Very good music. Hi everyone! I have the best job in the world because I have three wonderful leaders to try and understand what they believe will change. What is still holding us back, and who will benefit from this next? The next era of discovery brings together three perspectives: Noubar on creating new companies and scientific platforms, Lila on building AI capabilities for science, and Kiran on biotechnology, drug development and bringing innovation to patients. So thank you all for joining us.
Let me start off with you. What do you think, in your wildest dreams, AI will help us discover that we can't even imagine yet? We can't imagine. But you can.
Yeah, it's a tricky question because I think the answer is everything. So in other words, I think how we discover everything in the future will be fundamentally different, and it will not be limited by human imagination. In nature, nothing is discovered through human imagination; it just exists. And now we're going to be able to do that in multiples of what humans have been able to do, and it's an exciting but also scary prospect.
Do you think AI is accelerating science or actually just fundamentally changing it?
I think it's helping us understand the world in a way that we never imagined, as Noubar said. Much like a microscope being able to go very narrow and deep, or a telescope to go out wide. And I do think that maybe the limitation is our ability to ask creative questions, to be bold enough and have the courage to ask.
What do you think is the next AI breakthrough?
Well, it's definitely going to be very different to what we are doing today, because right now, you know, biology is a very empirical science, and I think AI will enable us to do it very differently. It will allow us to collect, connect very unrelated dots. So I am looking at developing and innovating new medicines that are not about targeting single targets — you know, not those single-targeting drugs — but I really believe that with a combination of understanding biology and leveraging AI, you're going to be able to basically holistically look at networks, signaling channels and neural circuits. And I think that's the way we are going to develop new drugs: multi-specific, multi-faceted drugs.
Noubar, is there something that actually remains intrinsically human in scientific discovery?
Um, well, maybe I'll reframe it in this way. So I know that we're at an era where we're trying to put the human back into life and existence, because it's being challenged by this other life form that we've created — at least thinking form. I do think that how it is to be human is going to change in the scientific fields in the following way. I think we're today obsessing over machine intelligence. We can call it superintelligence, AI or whatever, but I just call it machine intelligence, because the machine form of intelligence, such as it is, and then we have human intelligence. But I think what we have failed to realize is that there is another form of intelligence surrounding us, which is nature's forms of intelligence. Every living cell, every cell that processes information, is also a form of intelligence. I think once we realize that, we're going to realize that what biology has been — which is humans trying to understand nature's intelligence — is now being supplemented by humans trying to understand machine intelligence. And we're not going to be good at either one, in my view. And we're going to have to figure out how to use machine intelligence to better understand nature's intelligence, whether that's the immune system, whether that's viruses.
I mean, but people often debate with me how I could say that a virus is intelligent. Well, a virus that could take down most of the immune system on the planet through a pandemic — if that's not intelligent, then our bodies must be pretty weak. The fact is, it's much more intelligent than viruses on computers that today we rely on. So I do think that humans are going to have to make room for understanding, but also harnessing machine intelligence to better understand nature's intelligence. And why not potentially vice versa.
But does it mean that there could be a discovery that we can't explain?
At some point, there already are tons of discoveries that we can't explain. It's called the modern pharmaceutical industry. Probably half to half the drugs — we don't actually understand how they work. We can give one version of how they might work, but for most of the drugs we don't actually have any mechanistic understanding. But through rigorous testing, through controlled experiments, we can show that it's more efficacious than it's harmful. And so that's regulated and it's approved. So I don't think human understanding — by the way, I don't understand how my iPhone does most of the things it does, and it doesn't stop me from using it. I just don't think this human conceit that anything we don't understand we should be afraid of. That's a new phenomenon. We've been coexisting with things we don't understand for a very long time.
Like Lila, how do you see this? And actually, if you look at DeepMind, I mean, this is a lot of the work that, you know, you and Demis had started.
Yes. And actually, as Noubar was speaking, I was thinking about AlphaFold as an example of a 50-year grand challenge and our advanced AI system. Now we have it available to millions of researchers worldwide, as simple as map search to be able to look up and understand the prediction of a protein, a 3D structure, which is remarkable because it tells us the basic building block of life, and if it misfolds, what that might mean. And we've been able to work with researchers, like in Spain, and taking one medication and applying it to Chagas disease, an FDA-approved medication that impacts 8 million people worldwide — something that as a human we hadn't thought of, but to ask the questions that helped us make that correlation between vaccines.
So I think the opportunity here is not only in the discovery of new things, but also how we can apply other ones. In fact, there are 180,000 researchers right here in India working on problems that India is facing and advancing other types of work in understanding diseases and the medications that go alongside them.
What are you saying, Kiran? I mean, what does it mean for Biocon? So what are you expecting to have? How does it change your company in the next five years?
Yeah. So of course, I think drug innovation is going to accelerate. But I also hope that affordability, or delivering affordable access, will also accelerate, because you are shortening the timelines of lab to market. Having said that, whilst we are all focusing right now on the discovery process, which is certainly going to accelerate and get abbreviated, the next challenge is how do you abbreviate the more expensive part of drug innovation, which is clinical trials. And that's where I again think AI can play a big role in helping us think through what do we want to glean and gather from clinical data that allows us to predict that it is safe and efficacious. And I think those are the kinds of things that can really compress timelines and deliver these medicines faster to patients. So to me, that's the excitement of AI. And that's what we are looking at as a company, saying how do we look at AI to really not just abbreviate but to also look at the affordability part for countries like India.
