Verge Genomics Scores $32 Million for AI-Driven Drug Discovery

July 16, 2018

Verge Genomics, a drug discovery company utilizing machine learning to develop new therapeutics, announced today that it has raised $32 million in Series A financing led by DFJ. New biotech investors WuXi AppTec’s Corporate Venture Fund, ALS Investment Fund, Agent Capital, and OS Fund also participated in the round. The oversubscribed round brings Verge’s total funding raised to-date to more than $36 million. With this round, Emily Melton of DFJ will join Verge Genomics’ Board of Directors.

“By vertically integrating machine learning and drug development capabilities under one roof, we have been able to quickly identify dozens of promising new therapeutic opportunities for devastating diseases such as ALS and Parkinson’s Disease,” said Alice Zhang, Co-founder and CEO.

Founded in 2015, Verge intersects machine learning, neuroscience, and experimental biology to accelerate drug discovery. The Company has developed lead therapeutic programs in ALS and Parkinson’s disease at breakneck pace while establishing partnerships with two pharmaceutical companies. Verge also invested heavily in the creation of its proprietary datasets, generating one of the field’s largest and most comprehensive databases of ALS and Parkinson’s Disease patient genomic data through partnerships with a dozen top-tier academic and government organizations.

The company is also developing therapies for other neurodegenerative diseases.

“Verge has built one of the most comprehensive datasets that we have worked with for disease understanding, machine learning, and artificial intelligence. Importantly, Verge’s integrated approach to drug development aligns patient data with animal models to ensure the use of appropriate translational research,” said Mr. Edward Hu, Chief Financial Officer and Chief Investment Officer of WuXi AppTec. “With our investment in Verge, we continue to strategically build multiple relationships in the area of artificial intelligence and machine learning applying to drug discovery.”

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