation Needed] Last time I looked into it, as is often the case, the ‘actual’ results were much much more sobering than the headlines seemed to suggest.

falcor84 2y ago
Yes, the actual results are definitely not as impressive as the overly hyped headlines, but there's still a lot. First off, in terms of research building up on top of it, as of today, Pubmed shows 9,364 articles citing their 2021 paper, and Google Scholar shows 21,719 results as a whole[1], but these include non-biomedical papers (e.g. applications of similar ML models to other disciplines). As for actual applications, it's a bit early, but here's a relatively extensive review from 2023[2], and there's a particularly notable example showing progress on liver cancer [3]. Researchers are still figuring out how to utilize it, e.g. with search mechanisms like Foldseek[4], and I'm sure we'll see a lot over the coming years. But you're definitely right it's not a silver bullet on its own, and there's still need for actual scientific research. On this note there's this interesting paper on the "illusion of understanding" we get from working with AI. [0] https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed_citedin&from_uid=34265844 https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed_cite... [1] https://scholar.google.com/scholar?cites=6286436358625670901&as_sdt=2005&sciodt=0,5&hl=en https://scholar.google.com/scholar?cites=6286436358625670901... [2] https://www.nature.com/articles/s41392-023-01381-z https://www.nature.com/articles/s41392-023-01381-z [3] https://www.genengnews.com/insights/first-application-of-alphafold-in-identifying-potential-liver-cancer-drug/ https://www.genengnews.com/insights/first-application-of-alp... (paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906638/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906638/) [4] https://www.nature.com/articles/s41587-023-01773-0 https://www.nature.com/articles/s41587-023-01773-0 [5] https://www.nature.com/articles/s41586-024-07146-0 https://www.nature.com/articles/s41586-024-07146-0

falcor84 2y ago

Yes, the actual results are definitely not as impressive as the overly hyped headlines, but there's still a lot. First off, in terms of research building up on top of it, as of today, Pubmed shows 9,364 articles citing their 2021 paper, and Google Scholar shows 21,719 results as a whole[1], but these include non-biomedical papers (e.g. applications of similar ML models to other disciplines). As for actual applications, it's a bit early, but here's a relatively extensive review from 2023[2], and there's a particularly notable example showing progress on liver cancer [3]. Researchers are still figuring out how to utilize it, e.g. with search mechanisms like Foldseek[4], and I'm sure we'll see a lot over the coming years. But you're definitely right it's not a silver bullet on its own, and there's still need for actual scientific research. On this note there's this interesting paper on the "illusion of understanding" we get from working with AI. [0] https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed_citedin&from_uid=34265844 https://pubmed.ncbi.nlm.nih.gov/?linkname=pubmed_pubmed_cite... [1] https://scholar.google.com/scholar?cites=6286436358625670901&as_sdt=2005&sciodt=0,5&hl=en https://scholar.google.com/scholar?cites=6286436358625670901... [2] https://www.nature.com/articles/s41392-023-01381-z https://www.nature.com/articles/s41392-023-01381-z [3] https://www.genengnews.com/insights/first-application-of-alphafold-in-identifying-potential-liver-cancer-drug/ https://www.genengnews.com/insights/first-application-of-alp... (paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906638/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9906638/) [4] https://www.nature.com/articles/s41587-023-01773-0 https://www.nature.com/articles/s41587-023-01773-0 [5] https://www.nature.com/articles/s41586-024-07146-0 https://www.nature.com/articles/s41586-024-07146-0