WhiteLab Genomics

About WhiteLab Genomics

WhiteLab Genomics utilizes a proprietary platform that combines graph knowledge technology and machine learning to optimize the design of payloads and vectors for gene and cell therapies. This technology accelerates the development process, reducing costs and time to market for genomic medicines, making them more accessible to patients.

<problem> Genomic medicine development is often slow and costly due to the complexities of payload and vector design, which require extensive datasets and in-silico simulations. Optimizing these designs and assessing genotoxicity can be challenging, hindering the efficient development of gene and cell therapies. </problem> <solution> WhiteLab Genomics offers an AI-driven platform that accelerates the discovery and development of genomic medicines. The platform leverages graph knowledge technology and machine learning to optimize payload and vector design for gene, RNA, and cell therapies. By providing in-silico simulations based on exhaustive datasets, the platform enables scientists to answer key scientific and technological questions, such as optimizing payload and vector design, assessing genotoxicity, and identifying optimal experimental protocols for in vivo and in vitro strategies. This approach reduces the development time and costs associated with bringing genomic medicines to market. </solution> <features> - AI-powered platform for optimizing payload and vector design - Graph knowledge technology and machine learning algorithms - In-silico simulations based on exhaustive datasets - Genotoxicity assessment capabilities - Identification of optimal experimental protocols for in vivo and in vitro strategies - Support for gene therapy, RNA therapy, and cell therapy development </features> <target_audience> The primary audience includes scientists and researchers in the field of genomic medicine, particularly those focused on gene therapy, RNA therapy, and cell therapy. </target_audience>

What does WhiteLab Genomics do?

WhiteLab Genomics utilizes a proprietary platform that combines graph knowledge technology and machine learning to optimize the design of payloads and vectors for gene and cell therapies. This technology accelerates the development process, reducing costs and time to market for genomic medicines, making them more accessible to patients.

Where is WhiteLab Genomics located?

WhiteLab Genomics is based in Paris, France.

When was WhiteLab Genomics founded?

WhiteLab Genomics was founded in 2019.

How much funding has WhiteLab Genomics raised?

WhiteLab Genomics has raised 10500000.

Location
Paris, France
Founded
2019
Funding
10500000
Employees
50 employees
Major Investors
Y Combinator, Debiopharm Group, Omnes Capital

Find Investable Startups and Competitors

Search thousands of startups using natural language

WhiteLab Genomics

⚠️ AI-generated overview based on web search data – may contain errors, please verify information yourself! You can claim this account with your email domain to make edits.

Executive Summary

WhiteLab Genomics utilizes a proprietary platform that combines graph knowledge technology and machine learning to optimize the design of payloads and vectors for gene and cell therapies. This technology accelerates the development process, reducing costs and time to market for genomic medicines, making them more accessible to patients.

whitelabgx.com5K+
cb
Crunchbase
Founded 2019Paris, France

Funding

$

Estimated Funding

$10M+

Major Investors

Y Combinator, Debiopharm Group, Omnes Capital

Team (50+)

No team information available.

Company Description

Problem

Genomic medicine development is often slow and costly due to the complexities of payload and vector design, which require extensive datasets and in-silico simulations. Optimizing these designs and assessing genotoxicity can be challenging, hindering the efficient development of gene and cell therapies.

Solution

WhiteLab Genomics offers an AI-driven platform that accelerates the discovery and development of genomic medicines. The platform leverages graph knowledge technology and machine learning to optimize payload and vector design for gene, RNA, and cell therapies. By providing in-silico simulations based on exhaustive datasets, the platform enables scientists to answer key scientific and technological questions, such as optimizing payload and vector design, assessing genotoxicity, and identifying optimal experimental protocols for in vivo and in vitro strategies. This approach reduces the development time and costs associated with bringing genomic medicines to market.

Features

AI-powered platform for optimizing payload and vector design

Graph knowledge technology and machine learning algorithms

In-silico simulations based on exhaustive datasets

Genotoxicity assessment capabilities

Identification of optimal experimental protocols for in vivo and in vitro strategies

Support for gene therapy, RNA therapy, and cell therapy development

Target Audience

The primary audience includes scientists and researchers in the field of genomic medicine, particularly those focused on gene therapy, RNA therapy, and cell therapy.

Want to add first party data to your startup here or get your entry removed? You can edit it yourself by logging in with your company domain.