FLOWR sets a new benchmark for ligand generation. Trained on SPINDR, a curated 3D dataset fixing flaws in CROSSDOCKED, it outperforms PILOT with 70× speedup and higher pose fidelity.

Excited to present our poster on Boltzmann Priors for Implicit Transfer Operators tomorrow at ICLR 2026! See you tomorrow at poster 13, 10-12:30.

Registration for this years CHAIR Structured Learning Workshop is open. Speakers include: Klaus Robert Müller, Jens Sjölund, Alex Tong, Jan Stühmer Arnaud Doucet, Marco Cuturi, Marta Betcke, Elena Agliari, Beatriz Seoane, Alessandro Ingrosso, ui.ungpd.com/Events/60bfc7b…

New paper out with FAIR(+FAIR-Chemistry): Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching We present a scalable method for sampling from unnormalized densities beyond classical force fields. 📄: arxiv.org/abs/2504.11713

Discriminator-generator gap seems to be the most important idea in AI for scientific innovation. With compute + clever search, anything that we can measure will be optimized. First up will be environments that can be verified quickly, with continuous reward, and at scale.

New preprint alert 🚨 How can you guide diffusion and flow-based generative models when data is scarce but you have domain knowledge? We introduce Minimum Excess Work, a physics-inspired method for efficiently integrating sparse constraints. Thread below 👇arxiv.org/abs/2505.13375

We are looking for someone to join the group as a postdoc to help us with scaling implicit transfer operators. If you are interested in this, please reach out to me through email. Include CV, with publications and brief motivational statement. RTs appreciated!

Pfizer and AstraZeneca’s FLOWR model generates ligands 70× faster with improved structural accuracy by conditioning on protein pocket shapes. The shift from diffusion to flow-based models like FLOWR shows promising gains in speed and precision for drug discovery.

Look mom, no experimental data! Learning to score protein-ligand interactions from simulations １．This work introduces Ligand Force Matching (LFM), a novel per-target scoring method for protein-ligand binding that learns from molecular dynamics (MD) simulations—no experimental

Synthesizable by Design: A Retrosynthesis-Guided Framework for Molecular Analog Generation １．SynTwins is a new framework for generating synthetically accessible molecular analogs, bridging the gap between AI molecule design and lab synthesis—a persistent bottleneck in drug and

Very cool application of pre-trained chemical language models!

PROTAC-Splitter: A Machine Learning Framework for Automated Identification of PROTAC Substructures 1. A new machine learning framework, PROTAC-Splitter, has been developed to automate the challenging task of identifying and annotating substructures within Proteolysis-targeting

🚀 Presenting #DeFoG: our discrete flow‑matching framework for graph generation! Catch our #ICML2025 oral presentation today (3:30 – 3:45 PM, in West Exhibition Hall C) and drop by the poster right after (4:30 –7:00). Come chat graphs & generative models! Manuel Madeira

Thrilled to announce that the first paper from Flemmings PhD was accepted as Poster for NeurIPS2025! In this paper we adapt the HollowNet idea from Ricky T. Q. Chen to equivariant MP networks, to get cheap sample likelihoods for CNF-based Boltzmann Generators. Preprint/code soon.

New preprint out! We present "Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamics,"