JD: It doesn’t really make sense to me, [but] I’m glad 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 frankly, I’m continuing my research.
AR: I always thought that the origin of the patent battle was not about money. My own reading of why it was so strongly fought against was that it was not about commercial control, it was about 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 the motives of others have been. Of course there will be an appeal against it. Obviously we do not agree with the decision. And of course, 30 countries and the Nobel Prize Committee also disagree, when you talk about who invented what, in the beginning.
AR: What does it tell you about how the patent system works that one person can accept a Nobel Prize, but then the patent goes elsewhere? Does that have to make sense to people?
JD: I don’t think it really makes sense. I don’t know if it makes sense to other people. In the scientific community, I don’t think there’s much doubt 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 working on your biography?
JD: Humble and a little scary, if I’m honest. Although I have to say I felt lucky that someone as talented as Walter was interested in the story because he’s a great writer. He did an amazing 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 scientific advisor to a Wall Street firm called Sixth Street. What will you be doing 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 funding could really accelerate the science and the business opportunity. One area where I think there’s a lot of potential is using machine learning to analyze data coming out of CRISPR. We know that one of the important opportunities with CRISPR in the future is to understand genomics, that is, the function of genes. And frankly not individual genes, but complete sets of genes and pathways and different cell types. The types of data that come out of those efforts clearly contain a tremendous amount of information, much of it subtle. And so I think using machine learning algorithms to mine those kinds of datasets is going to be very powerful. You might imagine using that type of strategy to understand the genetics of disease — our individual susceptibilities — and to identify new therapies.
AR: I always see you as a kind of 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 prompts you to do something different. Why do you think you could be good at choosing technologies for commercial investments, as opposed to the most intriguing scientific questions?
JD: I love science, and my best days are when I’m leaning over a student in the lab and looking at data. But I’ve come to realize that for CRISPR to have an impact over the next decade, it takes real use of the right teams.
AR: A study in Harvard Business Review found that: only 2.3% of VC money went to female-led startups†Were you shocked when you heard that?