Ai labs look to be desperate to try and stave off the cooling hype that looks to be snowballing in the industry right now. These next few months are going to be crucial to their potential survival and future.
Its not surprising to me that this press release looks half-baked.
That must be hard to say with a straight face. Over the last 4-6 months I think very few corporate developers have handwritten any code at all and the “intelligence” growth from one model to the next is very tangible.
Odd. I barely perceive the growth, and I use them a ton at work. Not saying the advancements aren’t there, I just wouldn’t describe every model release as having tangible growth.
Also the handwritten code doesn’t remove the work. If anything, it makes the thinking more difficult (for me) because instead of thinking during the coding, I have to before and after (in iterations).
For big projects I actually go back to tradcoding some to make sure I understand the system, since I’m the one responsible.
The problem was never generating leads. The bottleneck is validated results. It’s performing clinical trials proving efficacy. And this is where I think AI will have the biggest impact—-helping scientist, navigate study complexity and regulatory burden
Drug design is hard because it’s a system problem not a molecule-protein interaction problem.
At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I’m not sure how wide-spread that is any more; it used a lot of reagents. And it ran into the real bottleneck: lead generation tells you little to nothing about the efficacy, toxicity, and side effects of a drug or biologic in actual humans.
In silicon will never do a better job at lead generation than combinatorial chemistry. It could help with the real bottleneck. But… there just isn’t a lot of high quality training data on this.
They’ve been pushing ML approaches to drug design since at least the early 2010s. The approach has evolved with the trends in ML. Overall, I think the results have been very mixed; much like the traditional pharma pipeline.
> At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I don't think this was ever the gold standard. I mean, some companies did employ these approaches, but they were generally viewed as low-quality, brute-force approaches (at least at the handful of companies I was associated with).
"The problem was never generating leads" is an absurd statement without any reference to the quality of those leads. Ideally you want a system able to generate one more lead, the correct one.
That is not true. For many diseases we lack good hypotheses about the underlying causal mechanisms which resulted in decades and billions being pumped into few questionable targets (e.g. amyloid and alzheimers). In other diseases such as many cancers we often lack "actionable" targets, resulting in hundreds me-too companies all chasing the same target with only subtle variations in approach (inefficient capital alocation).
So AI could help a lot in discovery, much more than the engineering like uses in antibody/drug optimization.
Clinical science is inundated with well-reasoned "potential leads". Ideas for targets, and even designing drugs to interact with those targets in the desired way, is not the bottleneck. Hasn't been for decades. The bottleneck is in clinical development i.e. human trials.
There are many many AI drug discovery companies out there, many of them very well funded, and ALL of them with no shortage of "potential leads".
These companies usually start out with the idea that their potential leads are better than the alternatives (generally because they cast a much bigger net/ considered more screening criteria than a human could, thanks to AI) and that big pharma will pay them license fees.
And yet, all of those companies, pretty much without exception, have turned into drug development companies themselves. They are forced to develop their own "potential leads" into actual approved drugs. And they are subject to the same timelines, same costs and - so far - the same probabilities of success as any other drug developer.
PerturbAI & OpenAI's "potential leads" might be better or worse than the next AI company. I would guess worse, given there is zero proprietary data involved and no clarity about why the underlying models are going to generate insightful findings. Either way, not useful.
I hope OpenAI will prove its usefulness in stealing joy from mathematicians, engineers, and biologists this time. It feels like there's a chance of warmer feedback this time from everyone out there keeping an eye on what they do.
Personally I would forgive all of the joy being stolen by AI if they discover something fundamental and outstanding that would help all of us.
> stealing joy from mathematicians, engineers, and biologists
This is the weakest possible rationalization against the AI takeover. Solving real world problems isn’t “stealing joy”. Those problems shouldn’t be gatekept. It paints our scientists as rent seekers.
> Solving real world problems isn’t “stealing joy”
Well, pretty good amount of the training data was stolen. Not only from the repositories without a license, but also archives of books, and they even steal training from each other. Funny enough, they still blame each other for stealing. I don't mind if you think your joy is not stolen. But I mind if you insist when your opinion is the only right one and joy has definitely not been stolen. It's quite sensitive territory starts here, and I don't think anyone (incl.yourself) has a right to speak for everyone in that regard.
Why waste so much space on a chart on whether "human experts preferred distributed-agent reports over single-agent reports"? I thought we were supposed to be curing alzheimer's and cancer!
I use LLMs every day but damn... can we please market them and value them as a coding tool and not the savior/destroyer of the world. "new leads" comes off flat after the previous tone
I am sorry, but if you still have stupid humans these leads will end up going the same place all the leads go. Nowhere.
So what if DCTN1 is linked to Alzheimer's? All they will do is invent a drug to mess with the enzyme and it might only still help a small percentage of people and no one will be able to afford it.
It is well know that diet plays a large role in Alzheimer's[1] and is largely an issue of personal genetics and environment.
OpenAI in sciences: "We have bitten off a bit of each of these apples - you guys finish eating them, because that is what you are best at - finishing half-eaten food. May we have a $2T valuation, please?"
Ai labs look to be desperate to try and stave off the cooling hype that looks to be snowballing in the industry right now. These next few months are going to be crucial to their potential survival and future.
Its not surprising to me that this press release looks half-baked.
How is the hype cooling? Opus 5.5 is doing most of my job
Hype and functionality are only loosely correlated.
