ESMCBiohub
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Evo 2Arc Institute
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About
ESMC is the latest in the ESM family of protein language models, establishing a new frontier in representation learning for protein biology. Trained on billions of evolutionary sequences, it learns representations that reflect a mechanistic reduction of protein structure and function. The model is built on a transformer architecture, supports sequences as its core modality, and is trained on up to 6 billion proteins. ESMC is designed for protein science research, including structure prediction, function annotation, protein design, and understanding evolutionary relationships between proteins. It can generate novel proteins from partial sequence, structure, or functional constraints, helping researchers explore new possibilities in protein design and biological discovery. The Biohub Platform provides access to ESMC through the API and the ESM Python package, with quickstart resources for installing the package, creating an API key, connecting to the platform.
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About
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It utilizes a frontier deep learning architecture to model biological sequences at single-nucleotide resolution, achieving near-linear scaling of compute and memory relative to context length. Trained with 40 billion parameters and a 1 megabase context length, Evo 2 processes over 9 trillion nucleotides from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function prediction across multiple biological modalities, including DNA, RNA, and proteins, and to generate novel sequences with plausible genomic architecture. The model's capabilities have been demonstrated in tasks such as designing functional CRISPR systems and predicting disease-causing mutations in human genes. Evo 2 is publicly accessible via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Protein science researchers who need transformer-based sequence representations for structure prediction, function annotation, mutation analysis, and protein design
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Audience
Computational biologists and genetic engineers in need of a tool to model and design biological sequences across multiple modalities, enhancing research in genomics and synthetic biology
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Pricing
Free
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationBiohub
Founded: 2016
United States
biohub.ai/models/esmc
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Company InformationArc Institute
United States
arcinstitute.org/tools/evo
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