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?

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 $10.5M.

Who founded WhiteLab Genomics?

WhiteLab Genomics was founded by Ori Cohen, David Del Bourgo and Lucia Cinque.

Ori Cohen - CEO/CTO
David Del Bourgo - CEO/Co-Founder
Lucia Cinque - Co-Founder/Chief of Staff

Location

Paris, France

Founded

2019

Funding

$10.5M

Employees

50 employees

Investors

Y Combinator

WhiteLab Genomics

Paris, FranceFounded 2019505K+ followers10/10 TractionRelative Traction Score based on online presence metrics compared to companies in the same age group.

Updated 19 months ago

Funding

$10.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

DGOC

Funding rounds are not available yet.

Founders

Product

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.

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.

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