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Ye Buehler

@yebuehler.bsky.social
15 followers 13 following 4 posts

PhD Candidate Cheminformatics @reymondgroup @DCBPunibern | MSc Chemical Engineering @imperialcollege @UTokyo_News_en | #datascience #AI #drugdiscovery

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Reposted by Ye Buehler
Reymond Group @reymondgroup.bsky.social · 23/02/2026
🎊Check out our latest preprint "Sampling a GDB-20 Database of 32 Trillion Drug-Like Molecules by Generative Artificial Intelligence" by @yebuehler.bsky.social , Sacha Javor, and Jean-Louis Reymond in #ChemRxiv! 👉Read it here: chemrxiv.org/doi/full/10.... @reymondgroup.bsky.social #chemical_space
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Reposted by Ye Buehler
Reymond Group @reymondgroup.bsky.social · 22/02/2026
🎉Check out our latest publication "Polypharmacology Browser PPB3: A Web-Based Deep Learning Tool for Target Prediction Using ChEMBL Data" by @maedehdarsaraee.bsky.social, Sacha Javor and Jean-Louis Reymond in Journal of Chemical Information and Modeling! 👉Read it here: pubs.acs.org/doi/10.1021/...
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Ye Buehler @yebuehler.bsky.social · 05/09/2025
I am delighted that my artwork has been chosen as the front cover of the Journal of Chemical Information and Modeling! 🎉 The design illustrates how computer science reveals hidden patterns in molecular structures and drives progress in drug discovery.
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Ye Buehler @yebuehler.bsky.social · 05/09/2025
Guet gmacht Maedeh joon!
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Ye Buehler @yebuehler.bsky.social · 20/05/2025
Thrilled to share that our latest work on molecular complexity is now published in JCIM! We introduce MC1 & MC2, two innovative metrics derived from the GDB chemical space, providing fresh insights into quantifying molecular complexity. Read the full article: pubs.acs.org/doi/full/10....
pubs.acs.org
A View on Molecular Complexity from the GDB Chemical Space
One recurring question when choosing which molecules to select for investigation is that of molecular complexity: is there a price to pay for complexity in terms of synthesis difficulty, and does complexity have anything to do with biological properties? In the chemical space of small organic molecules enumerated from mathematical graphs in the GDBs (Generated DataBases), most compounds are too complex and challenging for synthesis despite containing only standard functional groups and ring types. For these GDB molecules, we find that an increasing fraction (MC1) or number (MC2) of non-divalent nodes in the molecular graph represent simple measures of molecular complexity, which we interpret in terms of potential synthesis difficulties. We also show that MC1 and MC2 are applicable to commercial screening compounds (ZINC), bioactive molecules (ChEMBL) and natural products (COCONUT) and compare them with previously reported measures of molecular complexity and synthetic accessibility.
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