NCI Awarded Envisagenics $2 Million Biotech Grant
The National Cancer Institute issued the grant to support Envisagenics in developing its new machine learning algorithm.
Updated on Oct. 13, 2026 in Biotech
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The National Cancer Institute awarded a $2 million Direct to Phase II Small Business Innovation Research grant to the New York-based firm Envisagenics. The funding supports the advancement of the SpliceDisco machine learning algorithm over the next two years.
Why it matters
This grant aims to accelerate the discovery of first-in-class cancer therapeutics by combining artificial intelligence with large-scale transcriptomic analysis. It specifically supports the development and commercialization of the proprietary SpliceCore platform and the new SpliceDisco tool.
The project leverages 14 million distinct RNA splicing events within a proprietary resource. The work integrates this data with the SpliceCore platform, an AI-driven software designed for transcriptomic analysis.
The players
Envisagenics
An AI-driven company based in New York that focuses on RNA splicing therapeutics.
National Cancer Institute
The federal agency responsible for cancer research and training that awarded the research grant.
The details
Envisagenics will use the capital to integrate AI-driven target prioritization with experimental validation processes. This combination is intended to streamline the identification of new drug candidates for treating various forms of cancer.
Timeline
October 13, 2026: Envisagenics announced the receipt of the grant award.
Two-year duration: The timeline for the project development and commercialization.
The Tech Race
The infusion of capital into AI-led drug discovery represents a broader industry shift toward computational biology to solve complex genetic targets. This move positions Envisagenics to compete with legacy pharmaceutical methods that traditionally rely on longer, manual trial-and-error processes.
While this grant supports early-stage research, the ultimate goal is to produce novel, more effective cancer treatments that could reach the clinical pipeline. Patients and the broader healthcare sector may eventually see improved drug efficacy if the algorithms successfully identify viable therapies.
The takeaway
The use of machine learning in RNA splicing demonstrates how computational tools are increasingly vital for navigating massive biological datasets. Implementing these technologies at the research level can potentially reduce the time and cost required to move cancer drugs from discovery to testing.
Further reading
Learn more about the latest industry trends in the Biotech section.
Source note: This article includes information reported by The Manila times.
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