"I wrote Machines of Loving Grace because I didn’t feel the AI industry was painting an inspiring enough picture of how the technology could radically transform the world for the better. The bulk of the essay is devoted to refuting skepticism of AI’s potential in health and biology, and showing why I think it will actually be possible to cure most human disease in ~5-10 years, as crazy as it may sound to ordinary people and frankly to biologists as well (I used to be one!). And, if you read my most recent essay (Policy on the AI Exponential), I discuss concrete proposals for how to streamline the FDA process to make sure the deluge of AI-accelerated drugs isn’t slowed down by the regulatory process."
This is fantasy, and I believe the vast majority of those actually working in pharmaceutical R&D would agree. Contrast Amodei's opinion here to the Derek Lowe post I shared recently: https://news.ycombinator.com/item?id=49313367 . Derek Lowe's opinion is closest to my own experience: AI, whether it be traditional ML or LLMs, can help here and there -- the former to help you to triage paths to explore in a way that's a little better than intuition in some cases, the latter mostly to generate code faster in the code-dependent aspects of pharma research -- but neither of these things are significantly widening the main bottlenecks. I don't think the data exists to do so, especially since so much of pharma R&D is looking for higher and higher hanging fruit (i.e. exploring avenues for which a trove of relevant training data does not already exist).
We need proof that AI can climb the vertical axis and is not just horizontally expanding on what we already know. ANNs (including LLMs) are approximators of things that already exist , but science occasionally needs paradigm shifts and we have not yet seen any of it with AI.
This is fantasy, and I believe the vast majority of those actually working in pharmaceutical R&D would agree. Contrast Amodei's opinion here to the Derek Lowe post I shared recently: https://news.ycombinator.com/item?id=49313367 . Derek Lowe's opinion is closest to my own experience: AI, whether it be traditional ML or LLMs, can help here and there -- the former to help you to triage paths to explore in a way that's a little better than intuition in some cases, the latter mostly to generate code faster in the code-dependent aspects of pharma research -- but neither of these things are significantly widening the main bottlenecks. I don't think the data exists to do so, especially since so much of pharma R&D is looking for higher and higher hanging fruit (i.e. exploring avenues for which a trove of relevant training data does not already exist).