Welcome to The Long Run. This is a podcast for biotech adventurers. I'm your host, Luke Timberman.
Today's guest is Daphne Koller. Daphne is the CEO of South San Francisco-based Incitro. The company is seeking to develop a new platform for drug discovery that leans on a combination of wet labs and machine learning algorithms to spot new biological targets for drug discovery.
Artificial intelligence and machine learning has been stirring imaginations in biopharma and generating more than a little hype for a few years. Smart people who know how complex drug discovery is are pining for something to make it less risky, less time-consuming, and less expensive. And Incitro represents one of the prominent wagers in this AI-for-drug-discovery category, as it raised a $143 million Series B financing in May 2020 led by Andreessen Horowitz, and which included earlier investors like Arch Venture Partners, Foresight Capital, GV, and Third Rock Ventures.
Daphne comes to this challenge with a fresh set of eyes. She was a math prodigy as a kid. She became a tenured professor of computer science at Stanford University. Then she became a successful entrepreneur, co-founding Coursera, the online learning platform. But when it came time for her to find challenging new problems to solve intellectually and important problems to solve for humanity, she decided that tackling disease was the place to be for her next act.
We talked about Daphne's journey and some of the key aspects of her strategy to make Incitro succeed in this new frontier.
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Now, please join me and Daphne Koller on The Long Run.
Daphne Koller, welcome to The Long Run.
So, Daphne, I'm going to throw a surprise question at you. It's kind of a nerdy one about terms. Machine learning, artificial intelligence — people throw these terms around a lot, interchangeably, in different settings. You describe what you're doing at Incitro as machine learning for drug discovery quite often. How do you think of that term, and why do you use that term as opposed to just AI for drug discovery?
I think it's a great question, and my view on this is not necessarily representative of that of everyone. Some people will tell you that machine learning is a subset of artificial intelligence, and I actually view it quite differently. I think artificial intelligence is the task of recreating intelligence as we know it in people, whereas machine learning is methodologies that learn to perform complex tasks by learning from data.
Now, it turns out that the right solution for many AI problems is by employing machine learning. But there are AI problems that don't involve machine learning. And maybe more relevant to what we're doing is that the problems that we're solving are not generally ones that people actually solve well. So we're creating, in some sense, a different type of intelligence — not what humans do, but in many cases tasks that humans cannot do.
Okay. Okay. I'm going to come back to this. That's a good working definition for starters. Okay. So, as you know with this show, I like to start out with some of the personal background — like how you came to this place where you could begin to think about these hard problems with machine learning for drug discovery. So, I know you were born in Jerusalem, grew up in Israel. Can you tell me a little bit about your experience growing up there?
Absolutely. So I had always found school to be a somewhat less-than-scintillating experience, at least at the schools that I got to go to. And so I ended up at a relatively early age time-sharing between time at school and time in college, so that I ended up finishing almost contemporaneously. So I finished high school at 16 and college at 17, and had always been interested in untangling, disentangling complexity.
Well, Daphne, let me back up just a second before we get there. Growing up in — I mean, you're something of a child prodigy. And math was kind of your first love, right?
Yes, because it was so conceptually elegant.
Uh-huh. Uh-huh. How did you — what did your parents do for a living, and how did they nurture this, you know, budding intellect?
So my parents were both sort of academics. My father was a professor — actually a professor of biology, as it happens, which is one of the reasons why I steered as far away from biology as I could as a child, because kids always want to do something different from what their parents did. My mom was an English teacher, and they, I think, didn't quite know what to make of me.
So when I asked for math books as gifts as a child, then my father went out and bought them. But it wasn't like they pushed me, which is what a lot of people think — that somehow you ended up doing this kind of weird trajectory that I had because the parents pushed you. That was definitely, from my perspective, a pull rather than a push. And I was actually the one who came to my father when I was 13 and said, 'Hey, Dad, I want to go to college in parallel with high school.' And he said no. And I said, 'No, really, please.' And so he helped facilitate an introduction, but everything else was just stuff that, you know, I just drove that process.
Well, I mean, you don't usually want to send your 13-year-old to college, especially when in Israel a lot of the — most of the college students are actually post-military service. So your average college student in Israel is in their mid-20s. So I was way younger than everybody else. A real social — I mean, misfit in a way.
