> Say they cure cancer.
A pet peeve of mine.
People keep mouthing this same fantasy. But cancer is so many different cancers. And "the cure(s)" aren't just 1000 different drugs, but maybe 10,000 different candidates for each that pass into lab tests, of which maybe 1% make it to phase 1 trials, and of those maybe 10% make it all the way through phase 3, each after 5-10 years of clinical trials and $100s of millions invested each. For the most part, AI-related biotechs are largely focussing on accelerating the discovery of good candidates. But that's a tiny fraction of the pipeline, which includes lots of lab work (which can be automated) and clinical trials (which OTOH require actual human guinea pigs and years of observation and assessment). How do you meaningfully accelerate that? Presumably by improving the odds of success in phase 3 trials from your initial candidates.
But nobody has proposed yet how LLMs can be a magic bullet here. At best they'll give you those 10,000 candidates per cancer type faster. But then you have all that lab work and clinical work to slog through, and all the likely failures you'll see in clinical trials. And there's just not enough data out there to train ML on to meaningfully improve the odds of success in clinical trials, since you'd really have to understand how your drugs work with all proteins and systems in every type of human body, e.g. "in silico" clinical trials. And we're only merely scratching the surface of that with stuff like OpenADMET.
That is, we can't even model well "how a human body works", much less "how those mechanisms go wrong in each type of cancer". But even if you could do all that, for the cures you'd need to figure out what novel, unusual, out-of-distribution thing (relative to your human model) you can throw into the system to avoid the bad behavior, that doesn't kill the person or interfere with other systems or get degraded too quickly or ... tons of other stuff.
Highly non-trivial. Maybe lower-hanging fruit is with a novel peptide or antibody, since those are better modeled with ML. But small drug-like molecules are galactic in number and not well modeled yet. ("Hey, what happens if I introduce this weird platinum-based drug into a body?" Well yeah, cisplatin is used for chemotherapy, but the body of knowledge needed to assess platinum-based drugs in a human body just doesn't exist, and ML isn't gonna intuit it from known data, nor "learn it" since that would require going down to QM, and then integrating that into its model of all human peptides and pathways.)
This is not a side-project for a chatbot company. Its a (multi-)generational project that requires buy-in from labs that can produce the requisite data.
(And okay so maybe I'm fast-and-loose above. Ideally a single "cure" would be to fix the apoptosis pathway, i.e. repair the built-in cell-death path for fast reproducing cells. But cancer cells mutate quickly and evade known avenues. Perhaps there's a generic fix? Plausible but same reasoning applies: ML doesn't have enough data to intuit a solution, if one exists. And there's no reason, a prior, to assume that one does exist. Only hope.)
A pet peeve of mine.
People keep mouthing this same fantasy. But cancer is so many different cancers. And "the cure(s)" aren't just 1000 different drugs, but maybe 10,000 different candidates for each that pass into lab tests, of which maybe 1% make it to phase 1 trials, and of those maybe 10% make it all the way through phase 3, each after 5-10 years of clinical trials and $100s of millions invested each. For the most part, AI-related biotechs are largely focussing on accelerating the discovery of good candidates. But that's a tiny fraction of the pipeline, which includes lots of lab work (which can be automated) and clinical trials (which OTOH require actual human guinea pigs and years of observation and assessment). How do you meaningfully accelerate that? Presumably by improving the odds of success in phase 3 trials from your initial candidates.
But nobody has proposed yet how LLMs can be a magic bullet here. At best they'll give you those 10,000 candidates per cancer type faster. But then you have all that lab work and clinical work to slog through, and all the likely failures you'll see in clinical trials. And there's just not enough data out there to train ML on to meaningfully improve the odds of success in clinical trials, since you'd really have to understand how your drugs work with all proteins and systems in every type of human body, e.g. "in silico" clinical trials. And we're only merely scratching the surface of that with stuff like OpenADMET.
That is, we can't even model well "how a human body works", much less "how those mechanisms go wrong in each type of cancer". But even if you could do all that, for the cures you'd need to figure out what novel, unusual, out-of-distribution thing (relative to your human model) you can throw into the system to avoid the bad behavior, that doesn't kill the person or interfere with other systems or get degraded too quickly or ... tons of other stuff.
Highly non-trivial. Maybe lower-hanging fruit is with a novel peptide or antibody, since those are better modeled with ML. But small drug-like molecules are galactic in number and not well modeled yet. ("Hey, what happens if I introduce this weird platinum-based drug into a body?" Well yeah, cisplatin is used for chemotherapy, but the body of knowledge needed to assess platinum-based drugs in a human body just doesn't exist, and ML isn't gonna intuit it from known data, nor "learn it" since that would require going down to QM, and then integrating that into its model of all human peptides and pathways.)
This is not a side-project for a chatbot company. Its a (multi-)generational project that requires buy-in from labs that can produce the requisite data.
(And okay so maybe I'm fast-and-loose above. Ideally a single "cure" would be to fix the apoptosis pathway, i.e. repair the built-in cell-death path for fast reproducing cells. But cancer cells mutate quickly and evade known avenues. Perhaps there's a generic fix? Plausible but same reasoning applies: ML doesn't have enough data to intuit a solution, if one exists. And there's no reason, a prior, to assume that one does exist. Only hope.)