PhD student at AstraZeneca and Chalmers University working on generative models for molecules. Computer Science undergrad at Imperial College. Gothenburg, SwedenJoined June 2024
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 @RickyTQChen to equivariant MP networks, to get cheap sample likelihoods for CNF-based Boltzmann Generators. Preprint/code soon.
🚀 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! @manuelmlmadeira
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…
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…
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…
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.
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!
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
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 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
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.
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