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What is ESMC?

ESMC marks the latest innovation in the ESM series of protein language models, advancing the understanding of representation learning in protein biology. By training on an enormous dataset of billions of evolutionary sequences, it effectively captures representations that provide insights into the mechanistic aspects of protein structure and function. Utilizing a transformer architecture, the model prioritizes sequences as its main input and is trained on a dataset that includes up to 6 billion proteins. ESMC is designed for a range of applications within protein science, including structure prediction, functional annotation, protein design, and the investigation of evolutionary relationships among proteins. Furthermore, it has the ability to generate new proteins from partial sequences, structures, or specific functional requirements, which allows researchers to explore novel possibilities in protein design and biological research. The model is readily accessible through the Biohub Platform, enabling users to interact with it via an API and the ESM Python package, which offers quickstart resources for installation, API key generation, and connection to the platform, thus ensuring a user-friendly experience. This ease of access not only promotes wider participation in protein research but also fosters collaborative efforts across the scientific community, ultimately driving further advancements in the field. With its capabilities, ESMC opens new doors for innovation and discovery in protein science.

What is Differential Bio?

Differential Bio is a cutting-edge virtual platform aimed at improving bioprocess optimization by enhancing the predictability, scalability, and cost-effectiveness of microbial growth through the use of robotic lab automation and AI-driven insights. The journey begins with automated, high-throughput experiments that simulate large-scale stress conditions in microliter volumes, generating valuable wet-lab data through techniques in liquid handling, fermentation, and measurement. Custom AI algorithms meticulously analyze this information to predict various outcomes such as biomass growth, protein synthesis, cell viability, titer, production rates, yields, and cost implications. Accessible via a web-based interface, users can define their optimization goals and parameter thresholds, carry out unlimited in-silico simulations, assess the outcomes of simulated fermentation processes, engage in multi-objective optimization, and determine the best conditions for scaling production. This holistic methodology not only optimizes the workflow but also equips teams with the ability to make informed, data-driven decisions in the realm of bioprocess development, thereby promoting innovation and efficiency in the field. By integrating advanced technologies, Differential Bio sets a new standard for the future of bioprocessing.

Media

Media

Integrations Supported

Biohub
Python

Integrations Supported

Biohub
Python

API Availability

Has API

API Availability

Has API

Pricing Information

Free
Free Version
Free Trial Offered?

Pricing Information

Pricing not provided
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Biohub

Date Founded

2016

Company Location

United States

Company Website

biohub.ai/models/esmc

Company Facts

Organization Name

Differential Bio

Company Location

Germany

Company Website

www.differential.bio/

Categories and Features

Categories and Features

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