[ad_1]
JD: It doesn’t really make sense to me, [but] I’m pleased that we have our 45 issued patents, our 40 pending patents, all in the US. And our 30 European patents are unaffected by the ruling. And honestly, look, I’m carrying on with my research.
AR: I always thought the origin of the patent fight was not about money. My own reading of why it was so strongly fought was that it was not over commercial control but over credit—who did the science—and the truth.
JD: That’s your speculation. It’s hard to say, isn’t it? I don’t know what others’ motivations may have been, Obviously, it will be appealed. Obviously, we don’t agree with the decision. And obviously, 30 countries and the Nobel Prize Committee also don’t agree, if you’re talking about who invented what, at first.
AR: What does it tell you about how the patent system operates that there could be one person accepting a Nobel Prize but then the patent going elsewhere? Should that make sense to people?
JD: It doesn’t really make sense to me. I don’t know if it makes sense to other people. In the scientific community I don’t think there’s much question about what happened.
AR: You have been the subject of a book by Walter Isaacson, who also wrote biographies of Steve Jobs and Leonardo da Vinci. What was it like to participate in your biography?
JD: Humbling and a little bit terrifying, if I’m honest. Although I have to say I felt fortunate that someone as talented as Walter was interested in the story, because he’s a wonderful writer. He did a great job of trying to capture the feeling we all had of being part of this incredible transformation that happened with CRISPR.
AR: You recently became the chief science advisor to a Wall Street firm called Sixth Street. What are you planning to do there, and why did you take on that role?
JD: I’m excited that at Sixth Street we can identify the right teams, the right opportunities, the right openings where financing could really accelerate the science and the business opportunities. One area where I think there’s a lot of potential is using machine learning to analyze data that are coming out of CRISPR. We know that one of the important opportunities with CRISPR in the future is to understand genomics, meaning the function of genes. And frankly not individual genes but whole sets of genes and pathways and different cell types comprehensively. The types of data that come from those efforts clearly contain a huge amount of information, much of it subtle. And so using machine-learning algorithms to mine those kinds of data sets, I think, will be very powerful. You could imagine using that type of strategy to understand the genetics of disease—our individual susceptibilities—and to identify new therapeutics.
AR: I always think of you as sort of a scientist’s scientist. I once saw a picture of you leaning over a student’s shoulder, and that’s who you are in my mind. But this is asking you to do something a little bit different. Why do you think you could be good at picking technologies for commercial investment, as opposed to the most intriguing scientific questions?
JD: I love science, and my best days are when I am leaning over a student in the lab looking at data. But I’ve come to appreciate that for CRISPR to have an impact in the next decade is going to require real capitalization of the right teams.
AR: A survey in Harvard Business Review found that only 2.3% of VC money was going to startups led by women. Were you shocked to learn that?
Source link