WeiQu

Research scientist @ ByteDance Seed. SeedProteo & SeedFold. Opinions are my own.

[Weikun.Wu @awakenkunkun]

😎Binder #5 from PallAtom v2 was designed using a head-and-tail binding strategy that simultaneously optimizes for target-binding affinity and E3 inhibitory potency. We look forward to conducting additional E3 activity inhibition assays to further validate its functional efficacy

[WeiQu @qu0518]

RBX1 Binder Design Competition results are out!

Against BindCraft's dominant performance, PallAtom stands out as the strongest Diffusion-based Model.

🎉Congratulations to my old crew!

[Weikun.Wu @awakenkunkun]

😎Binder #5 from PallAtom v2 was designed using a head-and-tail binding strategy that simultaneously optimizes for target-binding affinity and E3 inhibitory potency. We look forward to conducting additional E3 activity inhibition assays to further validate its functional efficacy

[SeedFold @SeedFold]

Here is SeedProteo, our latest diffusion-based model for de novo all-atom protein design from ByteDance Seed! Our server is now live — feel free to give it a try! https://t.co/Tqsu2VPmWw

[Zaixiang Zheng @zaixiang_zheng]

Introducing PAR: We’ve been exploring autoregressive protein generation lately — generating protein structures in an autoregressive language modeling style is surprisingly fun.

[wesley hsieh @ICML 2026 @chengyenhsieh]

🧬 Protein Autoregressive Modeling (PAR), ByteDance Seed

We introduce PAR, the first multi-scale AR framework for protein backbone generation.

Autoregressive modeling of 3D protein structures has long been considered difficult.
Here’s how we make it possible with PAR. 👇

https://t.co/VYN6PJim2p

[Nima Shoghi @nimawrites]

Excited to share STAR-MD: a scalable autoregressive diffusion model that generates stable, high-quality protein MD trajectories at microsecond timescales, where existing methods fail catastrophically.

Accepted at ICLR 2026! Links and details in the thread 🧵👇

1/7

[SeedFold @SeedFold]

Introducing SeedFold from ByteDance Seed, a biomolecular structure prediction model that outperforms AlphaFold3 on protein-related folding tasks. https://t.co/4a93w6VLJL

[Chai Discovery @chaidiscovery]

We’re thrilled to announce our collaboration with @EliLillyandCo.

We’ll be deploying Chai’s AI models to power biologics discovery across a broad set of drug programs at Lilly.

This is a major step forward in pharma’s adoption of AI-native R&D.

[alex zhang @a1zhang]

Announcing a new @GPU_MODE kernel writing competition: our first featuring both NVIDIA and AMD hardware!

The first problem will be the Triangle Multiplication operator essential to the AlphaFold 🧬 models! It's a particularly tricky problem with no good public implementation!

[Biology+AI Daily @BiologyAIDaily]

Benchmarking Sequence-Based and AlphaFold-Based Methods for pMHC-II Binding Core Prediction: Distinct Strengths and Consensus Approaches

1. This study benchmarks four recent methods for pMHC-II binding core prediction: two sequence-based (NetMHCIIpan, DeepMHCII) and two AlphaFold-based (AF2-FT, AF3). AF3 achieved the highest positive recall (0.86), while NetMHCIIpan excelled in predicting non-binders, with the highest negative recall (0.93).

2. The study demonstrates that AlphaFold-based methods are excellent at predicting positive binders but tend to misclassify non-binding peptides. NetMHCIIpan, in contrast, is highly precise in identifying non-binders but struggles with positive recall, missing true binders.

3. A consensus approach combining AlphaFold-based methods’ strengths for positive prediction and NetMHCIIpan’s non-binder filtering improves overall accuracy and precision. This consensus approach achieves an F1 score of 0.93 with perfect recall, highlighting the potential for improved pMHC-II binding core predictions.

4. The study also identifies challenges with longer peptides and chimeric MHCs, both of which reduce prediction accuracy, especially for sequence-based methods.

5. Future improvements should focus on enhancing AlphaFold-based models’ ability to filter out non-binders and refining NetMHCIIpan’s binder prediction capabilities to strike a better balance between precision and recall.

📜Paper: https://t.co/4g326P8uSw

[Biology+AI Daily @BiologyAIDaily]

Improved deep learning prediction of antigen–antibody interactions @PNASNews

1/ This paper presents a breakthrough in predicting antigen–antibody interactions using deep learning models, particularly focusing on the spike protein of SARS-CoV-2. It evaluates the AF2Complex model's accuracy in predicting the structure of antibody-antigen complexes.

2/ One key innovation is the use of B cell sequencing data to improve prediction accuracy. By employing known RBD-binding antibodies, the AF2Complex model significantly outperforms previous approaches, achieving a top prediction accuracy of 61% on a benchmark of 36 antibodies.

3/ The study demonstrates that the deep learning approach can distinguish true antigen-binding antibodies from randomly selected antibodies with remarkable precision. For example, the model identifies true positives with a precision of 90% while maintaining a recall of 25%.

4/ This deep learning method offers a robust tool for predicting antibody-antigen interactions without requiring experimental structural data, making it highly valuable for vaccine design and therapeutic antibody development, especially in cases like COVID-19.

5/ The study highlights the AF2Complex model's capability to detect antibodies that target diverse epitopes, proving its utility for complex immune-related tasks and expanding the potential applications of deep learning in immunology and drug development.

💻Code: https://t.co/B96Mf2j4NK
📜Paper: https://t.co/VHMkk5tDyC

[Weikun.Wu @awakenkunkun]

1/X Pallatom is an end-to-end all-atom protein generation model that directly learns the distribution of atomic coordinates. We believe optimizing the P(all-atom) may open a new way for de novo protein design.
code(ing):https://t.co/QqNhRKmpGN
bioxiv:https://t.co/2I9Tv6DPra