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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
No images available
Integrations
Biohub
Yes
Hugging Face
No
Mira
Yes
ModelScope
No
Python
Yes
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/