I'm excited to announce that GCDM for 3D molecule diffusion generation and optimization is now published in Nature @CommsChem! Paper: https://t.co/pnqRVmnjTe Code: https://t.co/uu0CiuaKsK

Interested in learning more about how flow matching has begun to advance bioinformatics and computational biology? And how it has already started making strides towards the development of an AI-based virtual cell? Paper: https://t.co/haI8aSPdvN Code:

🔬Interested in training AlphaFold3 faster, at scale, and beyond NVIDIA GPU? Now you can. AlphaFold3 is a major leap in biomolecular modeling, but behind the scenes, it introduces severe system bottlenecks: 🧠 2D EvoAttention spikes memory usage 📉 Retrieval-augmented training

Delighted to announce that FlowDock has been accepted to ISMB 2025. See you all in Liverpool! Paper: https://t.co/zvl7wbHVB8 Code: https://t.co/F6t9uW2w7V

v0.6.0 of PoseBench is now available, featuring (1) results for AlphaFold 3, the new PLIF-WM metric, and (3) the new DockGen-E dataset of challenging docking targets. See the GitHub release below for more details. Paper: https://t.co/rKCnwvD8lC Code:

Excited to release FlowDock, an all-atom flow matching model for generative protein-ligand docking and affinity prediction (ranked as a top method in CASP16)! Paper: https://t.co/zvl7wbItqG Code: https://t.co/F6t9uW33Xt

PoseBench v0.5.0 is now released, featuring (1) docking results with AlphaFold 3's predicted protein structures, (2) Chai-1's benchmarking results, and (3) support for running exhaustive HPC benchmarking sweeps. 🧪 Paper: https://t.co/rKCnwvD8lC Code:

At @icmlconf this week where I'm presenting PoseBench at the AI4Science workshop as a spotlight. I'll also give an oral presentation on RNA-FrameFlow at the SPIGM workshop (AI4Science spotlight as well!) on behalf of many amazing collaborators including @rishabh16_ and @chaitjo.

Unfortunately can't join in-person @icmlconf 🇦🇹 but our awesome co-author @MoreheadAlex will be there!!! Check out our Oral presentation @ SPIGM Workshop on 26 July and Spotlight poster @AI_for_Science Workshop on 27 July ✨🥳 See our poster + schedule below 👀👇🏻

Examples of pocket-specific 3D molecules generated by GCDM:

GCDM also generates significantly more PoseBusters-valid 3D molecules in target protein pockets.

GCDM enables out-of-the-box (reliable) property and stability-specific optimization of existing 3D molecules.

GCDM generates more than twice as many PoseBusters-valid large (i.e., GEOM-Drugs-sized) 3D molecules compared to existing methods.

GCDM generates 3D molecules with specific molecular properties more accurately and stably compared to existing methods.

Pocket-only docking results for the PoseBusters Benchmark dataset:

New benchmark results for the DockGen dataset from the recent DiffDock-L paper:

LoG Conference 2024 is back !!!👉 We are looking for more reviewers! We have a special emphasis on review quality via monetary rewards, a more focused conference topic, and low reviewer load (max 3 papers). But for this we need your help! Sign up here: https://t.co/u14d8fxM8t!

Let us know if you have any feedback for us regarding the future of PoseBench's development! We would be happy to hear from you.

Using a curated version of the CASP15 protein-ligand interaction dataset, we find that pretraining on a large corpus of 3D (fragment) molecular structures appears to be key to generalizing well to multi-ligand docking (e.g., by mostly avoiding ligand-ligand steric clashes).

With PoseBench, we find that all recent DL docking methods (except for one) fail to generalize to multi-ligand protein targets and also that template-based docking algorithms perform equally well or better for multi-ligand docking as recent single-ligand DL docking methods.