Jerome Eberhardt

Excited to announce a preprint describing our software package Meeko! Meeko is a Python package that uses RDKit for receptor and ligand preparation, including protonation, bond order, and connectivity and processing of docking results. It is customizable and suitable for high-throughput workflows.

At least this it is what I wish for boltz.

And I think this is really what we should be aiming for! Not be limited by the amount of data, but only by how protein-ligand interactions are represented.

Yes, but it should not be bound by that. For example, the AutoDock-Vina scoring function (6 parameters) was fitted using only 800 complexes. However, I know performance mainly depends on having the correct pocket conformation (+ other factors), but not because it didn’t see a particular complex.

About the val&test, no clue how many truly novel protein-ligand complexes were deposited since 2023. This would directly affect our capacity to benchmark these methods.

That’s a bit problematic and means that we reached the limit of this type of architecture, right? If a model has to see first examples of a system for making predictions, then it will always lag behind the field, such as the discovery of a new pocket, new binding mode or chemistry.

So you changed the cutoff date to 2023-06-01. Why? Are you scared of Runs N’Poses? 😏

Introducing CAMEO Structures & Complexes - automated weekly blind benchmarking of structure prediction servers. Now with heteromeric and protein-ligand complexes.
Join us and register your server now!
cameo3d.org

Open Postdoc Position at the schwede lab, Biozentrum Basel.

🧪🧬🔬 Postdoc in Computational Structural Biology at the @biozentrum.unibas.ch & @sib.swiss in Basel, Switzerland.

www.biozentrum.unibas.ch/open-positio...

The position is initially funded for 3 years, possibility to start immediately.

#StructurePrediction, #Bioinformatics, #Uniprot3D, #AI

We re-ran AF3 without templates, since we noticed it could use any template in the PDB, including the ground truth. We see the performance drops slightly in the lowest bins, but the gap to other methods still exists. We will update the preprint shortly!

New leaderboard on @polarishub.io for Runs 'N Poses! 🎸

Anyone has any protein-ligand co-folding methods laying around they would like to put to the test?

polarishub.io/benchmarks/p...

Great work @peterskrinjar.bsky.social @jeeberhardt.bsky.social @torstenschwede.bsky.social @ninjani.bsky.social

Have #AI methods for protein-ligand co-folding moved beyond memorisation and predict really novel leads for #drugdiscovery? Or do we find “more of the same”? This new benchmark lets you find out… ⬇️⬇️⬇️

I want to thank my co-authors @jeeberhardt.bsky.social, @torstenschwede.bsky.social, @ninjani.bsky.social and all of our collaborators! RunsN’ Poses builds on PLINDER and OpenStructure—this work wouldn’t be possible without them!
Also thanks to @rokbreznikar.bsky.social for this amazing logo! 9/9

Basically, novelty is not defined by time in the PDB.

We’ve been benchmarking deep learning co-folding methods for protein-ligand complex prediction, leading to the creation of 🌹Runs N’ Poses🌹. Great effort by @peterskrinjar.bsky.social and @jeeberhardt.bsky.social putting this together so quickly. Please have a look, excited for community feedback!

Have protein-ligand co-folding methods moved beyond memorisation? https://www.biorxiv.org/content/10.1101/2025.02.03.636309v1

First lab #Chemrxiv preprint of 2025 and first foray into AI-based discovery of reactive cysteines! Congrats to First author Lisa Boatner and thanks to @forlilab.bsky.social, @jeeberhardt.bsky.social, and the rest of the team for the stellar collaboration! chemrxiv.org/engage/chemr...

Here's a year-end update from #PLINDER. It's been really great working on this project and all the other projects that it has kickstarted - the gift that keeps on giving.

Happy holidays everyone!

www.plinder.sh/blog/updates

Automated benchmarking of combined protein structure and ligand conformation prediction The prediction of protein-ligand complexes (PLC), using both experimental and predicted structures, is an active and important area of research, underscored by the inclusion of the Protein-Ligand Int....

We actually had a similar benchmark (with LDDT-PLI) in the same CASP15 issue a while ago (onlinelibrary.wiley.com/doi/10.1002/..., Fig3B) conclusions were (1) pocket detection needed for physics-based (2) DL models overfit (3) nothing performs on non "re-docking". Was my main inspiration for PLINDER

For future reference, this is the slide (without the bottom cropped) that should have been posted here. Boltz-1 and AF3 are actually performing similarly on the Chymase and Mpro datasets, and differ only on the Autotaxin dataset for still unknown reasons.

Hi @ddelalamo.bsky.social unfortunately, this paper from Jain et al. contains falsehoods, misleading comparisons, seemingly deliberate omissions, and is written in a tone not intended as a serious research paper. Please see our detailed response: www.linkedin.com/pulse/respon...

Ok, make sense. Thanks!

Nice work! Any reason why using AutoDock-Vina 1.1.2 instead of the last version (1.2.5), and prepare PDBQT files with Meeko (which allow you to convert it back to MOL)?

@jeeberhardt.bsky.social presenting CASP16 PLI baselines. Big shout out to @jeremywohlwend.bsky.social and @gcorso.bsky.social for helping us with running Boltz-1 on short notice! Pretty weird results on autotaxin.

Nope, no allosteric binders. The only molecule binding outside the canonical pocket was in the Mpro dataset, and it was stabilized by crystallographic contacts.

Cover of the special CASP issue in Proteins

The #CASP15 special issue has been published in PROTEINS. #OpenAccess

We want to thank all contributors to the CASP experiment and remind everyone that #CASP16 is only half a year away.

CASP organizers

onlinelibrary.wiley.com/toc/10970134...