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Shiyayori t1_isqigan wrote

Not sure how exaggerated the article is, but I certainly didn’t expect this so soon… incredible, it’s only 2022

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PandaCommando69 t1_isqrmth wrote

This really does sound amazingly promising.

>Described in a newly published paper in Nature Machine Intelligence, the new model, called CODE-AE, can screen novel drug compounds to accurately predict efficacy in humans. In tests, it was also able to theoretically identify personalized drugs for over 9,000 patients that could better treat their conditions. Researchers expect the technique to significantly accelerate drug discovery and precision medicine.

I've been hearing about personalized medicine for so long, but maybe we're finally getting there?

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hateboresme t1_iss058o wrote

The AI spring is here. Get ready for an interesting ride. It's gonna get all exponential up in here.

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rationalkat t1_issedrd wrote

We are getting there!

"Our process primarily tests single drugs to identify which drugs show potential, which is where our artificial intelligence/machine learning (AI/ML) comes into play. The AI/ML platform uses the drug sensitivity data—which drugs worked, and which did not—and the patient’s genomic profile to identify multivariate mechanisms driving drug sensitivity. By understanding those critical vulnerabilities, identifying drug combinations becomes a matter of matching FDA-approved, clinically available drugs to the tumor vulnerabilities. We can then use the AI/ML to inform combination design, which, alongside physician input on combinations of interest, guides follow-up combination drug testing. The AI/ML enables us to close the feedback loop in drug combination testing for any cancer patient."

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onyxengine t1_ist5dw0 wrote

Think about the complexity of gpt3 and the text to image generators, with a database of known compounds and effects we could predict we would have this about a year ago if not sooner. We were printing unknown compounds before that.

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Murky-Garden-9967 t1_isu13r8 wrote

We’ve had swisstargetprediction for a while, which tells you receptor binding, and as a pharmacologist if you gave me the receptor binding of a drug I could give you a rough idea of what it’ll do and if it’s toxic. This AI is just listing the effects of agonist/antagonism on a particular receptor, and outputting that information in relation to the binding profile of a chem. X molecule bonds to 5HT2A, Mu Opioid receptor and D1, you’ll get stimulation, nausea, hallucinations, euphoria, potentially drowsiness. Agonist on 5HT3 we get stimulation, nausea, etc etc. Honestly a layperson could probably do a somewhat decent job by looking up how a bunch of drugs work and what receptors they bind to as well as their chemical structure.

To prove it, link pictures of molecules and I’ll do my best to tell you roughly what they do. This is insanely useful but not that cutting edge - the thing that determines the cutting edgeness is the accuracy - STP leaves a lot to be desired.

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OutOfBananaException t1_iswdfrf wrote

They said they're using the patients genomic profile. As to how beneficial that is, I'm not sure (also unclear just how much data exists for drug effects paired with genomic data).

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