Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Alchemite specializes in AI-enhanced physical modeling and offers solutions that assist organizations in deriving actionable insights from both experimental and simulation data, merging machine learning techniques with physics-informed models to enhance prediction accuracy, decrease experimental expenses, and streamline product and process development. Their offerings encompass a variety of domains, including materials discovery and design, predictive modeling for performance and reliability, multiscale modeling that bridges atomic and macroscopic behavior, as well as the automation of various workflow tasks such as data integration, surrogate modeling, and model validation. Furthermore, they advocate for physics-aware neural networks and hybrid modeling strategies that adhere to fundamental scientific principles while simultaneously learning from data, leading to quicker and more precise simulations, a diminished need for costly physical testing, and better-informed decision-making processes. Intellegens' tools find applications in various fields, including the prediction of battery performance and optimization of chemical processes, showcasing their versatility and effectiveness in addressing complex challenges. By integrating advanced computational methodologies, Alchemite aims to empower organizations to innovate and achieve their goals more efficiently.
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.
API Access
Has API
API Access
Has API
Integrations
Biohub
Python
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Intellegens
Founded
2017
Country
United Kingdom
Website
intellegens.com/solutions/
Vendor Details
Company Name
Biohub
Founded
2016
Country
United States
Website
biohub.ai/models/esmfold2
Product Features
Machine Learning
Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization