Kieran Didi

The unification of representation learning and generative modelling A deep dive into the convergence of discriminative and generative AI, covering 4 phases of evolution from REPA to RAE and beyond.

Too many REPA / RAE / representation alignment papers lately?
I was lost too, so I wrote a blog post that organizes the space into phases and zooms in on what actually matters for general/molecular ML.
Curious what folks think - link below!

🔗 Blog: kdidi.netlify.app/blog/ml/2025...

GPU-accelerated homology search with MMseqs2 - Nature Methods Graphics processing unit-accelerated MMseqs2 offers tremendous speedups for homology retrieval from metagenomic databases, query-centered multiple sequence alignment generation for structure predictio...

GPU-accelerated MMseqs2 offers tremendous speedup for homology retrieval, protein structure prediction with ColabFold, and protein structure search with Foldseek. @martinsteinegger.bsky.social @milot.bsky.social @machine.learning.bio

www.nature.com/articles/s41...

GPU-accelerated homology search with MMseqs2 - Nature Methods Graphics processing unit-accelerated MMseqs2 offers tremendous speedups for homology retrieval from metagenomic databases, query-centered multiple sequence alignment generation for structure predictio...

MMseqs2-GPU sets new standards in single query search speed, allows near instant search of big databases, scales to multiple GPUs and is fast beyond VRAM. It enables ColabFold MSA generation in seconds and sub-second Foldseek search against AFDB50. 1/n
📄 www.nature.com/articles/s41...
💿 mmseqs.com

For more details read the thread by the man himself: bsky.app/profile/ncor...

An incredible project to witness, led by the most incredible dreamteam @ncorley.bsky.social , @simonmathis.bsky.social and Rohith Krishna with an amazing team inside and outside the Baker lab. Check it out and let us know what you think/contribute to the codebase! 6/6

The preprint shows how atomworks leads to better reference conformers (and better predictions!), enables advanced features in RF3 like chirality-aware training or ligand templating and narrows the performance gap to closed-source models. 5/6

`atomworks.ml` on the other hand offers advanced dataset featurization and sampling for deep learning workflows, all operating on the canonical AtomArray object from @biotite_python so that all transforms are traceable and generalizable between models. 4/6

AtomWorks has two main components: atomworks.io takes a file (cif, sdf, ...) and does parsing, cleaning and more. You can also look at your structures in a notebook or via PyMol thanks to pymol-remote, so you can directly inspect if your code does what you want! 3/6

In the past, every BioML model had its own data pipeline, creating loads of overhead. With AtomWorks, >80% of code is shared between models like ProteinMPNN, RF3 or design models. 2/6

AtomWorks is out! Building upon @biotite_python, we built a toolkit for all things biomolecules and trained RF3 with it. All open-source, test it via `pip install atomworks`!

AtomWorks: github.com/RosettaCommo...
RF3: github.com/RosettaCommo...
Paper: tinyurl.com/y2w4z65b

1/6

RosettaFold 3 is here! 🧬🚀

AtomWorks (the foundational data pipeline powering it) is perhaps the really most exciting part of this release!

Congratulations @simonmathis.bsky.social and team!!! ❤️

bioRxiv preprint: www.biorxiv.org/content/10.1...

Accelerating Biomolecular Modeling with AtomWorks and RF3 Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilita...

(1/7)
Training biomolecular foundation models shouldn't be so hard. And open-source structure prediction is important. So today we're releasing two software packages: AtomWorks and RosettaFold3 (RF3)

[https://www.biorxiv.org/content/10.1101/2025.08.14.670328v2](www.biorxiv.org/content/10.1...)

Very excited about our latest all-atom generative model proteina, check out the project page (research.nvidia.com/labs/genair/...) and stay tuned for the code release soon!

Excited to present my first paper officially as a PhD student now as an ICLR Oral this week! Super fun work with the GenAIR team at NVIDIA.

Talk: Fr 10:54 - 11:06 (Oral Session 3B, Garnet 213-215)
Poster: Fr 15:00-17:30 (Hall 3 + Hall 2B #5)

Come by the poster/reach out to chat

Such a fun project to work on with a stellar team! Stay tuned for other things to come here, and see you all in Singapore!

Yet another story of issues with benchmarks and evaluations in ML4bio + a much stronger and fair benchmark #bioMLeval

Have a look at our shiny new benchmark for motif-scaffolding in computational protein design! New (and harder) tasks, including a reproducible evaluation pipeline

This!

Also well put in this editorial in PLOS Comp Biol:
Putting benchmarks in their rightful place: The heart of computational biology
doi.org/10.1371/jour...

Our paper on computational design of chemically induced protein interactions is out in @natureportfolio.bsky.social. Big thanks to all co-authors, especially Anthony Marchand, Stephen Buckley and Bruno Correia!

t.co/vtYlhi8aQm

So excited for you and what is to come! Onwards and Upwards;)

Structure-based drug design with equivariant diffusion models - Nature Computational Science This work applies diffusion models to conditional molecule generation and shows how they can be used to tackle various structure-based drug design problems

After two years, our paper on generative models for structure-based drug design is finally out in @natcomputsci.bsky.social

www.nature.com/articles/s43...

Thanks Jascha 🫶

We’re working hard to create sustainable funding mechanisms for open source scientific tooling - understanding the challenge landscape is a key first step!

Love PyMOL Remote, one of these tools that does one thing and does it well!

This should go chiral

MSAs go brrr with MMseqs2-GPU! Super fun project, happy to work with and learn from a stellar team of engineers and scientists. Try it out and stay tuned!

📄 Preprint: www.biorxiv.org/content/10.1...
💾 Code: mmseqs.com
🗞️ Blog: developer.nvidia.com/blog/boost-a...