3⃣ Efficient Molecular Conformer Generation with SO(3) Averaged Flow-Matching and Reflow
openreview.net/forum?id=1B1...

⭐️ I'm also on a panel on synthetic data (synthetic-data-iclr.github.io)!

I'm excited to discuss research and to meet new and old friends and collaborators! 🎉

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@gembioworkshop.bsky.social
papers:

1⃣ EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants
arxiv.org/abs/2410.09667 (oral)
(screenshot below)

2⃣ Hierarchical Protein Backbone Generation with Latent and Structure Diffusion
arxiv.org/abs/2504.09374

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3⃣ Energy-Based Diffusion Language Models for Text Generation
arxiv.org/abs/2410.21357
Posters 2

4⃣ Truncated Consistency Models
arxiv.org/abs/2410.14895
Posters 4
(screenshot below)

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Main track:

1⃣ Proteina: Scaling Flow-based Protein Structure Generative Models
research.nvidia.com/labs/genair/...
Orals 3B, posters 4
(video below)

2⃣ ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids
arxiv.org/abs/2503.05025
Orals 2C, posters 3

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🔸Check out our project page (research.nvidia.com/labs/genair/...), our paper (arxiv.org/abs/2503.00710), and our code (github.com/NVIDIA-Digit...).

🔥 We released 8 sets of weights, for all experiments, for you to play with! 🔥

Enjoy! And see you at ICLR'25! 😀

(11/11)

🔸Proteina is a fantastic collaboration with wonderful colleagues at NVIDIA:

🔥 Tomas Geffner*, @kdidi.bsky.social*, Zuobai Zhang*, Danny Reidenbach, Zhonglin Cao, @jyim.bsky.social , Mario Geiger, @machine.learning.bio, Emine Kucukbenli, @arashv.bsky.social, @karstenkreis.bsky.social* 🔥

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🔸We also demonstrate LoRA-based fine-tuning on a smaller set of high-quality protein structures from the PDB, and we show that autoguidance, where the model is guided by a weaker version of itself, can be used to boost designability. See our paper for details.

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🔸Protein structure generation performance is often measured in terms of designability, diversity and novelty. Drawing inspiration from image generation, we explore three complementary metrics that analyze models at the distribution level, providing additional insights.

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🔸Proteina also outperforms previous models on motif-scaffolding, where a functionally relevant motif is given and the model is tasked with generating a viable supporting scaffold. Below, we show quantitative evaluations for the benchmark introduced by RFDiffusion.

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🔸Quantitatively, Proteina achieves state-of-the-art designable and diverse protein backbone generation (unconditional or fold class-conditional). In particular at long lengths, it significantly outperforms previous models, which cannot generate proteins at this scale.

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🔸Proteina uses an efficient and scalable non-equivariant transformer network with up to 400M parameters. We minimize the use of computationally expensive and memory-consuming layers such as triangle attention, allowing Proteina to generate backbones of up to 800 residues.

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🔸The fold class conditioning provides fine control during generation and allows us to guide with respect to high-level secondary structure content or low-level specific fold classes. The method can also be used to enhance the amount of beta sheets in a controlled manner.

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🔸We train on synthetic datasets of up to 21M protein structures curated from the AlphaFold Database (left plot). Further, we condition Proteina on hierarchical C.A.T.H protein structure classification labels (right plot), with a tailored classifier-free guidance scheme.

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🔸Proteina is a novel flow-based protein backbone generative model. It uses an alpha carbon backbone representation, is trained with flow matching, relies on a scalable and efficient transformer network, and offers hierarchical fold class conditioning for enhanced control.

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