And I do want to come back to the speed of it, but actually to the affordability. Do you think AI will make the medicine just cheaper?
No. As I mentioned, I think it's a question of time is money. And if you can compress that time, there is of course going to be a cost element to the use by itself. But hopefully the overall benefit will be that you can get medicines to patients cheaper and faster. That's my number.
To that point, you know, you can have a drug discovery that's faster. But when does the treatment of the patient get faster?
Yeah. I mean, I think there's a lot of components that AI is helping and will help a lot. I think the hardest thing will be to do what is out there these days in terms of speculation, which is simulating clinical trials and avoiding them altogether. I think that requires an understanding of human biology and physiology that, I'm afraid, we're quite far from. Albeit, I think that we've certainly modeled digitally lots of complex systems, but I think most people who don't work in biology and medicine don't understand just how complex these systems are. I assume our colleagues at DeepMind, who worked on physics problems and biology problems, learned what Schrödinger learned 80 years ago, which is that biology is not just physics. And as you go up and scale the complexity, the problem goes exponentially high. And in fact, the folding problem is probably among the most deterministic parts of biology. Everything else, through interaction effects, gets very complex. A long way of saying, I think there are going to be shorter-term milestones. So, for example, which patient do you want to include in the trial? Can you train through electronic medical records and find a subset that are going to be more responsive, not just based on diagnostics, but just based on their molecular profiles?
In cancer, I'll give you another example. There's a lot of interest in screening to find early cancer. The problem is those tests create ten times more false positives than they do true positives. What are you going to do with those people if you put them into the diagnostic system? It'll drive costs through the roof. Can you use AI to better triage and massively reduce the number of false positives? Those are things that I think as we start taking care of, it will start impacting clinical care. And over time we could imagine more shortened trials, clearer endpoints. These are all things we're working on. But I don't think this notion of a digital twin of a human that we use for testing is something that we should be looking for too soon.
But what you're talking about, what kind of timeline are we talking about? Like four or five years, that the bottlenecks kind of unwind?
I think the impact's being felt now. And it's kind of hard saying what sequence. But what I was talking about is this massive disruption in dramatically shortening clinical trials. I wish that — I've been doing this for 39 years. There's nothing that would address the cost of new medicines as much as the clinical regulatory complexity and time. But I think that may be kind of a step too far to represent to society and then disappoint people.
I mean, there's already enough with AI that's causing disappointment and fear that I think going out and saying we're going to cure cancer, as some of the frontier lab people are saying, as though it's a proximal goal. It's really difficult to put that out there and then realize that five years from now you're still curing. I mean, we've been curing cancer for 70 years. The joke in the biotech industry is that if mice had a cancer problem, we would have solved it completely, because we've cured every which type of mouse cancer. Humans turn out to be dramatically different from mice in their physiology. So we have to be more humble.
Let me say one other thing. I would say what AI has taught us in the last few years, the new versions of AI, generative AI, is that we now know there's more we don't know than we thought there was. In other words, as a fraction of what there is to be known, we now have gone backwards, because we now know that it's accessible, if only we could make measurements, if only we. So I think that the fight between the unknown and the known is going to stay interesting for a while. And that means, I think as humans, we have to be pretty humble in the way we go about doing it.
Like, how do you look at this?
Yes, it deserves some applause. Why not? I'll go back to what I said at the beginning. I think it gets back to us just asking better questions, asking questions that maybe we didn't have access to before, as Kiran mentioned. We can take now an interdisciplinary approach, like in the past, where maybe we went really deep, being able to look at patterns that may have been inaccessible.
One of the things I've actually — I've been in India for the past week, and one of the areas I've been really excited to learn about, how we've been collaborating here locally, and some of the local innovation is around the area of agriculture. So if I can shift maybe from the human side for a moment, we're working with an organization called Microsoft that's based in Hyderabad. And one of the biggest challenges that we have is that sorghum, which 500 million people rely on as a food source or to feed livestock, has only really been studied in the West, the Western varietals, not the indigenous ones here. So one of the challenges being faced in India is, how do we actually accelerate the understanding around the crop genetics and how to breed it? So we're now working with crop breeders to look at sorghum or jowar, which I have learned my work this week, to excel, to be able to map all the genes and make that freely available so the innovation can happen on the side of that.
Now, as mentioned by my colleagues, there's a lot of work that needs to happen that follows that. But if we can have the breakthrough and get the inputs from the crop breeders, the inputs from the farmers up front and accelerate, take out decades' worth of time and start to crunch that together — like, imagine what this could mean for a more drought-resistant crop, something that can handle better diseases. And this is actually quite personal to me because my mom actually did sorghum research in Africa over 20 years ago, and they got the World Food Prize for one of the first breakthroughs in drought-resistant sorghum. But Africans and Indians are still facing so many challenges, and it's really amazing to see now what might be possible with some of the opportunities of applying AI into areas that have traditionally been underrepresented and understudied.
