Why Machine Learning and Artificial Intelligence Are Transforming Pharmaceutical R&D Collaborations
By Jane Rhodes, PhD, MBA, Chief Business Officer
The drug discovery and development industry is undergoing a fundamental transformation. With increasing affordability and scale, we are now able to generate and analyze massive quantities of relevant data to address highly complex biological and chemical questions. This is the type of data analysis at scale which has historically been the mainstay of the technology industry.
Today, biotechnology companies are using computational methods such as artificial intelligence (AI) and machine learning (ML) to accelerate drug discovery and increase the probability of technical success of making effective drugs for the benefit of patients. The world’s largest pharmaceutical companies are taking note and embracing this innovation. Their steadily increasing investments, in the form of equity and partnerships with technology-enabled biotech companies, like Verge, is a signal that the use of AI/ML for drug discovery is becoming broadly adopted. As of March 2023, investments in AI for drug development totaled $60.2 billion.1
Biology is complex, and a major obstacle in developing effective therapies for many diseases is the lack of predictive cell or animal models. We believe that to succeed in humans, you have to start in humans. And to address this opportunity, we developed CONVERGE™, our full-stack platform that combines industry-leading human tissue datasets with computational analytics, including machine learning, to find new targets which we believe will have a higher probability of clinical success. We’ve used CONVERGE™ to advance a drug development program for a target for amyotrophic laterals sclerosis (ALS), a disease with incredibly high unmet need, and we advanced this program from discovery to clinical candidate in less than 5 years.
We’ve scaled CONVERGE™ to diversify our own pipeline and apply it more broadly in the context of partnerships. Because our platform is disease agnostic, we have been able to extend our target discovery efforts across a variety of complex diseases.
We recently announced a new collaboration agreement with Alexion AstraZeneca. It’s an exciting opportunity for Verge because it
Combines the power of CONVERGE™ with Alexion’s expertise in developing and commercializing rare disease treatments,
Expands Verge’s discovery efforts to identify and validate therapeutic targets in rare neurodegenerative and neuromuscular diseases,
Builds on Verge’s achievements in CNS drug discovery, and
Further validates the CONVERGE™ platform.
This follows our collaboration with Eli Lilly to discover and validate up to four new targets for amyotrophic lateral sclerosis (ALS) which Lilly can advance through clinical development and commercialization.
Our partners Alexion AstraZeneca and Eli Lilly, along with Merck Global Health Innovation Fund, have also made equity investments in Verge. Investment by three of the ten largest biopharma companies in the world illustrates a strong belief in the potential of technology-enabled drug discovery, and specifically our all-in-human, AI-powered drug discovery platform, to significantly increase the probability of technical success and reduce the time and cost it takes to bring a drug to market.
As pharma continues to look outside its own walls for innovation, companies like Verge can bridge the technology gap to support drug discovery efforts and create new medicines. We are thankful for the opportunity to work with and learn from the world’s leading pharmaceutical companies that are investing in human data and advanced computing to solve some of our toughest, most complex diseases.
References
1. Deep Pharma Intelligence. “Artificial Intelligence for Drug Discovery Landscape Overview, Q1 2023.”