Happy to play a small part in this perspective led by @andreasbender.bsky.social on where AI is in drug discovery, and what we should do in future. www.nature.com/articles/s41... Free link: rdcu.be/fyr77

Artificial intelligence in drug discovery — what it is, where we stand and the path forward - Nature Reviews Drug Discovery

Applications of artificial intelligence (AI) in drug discovery have attracted high interest in recent years, but evidence for clinically relevant impact so far is limited. This Perspective discusses p...

nature.com

🚨 Can AI reliably handle real-world, multi-step MD workflows comp chemists face every day? With Nithishwer Mouroug Anand and colleagues in the Biggin Lab @philbiggin.bsky.social @oxfordbiochemistry.bsky.social, we built MDArena from problems in active research projects. arxiv.org/abs/2608.02642

MDArena: Evaluating Coding Agents on Realistic Molecular Dynamics Workflows

Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort...

arxiv.org

It's exciting to see real users testing agentic workflows that make use of our models and tools. Thanks to the Biggin lab for publicizing these results and showing the work that still needs to be done!

Phil Biggin@philbiggin.bsky.social · last mo.

Latest preprint where we pooled our sim experiences to create "MDArena" for AI agents trying to automate MD simulation and analysis. Great work led by Nithishwer Mouroug Anand and @weitse-hsu.bsky.social and thanks to all lab members! @oxfordbiochemistry.bsky.social arxiv.org/abs/2608.02642

Latest preprint where we pooled our sim experiences to create "MDArena" for AI agents trying to automate MD simulation and analysis. Great work led by Nithishwer Mouroug Anand and @weitse-hsu.bsky.social and thanks to all lab members! @oxfordbiochemistry.bsky.social arxiv.org/abs/2608.02642

MDArena: Evaluating Coding Agents on Realistic Molecular Dynamics Workflows

Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort...

arxiv.org

Happy to see our work on SLCO2A1 with @smlea.bsky.social, Nakanishi and Newstead labs out now. Important insight into how prostaglandin and many drugs are transported. Hats off to @weitse-hsu.bsky.social for computational work! @oxfordbiochemistry.bsky.social www.nature.com/articles/s41...

Structural basis for prostaglandin and drug transport via SLCO2A1

Nature Communications - SLCO2A1 (also known as OATP2A1) is responsible for the transport of eicosanoids, including prostaglandins (PGs), as well as of a subset of nonsteroidal anti-inflammatory...

nature.com

Now out in JACS! 🎉 : "Computing Solvation Free Energies of Small Molecules with Experimental Accuracy"! It's been a pleasure to collaborate on this with Harry Moore (@jhmchem.bsky.social) & Gábor Csányi pubs.acs.org/doi/10.1021/...

New Preprint!! We show that binding entropy can be quantitatively predicted from crystallographic ensemble models, accounting for both protein conformational entropy and solvent entropy! www.biorxiv.org/content/10.6...

📢 Can AI-Predicted Complexes Teach Machine Learning to Compute Drug Binding Affinity? In our recent JCIM work, we tested whether co-folding models can be used for data augmentation for training ML-based scoring functions (SFs). We asked 3 simple but critical questions. 👇 (1/6)