View comment

AI for drug manufacturers

by /u/gemini · 0 votes · 2024-01-02 09:32:00

Currently I'm training gpt2 on various manufacturing methods for amphetamine and mdma which is a previous version of gpt3 or chatgpt.

By compiling recipes and knowledge into txt format gpt2 can store this knowledge inside it's modal which can be used offline without notifying authorities of what you are doing.

This can all be done using Google collab which provides a free python environment with required hardware such as ram and video ram using a GPU.

The knowledge base stored in the txt file should contain information on chemicals such as the precursors, methods of manufacturing, effects and any notes needed to manufacture the product. This is more complicated than it sounds however I do teach this privately not for free and the txt file can be then used to train either gpt2 or llama by a method called fine-tuning.

Currently i only used a small amount of recipes for proof of concept but I can generate various models for use in chatbots where you can ask it questions such as "how do I make amphetamines from ephedrine pills" and this will provide a recipe.

Once your dataset or modal reaches a certain size, gpt2 can then start creating more complex answers from the information in the modal and potentially create new drugs and manufacturing techniques based on the data you provide it. This technique is being used currently to create new medicine and antibiotics using both gpt2 an llama transformer architecture.

If you want more information it's all available on Google and YouTube while at same time it's easy to understand it can be a pain in the butt at times.

User: /u/ZillaKami138

Yes, in theory. Alphafold is a generative AI that predicts protein folding and thus 3-d structure. I don't see why you couldn't use that or a similar program trained to predict biochemical properties of large globular proteins, smaller peptide fragments, or small molecules to identify species--known, unknown, or both--that bind to mew opioid receptors as full agonists but which have little to no kappa opioid receptor affinity, for example. Or predict new stimulants that can cross the blood-brain barrier and inhibit DAT and VMAT-2 with affinities similar to methamphetamine, but which have very little SERT activity. Smarter people than me can come up with more interesting and complex sets of interesting combinations of pharmacological properties--my knowledge med chem/pharmacology is rudimentary at best. But I fully expect such programs will become common place in big pharma soon as the primary tools for new drug discovery.