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Ian Dunn

@ian-dunn.bsky.social

453 followers 223 following 16 posts

PhD Candidate in Computational Biology @ University of Pittsburgh. Working on deep generative models for molecular structure. iandunn.io

Posts Replies Media

Ian Dunn @ian-dunn.bsky.social · 02/09/2025

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.

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Emma Flynn @emmaflynn.bsky.social · 27/05/2025

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...

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Rishal Aggarwal @rishalchich.bsky.social · 31/03/2025

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/

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Alisia Fadini @alisiafadini.bsky.social · 24/02/2025

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.

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Ian Dunn @ian-dunn.bsky.social · 15/12/2024

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

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Ian Dunn @ian-dunn.bsky.social · 15/12/2024

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...

colab.research.google.com

Google Colab

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Ian Dunn @ian-dunn.bsky.social · 11/12/2024

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

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David Ryan Koes @dkoes.compstruct.org · 10/12/2024

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/...

pubs.acs.org

CACHE Challenge #1: Docking with GNINA Is All You Need

We describe our winning submission to the first Critical Assessment of Computational Hit-Finding Experiments (CACHE) challenge. In this challenge, 23 participants employed a diverse array of structure...

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Reposted by Ian Dunn

David Ryan Koes @dkoes.compstruct.org · 22/11/2024

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.

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