ESMCBiohub
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NVIDIA PhysicsNeMoNVIDIA
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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
NVIDIA PhysicsNeMo is an open source Python deep-learning framework for building, training, fine-tuning, and inferring physics-AI models that combine physics knowledge with data to accelerate simulations, create high-fidelity surrogate models, and enable near-real-time predictions across domains such as computational fluid dynamics, structural mechanics, electromagnetics, weather and climate, and digital twin applications. It provides scalable, GPU-accelerated tools and Python APIs built on PyTorch and released under the Apache 2.0 license, offering curated model architectures including physics-informed neural networks, neural operators, graph neural networks, and generative AI–based approaches so developers can harness physics-driven causality alongside observed data for engineering-grade modeling. PhysicsNeMo includes end-to-end training pipelines from geometry ingestion to differential equations, reference application recipes to jump-start workflows.
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Platforms Supported
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Cloud
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
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Android
Not Supported
Chromebook
Not Supported
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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
Researchers, engineers, and developers who need an open source Python AI framework to build, train, fine-tune, and deploy physics-informed machine learning models for simulation, digital twins, and real-time prediction
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Support
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API
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Pricing
Free
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Free
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Training
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Company InformationBiohub
Founded: 2016
United States
biohub.ai/models/esmc
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Company InformationNVIDIA
Founded: 1993
United States
developer.nvidia.com/physicsnemo
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