Huge shoutout to the amazing team behind this:
👏 Lead author Yingze (Eric) Wang and @kunyangsun.bsky.social
👏 PI Prof. Teresa Head-Gordon
👏 Teammates Jie Li, Xingyi Guan, Oufan Zhang, Dorian Bagni
👏 Collaborators Dr. Heather A. Carlson and Prof. Yang Zhang
What’s next?
We’re exploring:
🔁 Rotamer refinement
🤖 Binding label extraction with LLMs (maybe 👀)
🧠 Better data splits (possibly inspired by PLINDER) to support ML research!
Since we're focused on structural data with binding labels, we applied this workflow to major open-access datasets (BioLiP, BindingDB, and BindingMOAD) to generate HiQBind: a cleaned, corrected dataset comparable in size to PDBBind v2020 but with significantly improved structural quality! 💥
Copying Oliver's post from Linkedin to help us gain some visibility here!
🚨 Our paper is out! 🚨
"A workflow to create a high-quality protein–ligand binding dataset for training, validation, and prediction tasks" is now published in Digital Discovery! 🎉
Check it out, and feel free to drop your questions here or on GitHub!
🔗 GitHub: github.com/THGLab/HiQBind
📄 Paper: pubs.rsc.org/en/content/a...
#machinelearning #proteinligand #proteinligandbindingaffinity #structuralbiology #ai4sci
In this work, we built HiQBind-workflow, a semi-automated workflow that processes protein–ligand structures from the RCSB PDB by adding missing atoms, correcting ligand geometries, fixing bond orders and protonation states, and much more!
We have left X for greener pastures and bluer skies - good riddance to Nazi-saluting Musk and his abuse of science and engineering that in fact enriched him.
