Chris Moxham Co-founded Transcripta Bio Scores $24M in Funding

The round saw the Backers Mayo Clinic, Omnimed, JAZZ Venture Partners, BlueYard Capital, and a group of life sciences family offices.

The company will use the funding to expand its operations and speed up product development.

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Transcripta Bio has developed a drug discovery platform that matches disease-related gene activity with drugs that may reverse those changes. It uses gene data from real patient samples to identify promising treatment options.

The platform combines a Disease Signature Atlas built from patient single-cell RNA sequencing data, a Drug-Gene Atlas that measures how approved and experimental drugs affect genes, and Conductor AI, a machine learning system trained on more than one billion gene responses.

Together these technologies help researchers evaluate drug candidates more accurately before they enter clinical trials. The goal is to reduce the risk of drug failure by identifying the most promising treatments at an early stage.

"Transcripta was founded to address the failure points that keep drugs from reaching the clinic. Our platform has already demonstrated results and this capital accelerates that work for diseases that have waited long enough for real treatments," said Chris Moxham, founder and CEO, Transcripta Bio. "Transcripta Bio is positioned to demonstrate what AI-native drug discovery can deliver for patients with neurological and neuromuscular diseases."

"Transcripta Bio has developed a discovery platform grounded in patient biology and applied it across four programs with significant unmet need," said Greg Wood, Managing Director, Omnimed. "We look forward to seeing what they can accomplish moving forward."

About Transcripta Bio

Founded in 2020 by Christopher Moxham, Transcripta Bio is an AI-powered drug discovery company focused on developing new treatments for neurological and neuromuscular diseases. Its platform analyzes gene activity in patients and matches it with drug candidates that may reverse disease related changes. This approach helps identify promising treatments faster, and makes the drug development process more efficient.

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