I mean, well, I, you know, I don't want to say misfit, because that suggests sort of some kind of dysfunctional relationship. And I think it was kind of like I was the kid, and people kind of cherished me as almost like a mascot, which is, you know, which was fine. And I had a good time with that. And I was, you know, I was actually a teaching assistant — in fact, a teaching fellow — when I was 17, teaching a class of mostly people in their late 20s in an advanced database class. And that was fine, and everyone treated me with respect. So I don't think it was dysfunctional. So I didn't mean like misfit in that sort of sense. It's just like you're not with your peer group, and that socialization part is, you know, so important to schooling, especially at that age.
Yeah. So there were some people who — Israel has this program where some 18-year-olds actually get to attend college pre-military service, and I mostly hung around with those people. And so if I was 16 and they were 18, it wasn't that big of a gap.
Okay. So you enjoy math. You're seeking out these challenges. Did you encounter a teacher who, like, really helped inspire you or lead you down that path?
I had a lot of really great teachers, but I think the more inspiring interactions that I had actually came later on when I was at Stanford doing my PhD, in terms of people who really inspired my path — my PhD and postdoc specifically.
Okay. Okay. We'll get there. So now you graduate from college at — was it 18? You said...
My bachelor's degree was at 17 and my master's at 18.
Okay. And this is at Hebrew University?
And then did you go serve in the — perform your Israeli military service?
I did. I was a military intelligence analyst. So it was actually kind of fun because I got to — it was my first encounter with Bayesian reasoning. How do you take lots of little pieces of evidence and put them together to create a bigger picture that makes sense? And it was actually a really stimulating intellectual exercise, very different from anything that I've done before, but helped significantly reinforce my love of untangling complexity.
Interesting. So did you — you said intelligence — did you work on, like, signals, kind of like what we think of at the — like the NSA here?
I shouldn't probably talk about my military service, but I will just say that I was an intel analyst in the sense you got lots of different pieces of information from different sources and kind of said, 'Okay, this is one tiny little shred of evidence without context that really doesn't make sense, and here's another one, and here's a third. How do you put them together and discard the stuff that doesn't fit and create a picture that then provides actionable insights to people who need to make decisions?'
Okay. Okay. So this would have been something like what — the late '80s?
That was in the mid-'80s.
Okay. Okay. And how long did you serve there?
Three years. So I was an intel officer. So as an officer, you end up serving longer than the stipulated two years. I ended up serving closer to three.
Was there any other, like, important aspect to shaping your career doing that kind of — you know, that service work, where you knew, like, eventually you're going to go to, you know, get a PhD or, you know, pursue some kind of career other than this? But I mean, we talk about this in the US, right — this idea of, like, should there be compulsory service, and there's some arguments for it and against it — but what did it mean to you?
Well, for one thing, it was a very maturing experience, because the Israeli military — because it is not just a volunteer army; everyone gets conscripted and has to do the service. First of all, you get to encounter people from all walks of life. And for those of us like myself who were privileged to have a more sheltered upbringing with, you know, a school that was relatively homogeneous from a cultural perspective, it was really interesting to just have that exposure.
The other piece is that because everyone joins the service approximately when they're 18, a lot of young people end up having relatively quickly roles of significant responsibility. And while I wasn't like, you know, at the top of the heap by any stretch, I did get to kind of have a little, you know, at the age of whatever, 19 or 20, this little unit that I was in charge of. And so that's not something that most college students in the US, for instance, get to do. And so I found it to be incredibly interesting, both in terms of just doing something that was entirely non-academic, unlike the work I'd done at school and university, and also just in terms of the exposure to a different mindset — both the leadership and the diversity of the people I worked with.
Interesting. So I saw on something you had written down that you came to the US July 4th of 1989, a rather memorable date. How did that come about?
Just by chance. I mean, I was actually with my then-boyfriend, ultimately husband, and we were supposed to come over together. We had this beautifully planned vacation that we were going to take in Europe after we both finished the service — was supposed to be three months — and then I was going to come and do my PhD, and he had a job lined up. And about two weeks before that was supposed to happen, his future boss said — actually, we need you — called and said, 'We need you here now.' So basically we both finished the service and we got on a plane the next day, and it happened to be July 4th.
And this was to go to Berkeley? Stanford?
Oh, Stanford. Okay. So Berkeley was my postdoc.
Okay. Okay. So what were you there to study at Stanford?
Computer science, straight up, straight away. PhD in computer science.
My undergrad was in math and computer science, so that was the natural continuation.
And what kinds of problems were you interested in then, in, you know, late '89 or the early '90s?
