
Daniil Boiko
@daniil_boiko
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ML-researcher, AI in chemistry
San Francisco, US
Joined May 2017
🧲Magnetic stirrers - used daily in millions of labs - may silently sabotage reproducibility. Our @JACS_Au article describes drastic variability in reaction outcomes based on vessel placement https://t.co/cFzO08CBQL
@ACSPublications @ACSCatalysis @ChemistryNews @NatureChemistry
pubs.acs.org
Magnetic stirrers, the most widely used and ubiquitous devices for performing chemical reactions in laboratory settings, may cause reproducibility problems. Reproducibility in a range of chemical...
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you should legally be required to disclose what quantization level you are serving your current model at like it was a nutrition label. you should also be banned from dynamically adjusting quantization based on demand without notification. (you know who you are ...)
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Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data #machinelearning #compchem
nature.com
Nature Communications - Mass spectrometry generates vast amounts of data in chemistry labs. Here, authors developed a machine learning-driven search engine that analyzes archived data to discover...
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Discovering new reactions from terabyte-scale mass spectrometry data with a machine learning approach Large experimental datasets have become a staple of modern chemistry research, yet the sheer volume of stored information often outstrips researchers’ ability to interpret and
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(7/n) And finally, thanks 🙌 to all the coauthors and everyone who has supported this work.
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(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 https://t.co/EkbxDsiFZR and reach out if you have any questions or excited about this research direction.
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(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. 🤖
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(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.
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(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 ...
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(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.
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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)
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Excited to share an awesome collab between the @NarayanLab ang @gpggrp! Check out our preprint on CATNIP, using ML to help chemists identify a compatible biocatalyst for small molecule synthesis! Huge thanks to @daniil_boiko for making it all possible!🎉 https://t.co/KPXRqe07S2
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Cursor is: - Great for bad coders - Bad for great coders
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