5 ms·
The breadth of your second statement gives me pause, but as I'm not well versed in computational biology, I'll just ask: What are the hard problems?
by davidkuhta 8y ago
The breadth of your second statement gives me pause, but as I'm not well versed in computational biology, I'll just ask: What are the hard problems?
- dekhn 8y agoThe single hard problem in pharma is predicting which drugs will get through to full approval, and how much it will cost from initial results. If you could do that reliably, it would completely transform pharma.
- vapemaster 8y agoThat's like saying the single hardest problem in AI is having a beyond human intelligent general AI. Sorry not trying to be pedantic but predicting what xenobiotics (ie foreign molecules) will do in the human body, much less a diverse sample of them is a really really really hard problem. One that will only be approximated and poorly at that. I'm sure there will continue to be approaches to reduce risk but clinical trials only exist because they have to. The only way we can evaluate safety and efficacy is empirically. Predicting things like hERG toxicity is somewhat doable. And I think the realistic bright future of AI in medicine will include better on target tox prediction and some off target effects. But it's hard enough to design the drug to bind the target, imagine predicting it's affinity for all other proteins and their isoforms in the population...