So I started out — and I think I mentioned this earlier — being really enamored by the elegance of mathematical solutions. And so at the time I was trying to think about how should people make decisions and how do we create decision-making environments that allow them to make better decisions. So I studied and worked on tools first from game theory related to multi-agent systems, and then also in single-agent decision-making using tools from probability theory and decision theory.
And it really was at that point, for me, in a way that I feel a little bit almost embarrassed to think about right now, just how elegant a conceptual framework I could put together with sound, strong mathematical principles. And so I ended up doing some work on both of these fronts — both decision-making in multi-agent systems and decision-making in single-agent systems — in a way that when I now look at it, it's very elegant perhaps, but it's not really good for much.
What was the discussion around the Bay Area, the field around artificial intelligence in those days?
So this was right around the time of one of the earlier AI winters. So AI has gone through peaks and troughs multiple times in its history, generally caused by the fact that certain groups of people made hyperbolic promises about what artificial intelligence would be able to accomplish in three years, five years. And those promises at that point didn't really materialize. And the late '80s, which is when I started my PhD, was just coming off the tail end of one of the boom periods, which was the mid-'80s, and then getting into one of the trough periods.
And the truth of the matter is, I didn't perceive what I was doing at the time as artificial intelligence. Artificial intelligence was at that point, in many ways, a somewhat squishy discipline. It was kind of like, we're going to put rules upon rules and then some kind of behavior is going to emerge. And those systems were oftentimes quite brittle, very difficult to engineer. Unexpected behaviors would just emerge in ways that were totally hard to correct for. So what I was doing was really more mathematical modeling. And so people will often ask me, 'How did you get into artificial intelligence?' And my response is, I didn't move into artificial intelligence. Artificial intelligence actually moved to encompass what I was doing.
It sounds like you were comfortable with the predictive ability of what you were doing.
So at the time — this was right around, you know, the tail end of my PhD was in the early '90s, '92-ish or so. And at that point I was really starting to get into machine learning because of the realization that the systems that one needed to build in order to make intelligent decisions were really hard to engineer well. And the only way that one could really circumvent what was then called the knowledge engineering gap or challenge was to not have to engineer the system, but rather have it learn for itself from data what the model ought to be. And so that's when I started to really get into machine learning as a discipline. So I've been working in that space, I would say, since the early '90s.
I know a lot of people now think that machine learning is a relatively recent phenomenon and it's something that has just recently emerged. But no, it really sort of took off as a discipline within computer science and engineering in the early '90s. I mean, statistics had existed long before then, but it was very different, and the two fields kind of merged. Statistics became more predictive, and machine learning took off within computer science. And I think the two together, plus ideas from neuroscience and optimization — those are really, it's the confluence of those that gave us what we think of as modern-day machine learning.
Interesting. So you said you moved on to Berkeley for your postdoc. Is that when you started looking around biology, you know, that field that your dad had done and you had no interest in as a kid?
No, that actually happened later. But I will say one of the things that happened after I returned to Stanford as a faculty member — and I can talk about that in a minute. But one of the things that really did happen to me at Berkeley, which to me was a sort of pivotal, almost kind of transformative event, was in the very first conversation that I had with my then-postdoc mentor, was when I first came and he took me to lunch and said, 'So you did all this really great work in your PhD and it's very elegant. So if I gave you a group of like three smart undergrads to work with you and build a system that would be useful based on the work that you did, what would you have them do?' And I was totally dumbstruck, because no one had actually ever asked me that question. And it bothered me that I didn't have an answer. So in some ways that was this inflection point in my thinking of, if I do something, it shouldn't just be pretty.
So that's when I started to work on — well, first of all, machine learning more broadly, because I felt like that was just a generally useful bag of techniques, which I think history has proven that is in fact the case. But also over time — and that started at Berkeley and continued in my faculty role at Stanford — working on applications of machine learning that would both be useful in themselves and also drive the development of new machine learning techniques, which I could have some assurance were actually useful because they arose in the context of a more applied problem that I was working on. So I gradually became more and more interested in that interplay between foundational methods and applications.
It's really interesting that he prodded you to think about what are the problems you really want to try to solve. And this is often where — I mean, kind of what allows the great scientists to stand out is selecting those problems that are important — I mean, intellectually interesting, but important as well.
Yeah. And I think I wish that more people actually spent time thinking about the nature of the problems that they work on and what value they provide to society. And I think that manifests on both sides of the spectrum. There are people who are doing, like, really obscure theory and don't spend time thinking about, is this going to benefit humanity in any way. And then on the other hand, you have really equally smart people working on problems that, while applied, are, shall we say, not as aspirational as one would hope.
