4 ms·

“Drug repurposing for AML” lol As a person who is literally doing his PhD on AML by implementing molecular subtyping, and ex-vivo drug predictions. I find this

“Drug repurposing for AML” lol As a person who is literally doing his PhD on AML by implementing molecular subtyping, and ex-vivo drug predictions. I find this super random. I would truly suggest our pipeline instead of random drug repurposing :) https://celvox.co/solutions/seAMLess https://celvox.co/solutions/seAMLess edit: Btw we’re looking for ways to fund/commercialize our pipeline. You could contact us through the site if you’re interested!

heyoni 2y ago
Can you explain what you mean by subtyping and if/how it negates the usefulness of repurposing (if that’s what you meant to say). Wouldn’t subtyping complement a drug repurposing screen by allowing the scientist to test compounds against a subset of a disease? And drug repurposing is also used for conditions with no known molecular basis like autism. You’re not suggesting its usefulness is limited in those cases right?
- celltalk 2y ago
  Sure. There are studies like BEAT-AML which tests selected drugs’ responses on primary AML material. So, not on a cell-line but on true patient data. Combining this information with molecular measurements, you can actually say something about which drugs would be useful for a subset of the patients. However, this is still not how you treat a patient. There are standard practices in the clinic. Usually the first line treatment is induction chemo with hypomethylating agents (except elderly who might not be eligible for such a treatment). Otherwise the options are still very limited, the “best” drug in the field so far is a drug called Venetoclax, but more things are coming up such as immuno-therapy etc. It’s a very complex domain, so drug repurposing on an AML cell line is not a wow moment for me.
ttpphd 2y ago
It's almost like scientists are doing something more than a random search over language.
- celltalk 2y ago
  I do hallucinate a better future as well.
  - coherentpony 2y ago
    It bothers me that the word 'hallucinate' is used to describe when the output of a machine learning model is wrong. In other fields, when models are wrong, the discussion is around 'errors'. How large the errors are, their structural nature, possible bounds, and so forth. But when it's AI it's a 'hallucination'. Almost as if the thing is feeling a bit poorly and just needs to rest and take some fever-reducer before being correct again. It bothers me. Probably more than it should, but it does.
    - pertymcpert 2y ago
      I think hallucinate is a good term because when an AI completely makes up facts or APIs etc it doesn't do so as a minor mistake of an otherwise correct reasoning step.
      - throwawaymaths 2y ago
        its more like conspiracy theory. when you're picking a token youre kinda like putting a gun to the LLM's head and demanding, "what you got next?"
- nazgul17 2y ago
  This search is random in the same way that AlphaGo's move selection was random. In the Monte Carlo Tree Search part, the outcome distribution on leaves is informed by a neural network trained on data instead of a so-called playout. Sure, part of the algorithm does invoke a random() function, but by no means the result is akin to the flip of a coin. There is indeed randomness in the process, but making it sound like a random walk is doing a disservice to nuance. I feel many people are too ready to dismiss the results of LLMs as "random", and I'm afraid there is some element of seeing what one wants to see (i.e. believing LLMs are toys, because if they are not, we will lose our jobs).
ncfausti 2y ago
Thank you for your work on this, truly.
- celltalk 2y ago
  Thanks :)

heyoni 2y ago

Can you explain what you mean by subtyping and if/how it negates the usefulness of repurposing (if that’s what you meant to say). Wouldn’t subtyping complement a drug repurposing screen by allowing the scientist to test compounds against a subset of a disease? And drug repurposing is also used for conditions with no known molecular basis like autism. You’re not suggesting its usefulness is limited in those cases right?

celltalk 2y ago
Sure. There are studies like BEAT-AML which tests selected drugs’ responses on primary AML material. So, not on a cell-line but on true patient data. Combining this information with molecular measurements, you can actually say something about which drugs would be useful for a subset of the patients. However, this is still not how you treat a patient. There are standard practices in the clinic. Usually the first line treatment is induction chemo with hypomethylating agents (except elderly who might not be eligible for such a treatment). Otherwise the options are still very limited, the “best” drug in the field so far is a drug called Venetoclax, but more things are coming up such as immuno-therapy etc. It’s a very complex domain, so drug repurposing on an AML cell line is not a wow moment for me.

ttpphd 2y ago

It's almost like scientists are doing something more than a random search over language.

celltalk 2y ago
I do hallucinate a better future as well.
- coherentpony 2y ago
  It bothers me that the word 'hallucinate' is used to describe when the output of a machine learning model is wrong. In other fields, when models are wrong, the discussion is around 'errors'. How large the errors are, their structural nature, possible bounds, and so forth. But when it's AI it's a 'hallucination'. Almost as if the thing is feeling a bit poorly and just needs to rest and take some fever-reducer before being correct again. It bothers me. Probably more than it should, but it does.
  - pertymcpert 2y ago
    I think hallucinate is a good term because when an AI completely makes up facts or APIs etc it doesn't do so as a minor mistake of an otherwise correct reasoning step.
    - throwawaymaths 2y ago
      its more like conspiracy theory. when you're picking a token youre kinda like putting a gun to the LLM's head and demanding, "what you got next?"
nazgul17 2y ago
This search is random in the same way that AlphaGo's move selection was random. In the Monte Carlo Tree Search part, the outcome distribution on leaves is informed by a neural network trained on data instead of a so-called playout. Sure, part of the algorithm does invoke a random() function, but by no means the result is akin to the flip of a coin. There is indeed randomness in the process, but making it sound like a random walk is doing a disservice to nuance. I feel many people are too ready to dismiss the results of LLMs as "random", and I'm afraid there is some element of seeing what one wants to see (i.e. believing LLMs are toys, because if they are not, we will lose our jobs).

ncfausti 2y ago

Thank you for your work on this, truly.

celltalk 2y ago
Thanks :)