That must be hard to say with a straight face. Over the last 4-6 months I think very few corporate developers have handwritten any code at all and the “intelligence” growth from one model to the next is very tangible.
Odd. I barely perceive the growth, and I use them a ton at work. Not saying the advancements aren’t there, I just wouldn’t describe every model release as having tangible growth.
Also the handwritten code doesn’t remove the work. If anything, it makes the thinking more difficult (for me) because instead of thinking during the coding, I have to before and after (in iterations).
For big projects I actually go back to tradcoding some to make sure I understand the system, since I’m the one responsible.
The problem was never generating leads. The bottleneck is validated results. It’s performing clinical trials proving efficacy. And this is where I think AI will have the biggest impact—-helping scientist, navigate study complexity and regulatory burden
If you can increase the fidelity of your research model (in-silico, murine, etc) to real life humans then there are fewer false leads.
Drug design is hard because it’s a system problem not a molecule-protein interaction problem.
At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I’m not sure how wide-spread that is any more; it used a lot of reagents. And it ran into the real bottleneck: lead generation tells you little to nothing about the efficacy, toxicity, and side effects of a drug or biologic in actual humans.
In silicon will never do a better job at lead generation than combinatorial chemistry. It could help with the real bottleneck. But… there just isn’t a lot of high quality training data on this.
They’ve been pushing ML approaches to drug design since at least the early 2010s. The approach has evolved with the trends in ML. Overall, I think the results have been very mixed; much like the traditional pharma pipeline.
> At one point the gold standard for generating leads was using combinatorial chemistry to experimentally screen through millions of compounds.
I don't think this was ever the gold standard. I mean, some companies did employ these approaches, but they were generally viewed as low-quality, brute-force approaches (at least at the handful of companies I was associated with).
The real problem is generating leads that are more likely to yield positive results than throwing darts at hypotheses.
That's what top-tier scientists are good at.
"The problem was never generating leads" is an absurd statement without any reference to the quality of those leads. Ideally you want a system able to generate one more lead, the correct one.
That is not true. For many diseases we lack good hypotheses about the underlying causal mechanisms which resulted in decades and billions being pumped into few questionable targets (e.g. amyloid and alzheimers). In other diseases such as many cancers we often lack "actionable" targets, resulting in hundreds me-too companies all chasing the same target with only subtle variations in approach (inefficient capital alocation).
So AI could help a lot in discovery, much more than the engineering like uses in antibody/drug optimization.
This is very, very weak.
Clinical science is inundated with well-reasoned "potential leads". Ideas for targets, and even designing drugs to interact with those targets in the desired way, is not the bottleneck. Hasn't been for decades. The bottleneck is in clinical development i.e. human trials.
There are many many AI drug discovery companies out there, many of them very well funded, and ALL of them with no shortage of "potential leads".
These companies usually start out with the idea that their potential leads are better than the alternatives (generally because they cast a much bigger net/ considered more screening criteria than a human could, thanks to AI) and that big pharma will pay them license fees.
And yet, all of those companies, pretty much without exception, have turned into drug development companies themselves. They are forced to develop their own "potential leads" into actual approved drugs. And they are subject to the same timelines, same costs and - so far - the same probabilities of success as any other drug developer.
PerturbAI & OpenAI's "potential leads" might be better or worse than the next AI company. I would guess worse, given there is zero proprietary data involved and no clarity about why the underlying models are going to generate insightful findings. Either way, not useful.
I hope OpenAI will prove its usefulness in stealing joy from mathematicians, engineers, and biologists this time. It feels like there's a chance of warmer feedback this time from everyone out there keeping an eye on what they do.
Personally I would forgive all of the joy being stolen by AI if they discover something fundamental and outstanding that would help all of us.
> stealing joy from mathematicians, engineers, and biologists
This is the weakest possible rationalization against the AI takeover. Solving real world problems isn’t “stealing joy”. Those problems shouldn’t be gatekept. It paints our scientists as rent seekers.
> Solving real world problems isn’t “stealing joy”
Well, pretty good amount of the training data was stolen. Not only from the repositories without a license, but also archives of books, and they even steal training from each other. Funny enough, they still blame each other for stealing. I don't mind if you think your joy is not stolen. But I mind if you insist when your opinion is the only right one and joy has definitely not been stolen. It's quite sensitive territory starts here, and I don't think anyone (incl.yourself) has a right to speak for everyone in that regard.
The valuation bubble pops if the market comes to believe frontier AI will not disrupt other industries at the rate that it did software engineering.
More PR bs from unscrupulous people. Until you've cured an actual disease for actual people, please shut up.
Why waste so much space on a chart on whether "human experts preferred distributed-agent reports over single-agent reports"? I thought we were supposed to be curing alzheimer's and cancer!
"new leads"
I use LLMs every day but damn... can we please market them and value them as a coding tool and not the savior/destroyer of the world. "new leads" comes off flat after the previous tone
I am sorry, but if you still have stupid humans these leads will end up going the same place all the leads go. Nowhere.
So what if DCTN1 is linked to Alzheimer's? All they will do is invent a drug to mess with the enzyme and it might only still help a small percentage of people and no one will be able to afford it.
It is well know that diet plays a large role in Alzheimer's[1] and is largely an issue of personal genetics and environment.
[1] https://news.harvard.edu/gazette/story/2024/07/alzheimers-st...
OpenAI in sciences: "We have bitten off a bit of each of these apples - you guys finish eating them, because that is what you are best at - finishing half-eaten food. May we have a $2T valuation, please?"