And how many people do we need applying machine learning to optimizing, you know, the stock market or selling ads online? I mean, maybe some of that, but you really would like to see more people spending their mental energies applying machine learning to problems of social good. And so to me, that was actually again the sort of natural next step of my evolution in trying to become more applied — is not only wanting to do something that was useful, but actually useful for good. And that was part of the transition into biology, which happened towards the late '90s when I was a relatively junior faculty member at Stanford.
So you're computer science junior faculty at Stanford. And how did you get to know what those people are doing in the wet labs?
So initially I was mostly looking for cool datasets. And the datasets that we had to work with as computer scientists at that point were rather lame. I mean, they were not as challenging technically, but more importantly, when you said, 'If I solve this problem, will anybody care?' and the answer is, I can publish a paper that said the accuracy went up from 83 to 87 percent, but so what? So I went and started talking to people on the biology side. My first problem, funnily enough, that I worked on was with someone who was an epidemiologist working on tuberculosis, who subsequently went to the Gates Foundation to work on TB there. And so it's actually a problem that's very relevant to our COVID problems today. And that was my first foray.
And then over time I expanded from epidemiology and problems that were more at the clinical level down to biology, and then back up again to work again on more clinically relevant data. And so I kind of did almost a full circle from clinical data — or in that case, epidemiological data — down to very in-depth biology using some of the earlier microarray datasets, some of the earlier genome sequencing datasets, and so on, and then over time back to working on data that was from clinical samples.
Okay. So you mentioned that this was the era of microarrays and gene sequencing. I mean, the Human Genome Project was up and going, but they didn't have — you know, that wasn't done yet. So we weren't in the genomics age. What was really lacking there in biology from your view? Was it just not enough data, or that the data types weren't properly linked, or both, or what?
Well, I mean, initially — I mean, certainly there were some interesting datasets there, and I think we did some — I worked on work that I'm still quite proud of on, for instance, how do you use gene expression data to uncover gene regulatory networks. And that was, I think, work that was quite influential, a paper still very highly cited. And so I think there one could make do even with the datasets that were out there and do some interesting — certainly development of machine learning techniques, but also extract interesting biological insights.
But I think what I realized over time was that as I developed my own sort of interest in biology and said, 'Here's a problem that I think if I were able to solve it, it would be super cool and I think quite valuable,' look around and the datasets just weren't there. And so you would go to a fellow biologist and say, 'Hey, if we generated this dataset together, think about the cool stuff that we can do.' And they would say, 'Oh, yeah, that could be quite interesting. Very cool. I don't really have a postdoc that's able to work on this right now, so why don't we write a grant proposal together to the NIH?' And you know, by the time you write the proposal and then you rewrite it when you get your first round of reviews, and then you hire the postdoc and then you onboard the postdoc, three years have passed, and by that point you don't really care.
You're alluding to some of the challenges of academia.
Exactly. And so that was rather frustrating — not to have any ability to sort of say, 'Look, this is a very important problem, and I would love to just be able to generate data that's fit to purpose.' So to me, that was part of the shift in thinking that ultimately led me to Incitro. But I will say the truth is that the tools to do what I would have wanted to do were just not there.
So I did, as an example — my post — not my postdoc, sorry, my sabbatical in a biology lab at UCSF with Jonathan Weissman, who's one of the leaders in the functional genomics CRISPR field. Incredible scientist. And when I did my sabbatical there, we were working with model systems like yeast and trying to do things like looking at the effect of one gene knockout versus two gene knockouts. And even in yeast, which is a much more tractable organism, these experiments were incredibly slow and painful to do because the biological toolkit just wasn't there. We didn't have CRISPR. We didn't have iPS cells. We didn't have all of the ways in which one could take individual cells and measure their transcriptional profiles. So I think my ambitions, if you will, the science just wasn't ready for them at the time.
But a tool-driven revolution was beginning to happen — like the ability to generate massive amounts of data of the types that you are mentioning. I mean, that's step one. Once you can generate all of these different kinds of data, well, now you can sink your teeth into it and analyze it and think about how it might go together and perhaps put machine learning to work. But we're getting a little bit ahead of ourselves now. So you had an entrepreneurial experience in here with Coursera, online learning, famous, right? You're co-founder and co-CEO of this thing. Now, did you actually, like, leave Stanford, like, full-time to do this, or was it a part-time thing?
