Nicholas Runcie
@nicholasruncie.bsky.social
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AI for drug design | Oxford DPhil student | MChem | RSci | opinions my own
🚀 LLMs can now do chemistry! Our new preprint shows that state-of-the-art reasoning models can now perform advanced chemical reasoning tasks, without any assistance from external tools. Here’s what o3-mini can already do👇 (1/🧵)
7 months ago
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Achira
10 months ago
We're excited to announce we've raised a Seed round with Dimension, Amplify, NVIDIA NVentures, and Compound! Read more about our mission at
achira.ai
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Odysseas Vavourakis
11 months ago
It’s an exciting time in protein design! 🧬✨ But much of the therapeutic potential—especially for antibodies—remains untapped. Why? 🤔 Antibodies seem like ideal candidates for design! 💉 Here’s a quick thread summarising our new review paper on the state of antibody structure prediction. 👇 1/
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Challenges and compromises: Predicting unbound antibody structures with deep learning
Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and imp…
https://www.sciencedirect.com/science/article/pii/S0959440X25000016?via%3Dihub
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John Chodera
11 months ago
Incredibly excited to see how well ADMET, affinity, and pose prediction models *actually* perform in the ASAP x OpenADMET x PolarisHub blind challenge:
polarishub.io/competitions
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Polaris
The benchmarking platform for drug discovery
https://polarishub.io/competitions
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Oxford Protein Informatics Group (OPIG)
11 months ago
New blog post from 1st year DPhil student
@nicholasruncie.bsky.social
on visualising cheminformatics data as molecular networks
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🔥 Molecule networks 🔥 New cheminformatics data visualisation: 📊 Rapid interpretation of SAR 🔊 Find molecules in the noise ❌ No more lists of molecules My new favourite visualisation method! 🚀 Read how I visualise Free Wilson analysis here
www.blopig.com/blog/2025/01...
11 months ago
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Matteo Ferla
11 months ago
Fragmenstein is finally published! ⌬🔩📗 This tool stitches together the atoms of parent hits and re-animates (minimises) them into a conformation close to the parents, preserving their interactions: a must for crystallography-driven drug discovery
doi.org/10.1186/s133...
#chemsky
#drugdiscovery
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Fragmenstein: predicting protein–ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding–based methodology - Journal of Cheminformatics
Current strategies centred on either merging or linking initial hits from fragment-based drug design (FBDD) crystallographic screens generally do not fully leaverage 3D structural information. We show...
https://doi.org/10.1186/s13321-025-00946-0
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