sk

Computational Structural Biologist |

[sk @compchemm]

@SylvainGariel @ahramkim1128 CMS
https://t.co/RcGc6n5kXd

[Alex Rives @alexrives]

At @Biohub, our goal is to build models that accelerate scientific discovery and progress toward the cure to disease. We’re releasing all of this under MIT license allowing commercial and non-commercial use.

Read more here: https://t.co/Rt0Vo4QnSA

[Alex Rives @alexrives]

Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology.

The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics.

We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity.

We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures.

ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences.

A world model of protein biology emerges through language modeling.

We’ve used the techniques of mechanistic interpretability developed to understand large language models to understand the concepts ESM uses to represent proteins.

The model’s representation space has a compositional organization of features across scales, levels of complexity, and abstraction, that reflects and mirrors the understanding of protein biology developed through a century of empirical science.

This understanding emerges without prior knowledge, just from language modeling of protein sequences.

Language models are becoming a powerful substrate to understand and program biology.

The design of protein interactions is one of the most fundamental problems in biophysics, and has critical implications for the discovery of new medicines. A simple gradient based search with the model was able to discover high-affinity protein binders.

I'm excited by the potential this has to accelerate basic science and the understanding of proteins. And especially for the new avenues it opens up for therapeutic design and medicine.

[sk @compchemm]

@LimitBreakerLab @josiezayner LOL! Do you know anything about Cursor.

[sk @compchemm]

- Protein Data Bank (PDB)
- Foldseek
- AlphafoldDB
- Uniprot
- Interpro
- Jaspar
- protein-sequence-similarity-search
- protein-sequence-msa
- Ensemble
- Pubchem
- Chembl
- pubmed
- unibind-database
- clinical-trials-database
- opentargets-database
Finally Pymol OMG!!

[sk @compchemm]

- Protein Data Bank (PDB)
- Foldseek
- AlphafoldDB
- Uniprot
- Interpro
- Jaspar
- protein-sequence-similarity-search
- protein-sequence-msa
- Ensemble
- Pubchem
- Chembl
- pubmed
- unibind-database
- clinical-trials-database
- opentargets-database
Finally Pymol OMG!!

[sk @compchemm]

Antigravity 2.0 - Computational Biologist.
Check it out and thank me later😉#phd #postdoc

[sk @compchemm]

@VWFNDRcamera @zerocam_app someone is stealing you

[sk @compchemm]

@Teknovist @VWFNDRcamera Someone copied you @zerocam_app

[sk @compchemm]

@billytcl @shae_mcl Should look for who is the depositors

[sk @compchemm]

repo now available on deepwiki: https://t.co/3dzSbpykGz

[Yeqing Lin @lin_yeqing]

Introducing Genie 3, a generative protein model that substantially advances the state-of-the-art for binder design, increasing in silico success rates by up to 20x on hard multimeric targets. It also debuts a form of inference-time scaling unobserved in other design models. 🧵1/8

[sk @compchemm]

@MolBioMike I have recently read a bit about the structure and interaction of rbx1 and its partners during & after the competition. I came to the conclusion & similar to a postdoc who left a comment on one of posts that targeting rbx1 as a monomer is the worst-case' biological scenario.

[sk @compchemm]

@TensorTwerker Interesting I use protenix msa

[sk @compchemm]

@MartinPacesa @miangoar

[sk @compchemm]

@MartinPacesa @miangoar

[Minkyung Baek @minkbaek]

AI can now design antibodies that bind with atomic precision, but not ones that cells can produce. Our preprint closes this gap, delivering a structural principle, an AI-guided rescue pipeline, and adalimumab variants with 20-100x in vivo potency.

https://t.co/GvfgHA5EcU

[Dr Alexander D. Kalian @AlexanderKalian]

Every time I tell AI utopianists that biology is too complex for AI to "solve", they cite the success of AlphaFold.

No, AlphaFold did not "solve" protein folding. It gets broad structures correct ~70-88% of the time (depending on evaluation), enabling useful but flawed statistical guesses.

True "solving" would require ~99.9%+ accuracy, practically zero meaningful edge cases, and high confidence across fine details like side chains and conformations.

Even then, this is just one narrow slice of the complexities of proteomics.

The persistent gap between the "AlphaFold solved protein folding" claim and reality is a perfect example of AI overhype in biology.

[sk @compchemm]

@TensorTwerker Before you proceed

[sk @compchemm]

@outsource_ This guy Qwen3.6-27B-UD-Q4_K_XL(1.13GB) can generate high quality draft than Qwen3-1.7B-UD-Q4_K_XL(1.34GB) with just 210MB extra size will test tomorrow and share results. thank you once again and there are smart people than me.

[sk @compchemm]

@outsource_ Conclusion: Dynamic Quants from Unsloth Better
I assume I can get easily 180-200t/s with the dynamic quants. These two guys Qwen3-1.7B-UD-Q4_K_XL(1.13GB), Qwen3.6-27B-UD-Q4_K_XL with Qwen3.6-27B-UD-Q4_K_XL in ik_llama.cpp