Our mission is to create better drugs, faster
for the most challenging diseases of our generation.
Technology has revolutionized nearly every aspect of our lives. Yet, in biopharma, the cost of developing a drug continues to multiply. Despite decades of work from millions of incredible scientists, deciphering biological complexity remains one of the greatest challenges and largest opportunities for humanity.
The molecular biology revolution of the 1950s resulted in the birth of modern biotech, forging companies like Genentech, Amgen, and Biogen. Today, technological advances in genomics, machine learning, and engineering have created a new opportunity to build the next enduring biotech companies of our generation. Verge integrates multiple technological innovations across biology, chemistry, translational sciences, and engineering to create medicines more efficiently and with an improved probability of success.
The CONVERGE® Platform
Our end-to-end CONVERGE® discovery platform is a closed-loop machine learning system combining industry-leading proprietary human genomics with advanced computational tools to predict new drugs with a higher probability of clinical success.
Our platform combines human-centric biology and chemistry to create a virtuous cycle of learning and a novel, proprietary pipeline of compounds that are developed towards the clinic.
Human Input Data
61.7 TB
Human Gene Expression
4,789
Gene Perturbations
5,524
ChIP-seq studies
> 2 million
Protein-protein interaction
61.7 TB
Inferred relationships between genes
Biological Validation Data
21 TB
Cell Imaging Data
47 TB
Cell ‘Omics Data
> 1 million
Physiological measurements
Clinical Validation Data
Healthy volunteer
2,100 patient samples
ALS patient
2,179 patient samples
PD patient
595 samples
FTD Patient
225 samples
Data, Direct-From-Human
Animal and cell models are poor predictors of whether drugs work in humans. We use human brain tissue sourced directly from patients, rather than animal or cell approximations. When combined with genetics, this reveals targets that are 3X more likely to succeed in the clinic.
End-to-End Infrastructure
We validate our in silico predictions in our wet laboratories, creating a mutually reinforcing cycle of learning with our machine learning platform.
Targeted Predictions
We use machine learning to predict high-confidence targets and drugs, thereby eliminating the need for brute force screening.
Success
We are one of the first AI-enabled drug discovery companies to discover a novel target and internally develop a proprietary clinical candidate entirely using our proprietary platform. We achieved this in just four years.
For greater probability of success in humans, we must start with humans.
