We highlight key advances and remaining challenges in this emerging structure-aware paradigm, and discuss how integrating protein information can enable AI to design molecules with stronger binding potential and real-world drug-like properties. https://t.co/DOESNI3lq6

We cover the evolution from early shape-based strategies to modern co-folding models, detailing how different representations of protein pockets (voxel and graph) can encode structural information (shape, interactions, all-atom detail) and guide molecular generation.

Excited to share our new review on deep learning for structure-based drug design! 🎉 https://t.co/DOESNI2NAy Authors: @LucyVost*, Yael Ziv*, Charlotte Deane

Recently, Prof. Charlotte Deane participated in a discussion about AI as part of the Bradford Science Festival. Read some of her (and the other participants') thoughts here: https://t.co/Z157vrMOv7

Great work by DPhil student Kate Fieseler. Thanks also to co-authors Max Winokan, Joseph Morrone, Charlotte Deane, Frank von Delft, and Warren Thompson

Syndirella proposes congeneric series you can actually make (multi-step, digitized routes) and explores the pocket more broadly. Additionally, by buying reactants (not products), it allows you to test far more designs for the same budget

📜 New preprint! Syndirella is a pipeline for proposing synthesis-directed elaborations for fragment-inspired designs with explicit routes and poses! Preprint: https://t.co/Nkin6VU6Ni Code:

AI models show promise for accelerating antibody drug discovery but can fall short in testing. Research from @OPIGlets, published in Nature Computational Science highlights the need for larger, and more diverse datasets for reliable prediction. 👉 https://t.co/KIYywJ2Q2Q

Please be aware that OPIG's website and web apps will be down for maintenance from 09:30 UTC on Tuesday 14th October 2025, and will be unavailable for approximately one hour. Apologies for any inconvenience and for the short notice of this downtime.

AIR Street Capital's "State of AI" 2025 Report references Nicholas Runcie, Charlotte Deane, and Fergus Imrie's work on Assessing the Chemical Intelligence of Large Language Models. Read the whole report here: https://t.co/ANCQ2GPMTD And preprint here:

If this sounds interesting, we’d love to hear from you (email deane@stats.ox.ac.uk or imrie@stats.ox.ac.uk)! Positions available to start immediately.

Postdocs will contribute to: - Developing and applying AI/ML methods for small molecule design and selection - Running blind community challenges - Assessing the value of large-scale structural biology datasets

🚨 We’re hiring! The OPIG group is looking for multiple postdocs to join OpenBind, an open science initiative generating foundational structural biology data to power the next era of AI/ML for drug discovery. https://t.co/nCRTFuqadT https://t.co/ShjU5gOul8

Our first pandemic preparedness database, Pox-AbDab, is now available! We present sequence, structural & functional data on antibodies and nanobodies that bind/neutralise orthopoxviruses: DB: https://t.co/5QnYw31zdJ Preprint: https://t.co/VUCKEpqa4z Work led by Henriette Capel

Congratulations to the authors: Henriette Capel, Isaac Ellmen, Chris Murray, Giulia Mignone, Megan Black, Brendan Clarke, Conor Breen, Sean Tierney, Patrick Dougan, Richard Buick, Alex Greenshields-Watson, and Charlotte Deane, for their contributions and support on the project

See more in the: Paper: https://t.co/r67NiflpBr GitHub: https://t.co/yEkLa0T58P WebTool:

LICHEN outputs are customisable and tuneable to experimental needs or desired content, enabling a collaborative light sequence design by leveraging computational capabilities alongside experimental expertise.