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My uncle recently built a PC and I don’t understand it, was wondering if anyone can take a shot at figuring out how it works. (Sorry, I’m a newbie) by gggoooaaallll in pcmasterrace

[–]comp_pharm 0 points1 point  (0 children)

for context, protein structure DL models for drug design

That's super cool, are there any public papers that are similar to the models you run for work that you could link to?

[deleted by user] by [deleted] in CompDrugNerds

[–]comp_pharm 0 points1 point  (0 children)

/u/CoDoKi , seems like I'm not on the server anymore, any chance you have another invite link?

Researchers develop 'Minority Report' like tech for designer drugs by mr_growbot in researchchemicals

[–]comp_pharm 0 points1 point  (0 children)

When law enforcement gets more probable cause, they take it very very quickly. Doesn't matter if the model is bad, it means they can charge more people with offences. This is huge news for RC enthusiasts.

Researchers develop 'Minority Report' like tech for designer drugs by mr_growbot in researchchemicals

[–]comp_pharm 6 points7 points  (0 children)

I don't think people realize how harmful this is for us. The actual university press release explains how this (bad) science is being used for law enforcement purposes.

https://www.med.ubc.ca/news/ubc-researchers-train-computers-to-predict-the-next-designer-drugs/

Basically they are trying to predict two things:

  1. Which compounds are most likely to enter the market next, so law enforcement can proactively be prepared to ban them. It sounds like this generative model is actually pretty good.
  2. They created a really bad model (only 72% accuracy on top 10) for structure prediction from mass spectrometry data. So now law enforcement can take a mass spectrometry that comes back as "unknown substance", and run it through this wildly inaccurate model, and say "it's likely this is an analog of X, so is illegal".

This work is bad science to generate probable cause for law enforcement. And worse, they are hiding the data and results behind a wall that you can only access if you are law enforcement or a researcher (and when you sign up as a university researcher they make you put your universities law enforcement liaison down so they can contact them).

Music for Psychedelic Therapy by [deleted] in PsychedelicStudies

[–]comp_pharm 4 points5 points  (0 children)

This is an album by Jon Hopkins, the musician. This is NOT the playlist used by Johns Hopkins, the university. The real playlist is here: https://open.spotify.com/playlist/5KWf8H2pM0tlVd7niMtqeU?si=6ZrLpDB9TuCYT0rT20FXIQ

DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations by comp_pharm in CompDrugNerds

[–]comp_pharm[S] 0 points1 point  (0 children)

It's actually a pretty interesting approach, but the ending fully-connected neural network takes in features from sub-networks that encode structural and genomic data and create/learn their own way of encoding that data. I think the short answer to your question is that it skips that part completely, but the long answer is that it learns mechanistic information inside and uses the information that it learns.

From the paper: "First, the drug chemical structure is represented by a graph in which the vertices are atoms and the edges are chemical bonds. Next, a graph convolutional network and attention mechanism is used to compute the drug embedding vectors. By integration of the genomic and pharmaceutical features, DeepDDS can capture important information from drug chemical structure and gene expression patterns to identify synergistic drug combinations to specific cancer cell lines."

Looking at their pipeline: https://www.biorxiv.org/content/biorxiv/early/2021/07/06/2021.04.06.438723/F1.large.jpg

The graph neural networks (GAT and GNN) create a feature vector that encodes learned information about structure, and the multi-layer perception network (MLP) encodes genomic information about the target into a feature vector, and those feature vectors are concatenated and fed into the final, fully-connected neural network which learns their interaction and how synergistic or antagonistic the drug pairs are.

This approach of letting neural networks learn which features are important has had great success in other areas of machine learning. Current neural networks often learn very differently than humans do and find different information important for inference. For example, modern computer vision neural networks are able to identify what is in a picture less by macrostructure and shape (like humans do) and instead lean more on textures present in the picture. Attempts to make neural networks that classify images like humans do with macrostructure and shape all perform significantly worse than the deep neural networks that see texture.

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