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:
I don’t think my link is working so here’s another one to a more academically oriented article:
Recent advances in artificial intelligence (AI) and machine learning (ML) have introduced a paradigm shift in drug discovery. These technologies are capable of processing vast chemical and biological datasets, recognizing complex patterns, and predicting bioactivity with unprecedented speed and accuracy. AI-based models can screen millions of molecules in silico, reducing both time and costs while accelerating hit identification [8]. A landmark example of this approach is the discovery of halicin, a compound originally developed as a c-Jun N-terminal kinase (JNK) inhibitor for diabetes and later repurposed as a potent antibiotic through deep learning algorithms developed at MIT [9].
Halicin exhibits a broad-spectrum antibacterial activity distinct from classical antibiotics …
… While our data are promising, several considerations must be addressed before clinical application. First, the pharmacokinetics and pharmacodynamics (PK/PD) of halicin in humans remain poorly understood. Preclinical studies have shown favorable results in mouse infection models, but extrapolation to human physiology requires careful dose optimization and safety evaluation [29,30]. Secondly, halicin’s stability in biological fluids, its interaction with host microbiota, and its potential cytotoxicity must be thoroughly evaluated [31].
It was identified by the MIT deep learning AI over six years ago; the article above published last year. It doesn’t seem that the process of getting from identification to clinical use is being sped up?
There’s a semantic argument to be had, but I would say that this is false overall. Transformers and the way they implement neural networks are pretty broadly shared between different types of deep learning models. ChatGPT (and large language models in general) and Geometric deep learning / graph neural networks are basically two implementations of the same idea. They aren’t separate inventions, they’re just two different branches on the same tree. If you invented one, it’s a relatively small leap to invent the other.
Degree of relatedness does come down to semantics, but at the very least, you can’t just ask ChatGPT “Which of this set of molecules will form a shape similar to this other molecule”, and expect any sort of reasonable answer.
Well, I disagree. I’m guessing your point is something like “having neural networks that help discover new medicines don’t require that we also have chat bots and image generators” but they’re the same genie from the same bottle, just different wishes, and if it’s not that, I’m not sure what your comment was trying to address or add.
It would be hard to not invent planes after the advent of the internal combustion engine and a little bit like saying the planes that fly are not particularly related to the things that drive on the ground. I mean, sure, there’s a distinction there, but they basically both were created as a result of the ICE and both practically inevitable after the invention of the ICE. I would actually say that the geometric deep learning and large language models are more closely related than cars and piston airplanes too.
So if you could magically go back and make LLMs not exist, you take the medicine-designing AI with it.