Immunai, a New York-based biotech startup, has raised USD 125 million in a Series B funding round led by Koch Disruptive Technologies on 27 October 2021.
Investor: Koch Disruptive Technologies led the latest funding round with participation from Talos VC, 8VC, Alexandria Venture Investments, Piedmont, ICON and others, bringing the company’s total funding to USD 295 million.
The Objectives behind the funding: The company plans to deploy the current proceeds to bring in more employees and keep enriching the immunological data set (and back-end infrastructure that can support it) at its disposal.
Founded in 2019 by Ansuman Satpathy, Dan Littman, Danny Wells, Luis F Voloch, and Noam Solomon, Immunai is a biotechnology company that combines single-cell genomics with ML algorithms to enable high-resolution profiling of the immune system. Immunai aims to map the entire immune system and its functions using single-cell genomics and machine learning. Immunai leverages single-cell technologies to profile cells from a blood sample and utilises machine-learning algorithms (powered by its proprietary database) to map the hundreds of cell types and their states to create an immune profile. They work on biomarker discovery and insights that identify how a cell responds to its changing environment. The startup is located in New York City, San Francisco, and Tel Aviv, Israel.
Immunai claims its data set, called the Annotated Multi-omic Immune Cell Atlas, AMICA, is the largest globally. In the past, the startup has acquired companies like Dropprint and Nebion. They also had about 70 external partnerships with hospitals and institutions.
What the Founder has to say:
Noam Solomon, Immunai’s co-founder and CEO, said, “Probably a year ago we were showing strong correlative data — that certain insights we have can explain relationships between certain genes and cells,” he says. “Today, we have more causal inference results. We are able to show that things we are doing with our functional genomic platform are actually causing certain results.”
“I think there are very few companies in the space that are trying to do more than create a small data set and apply sophisticated machine learning tools,” he added. “Our approach is the opposite. We believe we need to build a robust database that we will be able to feed and grow, with the data engineering tools to make sure that our algorithms can run on 100,000 samples.”
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