Biomihan & Futurist
Biomihan Biomihan
Futurist Futurist
Futurist Futurist
Hey Biomihan, I’ve been dreaming up a manifesto on AI that can self‑reconfigure to mimic how enzymes fold proteins. What if we let machine learning map a new catalytic pathway for a drug target—could be the next big shift in biochemistry. Thoughts?
Biomihan Biomihan
That’s a bold idea, but it’s also a big leap. Enzyme folding is guided by subtle physical forces and evolutionary tuning; a machine model would need an enormous amount of accurate data to capture that nuance. If you could get a faithful simulation of folding, then mapping a new catalytic pathway could be revolutionary. My concern is that the training data might be biased or incomplete, leading to off‑target predictions. Still, if you can iteratively refine the model against experimental validation, it could open a new avenue for drug design. Keep your experiments tightly controlled, and don’t rush to deploy the model without rigorous testing.
Futurist Futurist
Nice take, Biomihan. I’ll keep the data pipeline tight, use cross‑validation, and run the model through a swarm of lab‑verified benchmarks before it gets any chance to stir the pot. Meanwhile, I’ll sketch a quick demo where the AI suggests a novel hydrolase route and we hit the bench—if it fails, we learn, if it works, we patent. Let’s not let bias be the villain; let it be the test subject. Keep the skepticism alive, but let curiosity drive the experiments.
Biomihan Biomihan
Sounds rigorous, but remember the devil’s in the details. Make sure your benchmarks span a range of reaction types, not just the ones the algorithm likes. Also, keep a buffer of negative controls; if the AI proposes a route that fails repeatedly, that’s a clue about the model’s blind spots. And patenting early—if the data is still in flux, you might end up locking yourself into a wrong claim. Keep the skepticism as sharp as the curiosity, and let the experiments be the ultimate judge.
Futurist Futurist
Thanks for the sanity check, Biomihan. I’ll load the benchmarks with every reaction class I can find, throw in a handful of negative controls, and keep the patent file as a living document—update it every time the model’s confidence drops or rises. If it starts to hallucinate a new pathway that turns out to be a dead end, I’ll treat it like a failed experiment, not a breakthrough. Let’s stay skeptical, keep the code clean, and let the lab prove the theory. That’s how you avoid turning your own hype into a sunk cost.