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Description

ESMFold2 builds upon its predecessor, ESMFold, by establishing a new benchmark in single-sequence structure prediction and facilitating the creation of novel functional proteins via exploration of the latent space within the ESMC model. This advanced model is capable of forecasting high-resolution, all-atom 3D structures of biomolecular complexes straight from the amino acid sequence, and it allows for the incorporation of multiple sequence alignments to improve accuracy on difficult targets. Tailored for predicting structures through both sequence and structure modalities, it employs ESM representations that drive a series of looped folding layers while a diffusion model translates pairwise representations into atomic-resolution outcomes. ESMFold2 excels in predicting protein structures from amino acid sequences, providing detailed structural data, including precise all-atom coordinates for both backbone and side chains, along with confidence metrics and optional distogram predictions for in-depth structural evaluation. Furthermore, its innovative approach enhances the understanding of protein folding dynamics and functional implications, making it a valuable tool for researchers in the field.

Description

Hydrogen-1.0 is a scientific language model with open weights, created by Deep Principle by post-training GLM-5.3 on a diverse range of data including chemistry, materials science, and life sciences, as well as laboratory notes and research paths. This model aims to assist researchers in tackling scientific inquiries, elucidating derivations, examining reaction pathways, interpreting spectra, designing experiments, and formulating material hypotheses. Among its demonstrations are scientific computing workflows, exploration of reaction networks, NMR structure analysis, planning of experiments, and discovery of materials. Users can access the model by downloading its weights from platforms like Hugging Face and ModelScope, using API access via NeuroSpark, or through Deep Principle's Mira AI Scientist. Moreover, Hydrogen-1.0 can be executed locally on suitable hardware and software configurations. It is essential for researchers to assess the model's outputs in light of pertinent evidence and scientific limitations, ensuring a responsible application in their work. Ultimately, Hydrogen-1.0 represents a significant advancement in making scientific research more efficient and accessible.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

No images available

Integrations

Biohub Yes 
Hugging Face No 
Mira Yes 
ModelScope No 
Python Yes 

Integrations

Biohub No 
Hugging Face Yes 
Mira No 
ModelScope Yes 
Python No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Biohub

Founded

2016

Country

United States

Website

biohub.ai/models/esmfold2

Vendor Details

Company Name

Deep Principle

Founded

2024

Country

China

Website

blog.deepprinciple.com/introducing-hydrogen-1.0/

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