Brian Hie
@brianhie.bsky.social
2.6K followers 110 following 7 posts
Machine learning for biology | Stanford and Arc Institute
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Reposted by Brian Hie
kevinkaichuang.bsky.social
Combine multimer structure prediction and an antibody language model to design de novo antibodies with nanomolar binding affinity.

@synbiogaolab.bsky.social @brianhie.bsky.social

www.biorxiv.org/content/10.1...
Reposted by Brian Hie
synbiogaolab.bsky.social
Having often dealt with the frustration of binder-limited projects, we sought a more accessible source for nanobodies than yeast display or llama. Here we introduce Germinal, computationally designing antibody-like binders with such a hit rate that only tens need to be screened for each target.
Reposted by Brian Hie
arcinstitute.org
In another preprint from the @brianhie.bsky.social Lab and @synbiogaolab.bsky.social, they introduce Germinal, a generative AI system for de novo antibody design.

Germinal produces functional nanobodies in just dozens of tests, making custom antibody design more accessible than ever before.
Reposted by Brian Hie
arcinstitute.org
In a new preprint from @brianhie.bsky.social's lab, the team reports the first generative design of viable bacteriophage genomes.

Leveraging Evo 1 & Evo 2, they generated whole genome sequences, resulting in 16 viable phages with distinct genomic architectures.
Reposted by Brian Hie
samuelhking.bsky.social
Many of the most complex and useful functions in biology emerge at the scale of whole genomes.

Today, we share our preprint “Generative design of novel bacteriophages with genome language models”, where we validate the first, functional AI-generated genomes 🧵
Reposted by Brian Hie
nationalacademies.org
#AI is transforming #biology, enabling rapid innovation — but with progress comes risk.

A new National Academies report examines how AI-driven biological tools could impact #biosecurity, from potential misuse to their role in risk mitigation.

Read: buff.ly/2unawE4
Reposted by Brian Hie
Reposted by Brian Hie
Reposted by Brian Hie
brianhie.bsky.social
We trained a genomic language model on all observed evolution, which we are calling Evo 2.

The model achieves an unprecedented breadth in capabilities, enabling prediction and design tasks from molecular to genome scale and across all three domains of life.
Reposted by Brian Hie
erictopol.bsky.social
The 2024 Good Tech Awards, spotlighted by @kevinroose.com for #AI/Science
nytimes.com/2024/12/30/t...
congrats!
@arcinstitute.org @brianhie.bsky.social @patrickhsuu.bsky.social @jameszou.bsky.social and the others for well deserved recognition
Reposted by Brian Hie
stanford-chemh.bsky.social
#YearInReview An AI-based method to develop better antibody medicines faster: https://buff.ly/3P74SCo
@brianhie.bsky.social
Institute Scholar Peter Kim, Brian Hie, Varun Shanker, and team’s new AI approach optimizes development of antibody drugs.
Reposted by Brian Hie
dijiang319.bsky.social
New @biorxiv-synthbio.bsky.social on #Evo 👀⤵️🧵 introducing Evo 1.5 for semantic mining + SynGenome - an AI-generated genomics database #AI #synbio #LLM🧬 @adititm.bsky.social @brianhie.bsky.social et al. @arcinstitute.org
brianhie.bsky.social
Yes they appear to be having server problems, here is a link to the paper PDF: evodesign.org/Semantic_Min...
evodesign.org
Reposted by Brian Hie
adititm.bsky.social
Excited to have the first project of my PhD out!! By leveraging genomic language model Evo’s ability to learn relationships across genes (i.e., "know a gene by the company it keeps"), we show that we can use prompt-engineering to generate highly divergent proteins with retained functionality. 🧵1/N
Reposted by Brian Hie
kevinkaichuang.bsky.social
A protein's position in the genome in relation to other genes is informative about function, so a genomic language model can be prompted to generate
- toxin/antitoxin pairs
- anti-CRISPR proteins

+ a database of 120B synthetic base pairs

www.biorxiv.org/content/10.1...

@brianhie.bsky.social
In-context genomic modeling and design with Evo. 120 billion base pairs of AI-generated genomic sequences with SynGenome. Evo generates functional anti-CRISPR proteins with no homology to known proteins. Evo generates functional toxin-antitoxin protein-protein interactions with remote homology to nature.
brianhie.bsky.social
We apologize for any inconvenience caused. This issue does not affect any of the results of our paper and only affects the evo-model API. Users interfacing via the Hugging Face AutoModelForCausalLM API should be unaffected. Special thanks to @danielchang.bsky.social for pointing us to the problem.
brianhie.bsky.social
We have found and fixed a bug in the code for Evo model inference affecting package versions from Nov 15-Dec 16, 2024, which has been corrected in the latest release. If you installed the package during this timeframe, please upgrade to correct the issue.