We have a wide variety of solid tumors that we are going for, lung cancer, ovarian cancer, and pancreatic cancer. That's something that we are really focused on.
And tell me a little bit about the partnership you have with Eli Lilly. What are you creating there when it comes to cancers?
The collaboration is mainly centered around targeted radiotherapy for treating solid tumors, and it's a multi-target research collaboration. Lila Biologics is responsible for giving Lilly a development candidate, which is basically an optimized drug molecule that is ready for FDA filing. Lilly will take over after we give them the optimized molecule for the clinical development and taking those molecules through clinical trials.
Why use AI for this? What edge is that giving you, or what opportunities does it have that human intelligence can't accomplish?
In the last couple of years, artificial intelligence has fundamentally changed how we actually design proteins. For example, in last five years, the success rate of designing protein in the computer has gone from around one to 2% to 10% or more. With that unprecedented success rate, we do believe we can bring a lot of drugs needed for the patients, especially for cancer and cardiovascular diseases.
In general, drug design is a very, very difficult problem, and it has really, really high failure rates. So, for example, 90% of the drugs that actually enter the clinic actually fail, mainly due to you cannot make them in scale, or some toxicity issues. When we first started Lila, we thought we can take a holistic approach, where we can actually include some of this downstream risk in the computational design part. So, we asked, can machine learning help us designing proteins that scale well? Meaning, can we make them in large scale, or they're stable on the benchtop for months, so we don't face those costly downstream failures? And so far, it's looking really promising.