A startup formed based on technology developed at Rutgers, The State University of New Jersey, looks to utilize artificial intelligence (AI) and automation to help researchers advance their work and make breakthrough discoveries. Plexymer was created at the Gormley Lab by Rutgers associate professor of Biomedical Engineering Adam Gormley, PhD, and his former student Matthew Tamasi, PhD, who now serves as Chief Technology Officer of Plexymer.
This technology utilizes automated combinatorial polymer chemistry and machine learning (ML) to develop a streamlined pipeline for data-driven polymer excipient design. It is based on the notion of self-driving cars, except it uses laboratories.
Polymers are large molecules used as the basis for many organic and synthetic materials, including therapeutics. According to Gormley, pharmaceutical and consumer products use a standard set of polymers for their applications, no matter how complex the application. Gormley believes custom-designed polymers would work better. But the challenge is how to discover those custom polymers. This is where Plexymer comes in.
Plexymer’s goal is to serve as a contract research organization for companies, organizations, or even researchers for whom AI and robotics may be useful to their discovery pipeline. And the company’s name stems from multiplexing (evaluating multiple experimental elements simultaneously) and ’polymer’. And the benefit of using Plexymer is that artificial intelligence cannot be distracted from its work.
After developing the technology in the lab, Gormley and Tamasi took part in the Rutgers NSF I-Corps program to learn more about the feasibility of their business model. Following this experience, the two fully explored the idea of what would become Plexymer through the national NSF I-Corps program – which provided the confidence that there was a solid business opportunity for the technology.
KEY QUOTES:
“Currently in labs, a scientist follows the conventional scientific method where they come up with a question, develop a hypothesis, design an experiment to test that hypothesis, implement the experiment, analyze the results, and then update their hypotheses. The question we had was, is that the most effective utilization of a scientist’s time? We thought that maybe outsourcing some of the work such as designing experiments, performing the experiments, and then doing routine analyses may ideally be suited for an automated system, an AI model. Then, the AI model could predict the next best experiment to answer fundamental questions or design material problems.”
“My lab is incredibly excited by the potential of self-driving labs, and we are neatly in the domain of applying these ideas into the fields of biomaterials, drug delivery, and polymer chemistry.”
“There are lots of things that complicate the schedule of a benchtop scientist on a day-to-day basis, such as meetings, other priorities, and bias. AI and automation, on the other hand, do not have any of those issues, especially if you make the process as autonomous as possible. It can potentially significantly improve not just the effectiveness but also the efficiency of the discovery process.”
“The challenge of companies that have platform technology like ours is that in essence we are a hammer looking for nails. We knew we had a good hammer; we just weren’t exactly sure what nails were out there, or even what were the best nails to hammer. Our experience with the regional and national I-Corps programs helped us understand the different problems people were facing and the markets of entry that we felt had the highest level of enthusiasm and viability as a commercial service.”
“I cannot express more gratitude and praise for the Office for Research. They are really excellent; they are engaged, they act as mentors, and they care deeply about making companies such as Plexymer successful.”
- Adam Gormley
“Drs. Gormley and Tamasi’s technology has the potential to advance important research in an expedient and efficient manner. We are proud to have supported their work, and we look forward to seeing what they can accomplish.”
- Deborah Perez Fernandez, PhD, MBA, executive director of Rutgers Technology Transfer, which negotiated the exclusive license with Plexymer
“Artificial intelligence and machine learning are both the future and the present, and Plexymer can be at the forefront of blending them with scientific research. Our team will continue to provide support to Plexymer to help it grow in size and scope.”
• ⁃ Vince Smeraglia, JD, executive director of New Ventures
