I'm excited to share FlowMol3! The 3rd (and final) version of our flow matching model for 3D de novo, small-molecule generation. FlowMol3 achieves state of the art performance over a broad range of evaluations while having ≈10x fewer parameters than comparable models.

Our new preprint PharmacoForge: Pharmacophore Generation with Diffusion Models is out now! PharmacoForge quickly generates pharmacophores for a given protein pocket that identify key binding features and find useful compounds in a pharmacophore search. Check it out! 🧪 doi.org/10.26434/che...

New "blogpost" from our lab, that got accepted at ICLR 2025! We compare an old MCMC method known as Sequential Monte Carlo to generative models trained on energy functions (iDEM/iEFM) and show that MCMC does better. Check it out here: rishalaggarwal.github.io/ebmvsmcmc/

Structural biology is in an era of dynamics & assemblies but turning raw experimental data into atomic models at scale remains challenging. @minhuanli.bsky.social and I present ROCKET🚀: an AlphaFold augmentation that integrates crystallographic and cryoEM/ET data with room for more! 1/14.

MLSB + the AI4Science field are clearly outgrowing the ML conference workshop format

FlowMol at your fingertips! We just released a colab notebook to make using FlowMol super easy. Come chat with us tomorrow at @workshopmlsb ! #NeurIPS2024 🧪 colab.research.google.com/github/Dunni...

I'm presenting a new paper "Exploring Discrete Flow Matching for 3D De Novo Molecule Generation" at @workshopmlsb.bsky.social this week! More info in this thread but reach out if want to chat at NeurIPS about generative models or molecular design. arxiv.org/abs/2411.16644

Our paper describing our winning submission (tied with @olexandr.bsky.social) is out with some extra computational analysis of the predicted binding modes. We didn't do anything fancy (but the hits weren't that great either...). pubs.acs.org/doi/10.1021/...

Here is how Boltz-1 (green), DynamicBind (magenta), and GNINA (blue) dock a collection of random molecules. GNINA, using a classical sampling algorithm (MCMC) hits all concave regions while the ML samplers have distinct preferences. Boltz is the most likely to induce a fit.