What we just have no evidence for, to my knowledge, is someone with no medical background doing the same. Instead they wind up buying dodgy supplements and peptides.
Fully embodied robots and fully automated assembly lines would also help with the production barrier. Regarding the FDA, that is a valid point, but it could be irrelevant if the AI developed drugs are significantly superior to previous alternatives. In such case, FDA acceptance would be shortened, or even bypassed.
> Maybe if we have fully embodied robots and fully automated assembly lines to generate organic compounds and test them with extremely realistic simulations it may help slightly
Anthropic has got biolabs: https://www.anthropic.com/research/claude-discovers-novel-en...
I legitimately don't believe it's inconceivable they could get to the point where there is treatments good enough that Cancer becomes "solved" in similar ways to AIDS.
> Neural networks in drug discovery: have they lived up to their promise?
"Over the last decade neural networks have become an efficient method for data analysis in the field of drug discovery. The early problems encountered with neural networks such as overfitting and overtraining have been addressed resulting in a technique that surpasses traditional statistical methods. Neural networks have thus largely lived up to their promise, which was to overcome QSAR statistical problems. The next revolution in QSAR will no doubt involve research into producing better descriptors used in these studies to improve our ability to relate chemical structure to biological activity"
> The use of neural networks in drug discovery started at the end of the 1980's when they were applied to various chemical problems including the study of quantitative structure–activity relationships (QSAR)
2002.
https://link.springer.com/chapter/10.1007/3-540-45984-7_6
Not enough robots and silicon for you?
> Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach
In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure–activity and quantitative structure–property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.
Biologists aren't slouches. The thought about using computers to process biological data has been "obvious" for as long as there has been computers. AI and biology have been a thing since the Dartmouth conference. A lot of things that could have been tried have already been tried, but I guess we'll keep trying until something works?
There ain't much room for miracles here.
It even says, right there in your quote: "It is expected that the power of CADDD will grow as the technology continues to evolve." This ~is~ technology evolving, and it's happening at a pace that people don't appreciate.
There's a long history of Silicon Valley people thinking they can just bring in their experience and with a bit of Moore's law brute-force their way into miracles.
AutoDesk tried this with molecular design. https://www.fourmilab.ch/autofile/e5/chapter2_82.html
Lots of Silicon Valley people in the late 1990s thought they could be on the ground floor with nanotechnology design.
The chemist Derek Lowe 10 years ago used the phrase "Andy Grove fallacy" to describe the optimistic vision that data + massive computer power = cure for diseases. "[T]he idea that understanding human disease and its treatments should be pretty much like designing a new chip or writing a new app", quoting the 2015 essay titled "Silicon Valley Sunglasses" at https://www.science.org/content/blog-post/andy-grove-rich-fa... .
Why Grove specifically? He links back to his 2007 essay at https://www.science.org/content/blog-post/andy-grove-rich-fa... : "So I see that Andy Grove, ex-Intel, is telling everyone that the drug industry could use some of that Moore's Law magic. I've noticed that people who spend a lot of time in the computer business often have an. . .interesting perspective on what constitutes progress in other fields, and we might as well appoint Grove the spokesman for their worldview"
AI has made good progress in text, code, math, digital images, and digital music. Basically troves of data that can be automated and trained on, validated, and with a purely digital interface. Outside of that, just gathering the data will be difficult, regulated, and sparse (that is if anyone will even give it to you now). While we can write a test to make sure we could generate drugs of the past, verifying we have generated any new working correct drugs is an RL loop with one iteration costing years and millions of dollars.