As I mentioned in my previous post, our CEO, Andrew Satz, recently spoked at “Alumni in Conversation,” hosted by Columbia University School of General Studies. During the discussion, Andrew explained how EVQLV’s algorithm models the drug discovery process. See below.
As Andrew explains, the traditional way of discovering antibodies involves injecting an animal with a disease or a virus. The animal is then going to have an immune response, producing antibodies to fight the disease. You then have an animal antibody that you now need to convert into a human antibody. This process takes roughly 4-5 years and cost over half a billion dollars.
At EVQLV, we model that entire process in the computer. In the above example, the process by which the animal is forming its immune response is evolutionary. Our algorithm models this evolutionary process in the computer, allowing us to generate millions of antibodies at a fraction of the cost and time it takes to do so in the lab. After we’ve generated our algorithms, we screen them so they’re less likely to have failure in animals or in humans. In short, we’re moving from drug discovery to computational drug design.