Some doctors often cheer AI for its potential to advance medicine. Though this seems true, one notes that very few new antibiotics have been developed in the last few decades. This has caused concern some resistant infections may be hard or impossible to treat.
AI has been offered as a way to review potential medications and identify strong candidates. Testing is still expensive and takes time. But I am unaware of a long pipeline of new antibiotics. Still, if it were available it would be strong proof of the benefits of AI in this domain.
So my question is, what similar developments would convince you that AI provides strong benefits commensurate with its unknown risks, in different domains?
It’s probably worth noting here that the “AI” systems that are developing new drugs (or at least, trying to) aren’t particularly related to ChatGPT and its ilk.
This is a good point. Most of the recent usage of the term ‘AI’ seems to relate to Large Language Models (LLMs): basically text-based generative systems trained on an enormous amount of ‘scraped’ material from the internet.
My impression is that these are all generically rather similar?
Does anyone know of a good technical summary that explains the similarities (or differences) between the current generation of these (ChatGpt, Claude, Gemini etc etc…)?
But it ain’t necessarily so: this is certainly the dominant model for ‘AI’ at the moment… but it is probably not a pathway to the Holy Grail of true ‘Strong general AI’?
I’m probably about as dubious about the wonders of AI in its current form as anybody. However, AI hasn’t been around in its current form, or anything like it, for the last few decades. More like the last couple of years, isn’t it, and changing all the time? So why would there be a decades long pipeline of new drugs from it?
Total WAG, but since this is IMHO, one of the low-risk applications of AI in medicine is to offer theories and suggestions that can be validated either by testing and/or cross-reference with other data. Thus ISTM that AI assistance in creating new medications can be valuable in areas like predictive analysis of molecular behaviours and how they may interact with human biology, all subject to exhaustive testing and validation once a potentially new drug has been developed.
This is analogous to an important distinction I’ve made before, the difference between asking a generative AI, on faith, to diagnose a set of medical symptoms, versus using it to suggest appropriate probing questions, like “given these symptoms, what should I ask my medical specialist to elicit the most complete possible information?”.
By the same token, AI can potentially be very useful in pharmacodynamics to answer the question, “what is likely the most productive path forward in developing a drug to treat this particular disease?”.
So the last new class of antibiotics was discovered in the 1980s. Some blame a lack of incentives for this gap, but newer fourth-generation antibiotics are very expensive so this is not a complete explanation.
In his book Super Agers, Topol talks about goweeeo neural networks looked at millions of candidates. For C. Difficile and M. Tuberculosis 107 million possibilities were reduced to eight (Stokes, Cell, Feb. 20, 2020). Impressive, but not much has been heard since then AFAIK. A new drug, lolamicin, has been said to work against bacteria without disturbing friendly microbiome bacteria in the gut. But after the initial hype, again AFAIK not much has been said since.
Sure, AI is new and this research problem is so old that progress in this area could be credited easily to AI. Of course vaccines may help with done things but that is a different kettle of fish.
Expensive isn’t enough. Antibiotics are used selectively and for short periods of time; the market incentive is much less than a new lifestyle medication that will be taken for the remainder of someone’s life.
Then there is the bit you already note: identifying targets is one thing; testing them is still a laborious many years real world process. And the drug has to be in a form that works pharmokinetically. Thus there is the exciting and cautionary example of halicin: discovered but not yet developed: