RT @AnanikovLab: 🧲Magnetic stirrers - used daily in millions of labs - may silently sabotage reproducibility. Our @JACS_Au article describe….

RT @_xjdr: you should legally be required to disclose what quantization level you are serving your current model at like it was a nutrition….

RT @ML_Chem: Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data #machinelearnin….

RT @bravo_abad: Discovering new reactions from terabyte-scale mass spectrometry data with a machine learning approach. Large experimental d….

RT @zeldapoem: How many reasons do you need to move to San Francisco

I guess not citing prior work is all you need….

Most useful website in the internet

(7/n) And finally, thanks 🙌 to all the coauthors and everyone who has supported this work.

(6/n) But this is only beginning and there is still a lot of work to be done. You can play with this platform at and reach out if you have any questions or excited about this research direction.

(5/n) Having all that in mind, we have built a system that takes a substrate, selects its neighbors in chemical space, looks up known reactivity, collects enzymes and their neighbors in enzyme space, and finally a separate model reranks this list to produce final output. 🤖

(4/n) . of course, it's not always the case, so some screening will be required. What we want here is that a researcher finds working biocatalyst sooner ⏲️, than later.

(3/n) So, what's next? We need to somehow recommend 🧠enzymes that might work for a particular substrate. Here we exploit two very simple ideas — similar substrates should have similar reactivity and similar enzymes should have similar reactivity as well. And . .

(2/n) For building such a system, one would need a big enough dataset 💾 first. The preprint introduces an enzyme library 📚, aKGLib1, and a dataset, BioCatSet1, that contains information about reactivity of enzymes from the library against a set of more than 100 substrates.

At @gpggrp we published a preprint in collaboration with @NarayanLab and particularly awesome @Apatoneh. #ML #AI #chemistry #science. The paper introduces a ML-driven platform for guiding biocatalyst selection in small molecule synthesis. But how does it work? 🧵 (1/n).

RT @Apatoneh: Excited to share an awesome collab between the @NarayanLab ang @gpggrp! Check out our preprint on CATNIP, using ML to help ch….

Wrote an article on how you can host LLM tools using @modal_labs .

RT @martyamark: Cursor is:.- Great for bad coders.- Bad for great coders.

(5/n) The paper is available at