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Value proposition
AI for digital twin of cells
DeepLife is an early stage startup founded in 2019 by Jonathan Baptista and Jean-Baptiste Morlot, backed by international investors, developing the first SaaS platform for Digital Twin of cells.
While we are able to design and build incredibly complex mechanical engines, engineering biological systems, such as cells, remains a challenge. However, recent technological improvements, notably in biochemistry, recently enabled the construction of databases with millions of quantitative cell activity measures, covering multiple diseases.
The DeepLife team develops a SaaS platform combining state-of-the-art deep learning algorithms on multi-omics sequencing data, public and private cell atlases, and proprietary deep learning-based cell engineering tools. Our goal is to supplement in vitro testing with in silico simulations and rapidly discover molecular triggers to efficiently engineer cell behavior.
We are working closely with biotech and pharmaceutical companies to accelerate their target identification processes. We are also collaborating actively with the academic community by publishing papers and providing open access to our platform’s features and data.
Drug Discovery, Pharmaceutical, Deep Learning, and Biotech -
Original language
AI for digital twin of cells
DeepLife is an early stage startup founded in 2019 by Jonathan Baptista and Jean-Baptiste Morlot, backed by international investors, developing the first SaaS platform for Digital Twin of cells.
While we are able to design and build incredibly complex mechanical engines, engineering biological systems, such as cells, remains a challenge. However, recent technological improvements, notably in biochemistry, recently enabled the construction of databases with millions of quantitative cell activity measures, covering multiple diseases.
The DeepLife team develops a SaaS platform combining state-of-the-art deep learning algorithms on multi-omics sequencing data, public and private cell atlases, and proprietary deep learning-based cell engineering tools. Our goal is to supplement in vitro testing with in silico simulations and rapidly discover molecular triggers to efficiently engineer cell behavior.
We are working closely with biotech and pharmaceutical companies to accelerate their target identification processes. We are also collaborating actively with the academic community by publishing papers and providing open access to our platform’s features and data.
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DeepLife | Digital Twin of Cells for Drug Discovery
At DeepLife, we harness deep learning on multi-omics data to model cells and efficiently engineer their behavior
https://www.deeplife.co
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