ESMFold2

ESMFold2

Biohub
+
+

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About

ESMFold2 is the successor to ESMFold, setting a new state of the art for single-sequence structure prediction and enabling the generation of new functional proteins through searching the ESMC model’s latent space. The model predicts high-resolution, all-atom 3D structures of biomolecular complexes directly from sequence, with optional multiple sequence alignment input for enhanced accuracy on challenging targets. It is designed for structure prediction using sequence and structure modalities, with ESM representations powering a series of looped folding layers and a diffusion model projecting pairwise representations to atomic-resolution predictions. ESMFold2 predicts protein structures directly from amino acid sequences and outputs comprehensive structural information, including all-atom coordinates for backbone and side chains, confidence metrics, and optional distogram predictions for detailed structural analysis.

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.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Structural biology researchers who need fast, high-resolution protein structure prediction from sequence for analysis, exploration, and experimental planning

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

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Biohub
Founded: 2016
United States
biohub.ai/models/esmfold2

Company Information

NVIDIA
Founded: 1993
United States
developer.nvidia.com/physicsnemo

Alternatives

Alternatives

ESMC

ESMC

Biohub
Alchemite

Alchemite

Intellegens
Evo 2

Evo 2

Arc Institute
NeuralWing

NeuralWing

Emmi AI
HyperProtein

HyperProtein

Hypercube
NeuralMould

NeuralMould

Emmi AI

Categories

Categories

Integrations

Python
Biohub
PyTorch

Integrations

Python
Biohub
PyTorch
Claim ESMFold2 and update features and information
Claim ESMFold2 and update features and information
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Claim NVIDIA PhysicsNeMo and update features and information