Compounds That Can Extend Lifespan
Can we use AI to predict these?
As a retired biochemist who’s spent decades in the lab, I’ve seen firsthand how tricky it can be to translate findings from simple organisms to humans. It’s a bit like trying to understand a symphony by listening to a single instrument — you get some idea, but you’re missing a lot of the complexity.
One of the biggest headaches we face is just how different humans are from model organisms. I remember working with C. elegans — fascinating little creatures, but a far cry from the intricacies of human biology. It’s not just about having more cells or organs; it’s the countless interactions between our systems that make us so complex.
Then there’s the data problem. In my day, we were always scrambling for good, reliable data. Now, with AI in the mix, that need has exploded. You need mountains of high-quality data to train these models, and let me tell you, that’s easier said than done when it comes to human biology.
Another issue that always bugged me was the whole correlation versus causation debate. AI is great at spotting patterns, but that doesn’t always mean it understands why those patterns exist.
In aging research, that’s crucial. We need to know the ‘why’ behind lifespan extension, not just the ‘what’.
I’ve also noticed that a lot of these AI models struggle to generalize. What works for a worm might not work for a mouse, let alone a human. I remember a compound that looked promising in C. elegans but fizzled out completely when we tried it in mice. It’s a humbling reminder of how different species can be.
And don’t even get me started on the validation process. In my later years, I was involved in some clinical trials, and boy, are they a slog. It’s not just about time and money — though those are big factors — it’s also navigating the ethical and regulatory maze. AI might speed up the initial discovery process, but it can’t fast-track these crucial later stages.
Lastly, I’ve always been a bit skeptical of ‘black box’ solutions. In my day, we liked to understand the mechanisms behind our findings. Some of these AI models are impressive, but if you can’t explain how they work, it’s hard to trust them fully. I think we need to find a way to combine AI with our traditional, mechanism-based approaches.
Don’t get me wrong — I’m excited about the potential of AI in aging research, but as someone who’s been in the trenches, I know we’ve got a long road ahead before we can reliably translate AI predictions from simple organisms to humans.
It’s going to take a lot of hard work, collaboration, and good old-fashioned lab time to get there.
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Healthwise: Exploring the Frontiers of Wellness and Science | Tom Kane | Substack
