In this Q/A session with Sam Cooper, co-founder and CEO of portfolio company Phenomic AI, Sam shares his experiences as a tech rebel in the field of biotechnology. Phenomic AI is developing deep learning solutions that seamlessly integrate AI with experiments to accelerate drug discovery.
What is your personal mission that led you to join Phenomic AI as a co-founder?
I’ve always had an interest in math and technology, and then I studied biochemistry for my undergraduate degree. After graduating, I began a Ph.D. at the Institute of Cancer Research where the lab was using automated microscopy to capture millions of images of cells as a way understand how drugs affect these cells and could be used to treat diseases. My role in the lab was to develop AI software for analyzing the enormous number of images being processed. Around the same time, my co-founder Oren Kraus was developing deep-learning tools for the same task at the University of Toronto. We realized that there was an opportunity to use the tools we were working with to accelerate drug discovery and decided to team up to start Phenomic AI.
How do you see the industry as being ripe for disruption today?
I wouldn’t say that we’re really disrupting an industry. What we’re doing is pioneering the foundation of a new industry. If you look at drug discovery, traditionally there have been two ways of finding drugs. One is called target based, where you have a specific protein or chemical target that is known to cause a disease and a drug is designed around this specific target. In the second, you create experimental models of the disease that you then test thousands of compounds against to determine which drugs have the desired effect on the disease.
Currently, people have only ever found biologics, or antibodies, through targeted approaches. The phenotypic screening model has never been tested with antibodies, so that’s what we’re doing. The first step in this approach is to build a phenotypic library. Once we have a library of antibodies that can be used for screening, this will allow the entire industry to start developing drugs through phenotypic approaches. We are going after antibody therapies to find the next blockbuster drug for some of the biggest diseases out there.
What has been your biggest success and your greatest learning as an entrepreneur?
The biggest success has been showing that our model can massively cut the time required to analyze data coming from the drug discovery process. The current process of identifying what you are looking for, developing a specialized data analysis program, and testing everything against it is very specific and time-consuming. We’ve shown that by using AI, we can reduce the time required to analyze data from weeks down to hours. This will enable us to build our antibody library.
I would say my biggest challenge has been learning about the business side of things. We’re a highly technical company, and we have a complex message. It’s an ongoing learning process to be able to tell a meaningful story that is based on complex algorithms and technical problems.
There are two types of entrepreneurial founders. There are those people who become entrepreneurs because they want to build a company and then go searching for a technology. We’re the other type; we came from a background where we had a technology that we knew could have a huge effect on drug discovery. We wanted to determine the most impactful use for that technology and building a company was the best way to find the answer. My advice for those deciding between research in academia or going commercial would be to go commercial. This gives you the opportunity for capital funding which makes things move faster and is a lot more exciting.
Why did you choose to partner with Hemi?
We chose to work with Hemi because of their activity in the life sciences space. They have great connections and are well regarded by other investors. Hemi is currently helping us by providing access to potential pharmaceutical partners and investors focused around series-A funding.
What would people be surprised to learn about you?
I’m very active in my spare time. I used to race in windsurfing competitions, and also worked abroad as a sailing and windsurfing instructor.
Looking forward, what upcoming products, plans or geographic expansions are in the pipeline?
What we’re doing at the moment is demonstrating that we can build an antibody library that will deliver good therapeutics. Our goal is to calculate how much the library will cost and then raise funds to build it. The next step would be to commercialize it by selling partnerships and using it ourselves to find new drugs. It’s going to take a few years, but once we get there, the potential is unlimited.