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Dr. Noubar Afeyan discusses the power artificial intelligence has in health sciences. 

Flagship Pioneering CEO on A.I. in health care 

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how has data and machine learning and ai transform the way you think about like the life sciences areas you work in well. Um so back up and say that kind of the machine learning AI component which is Kind of intensified in the last few years, has its roots 25, 30 years ago as well. And life science has always been about data in that we're studying systems that precede us right? Most other human endeavors study things that humans made, we don't and so and we never did. So what the only way you can do that is to get better and better measuring and then interpreting and then coming up with models often inadequate models with which to make slight perturbations which we call drugs or or other such things. So so it's always been about data and the more we've been able to measure both more precisely and in more diverse ways, the more data we generated, the more we've needed better and better models. The machine learning advances in the recent past simply have given us better models to do that with and that has increased if you will are leap size. Now there's another side of this week leap leap size. Yeah, the size of the steps we can take. So you can you know basically if you can take a whole system and keep it intact and look at it from many different perspectives and then say this is what's happening then the kind of questions you can ask is very different than if you have to disintegrate the system and study time, little portions of it which is what molecular biology has been doing for a long time I think about drug discovery. What we do is we take a complex human and we disintegrated into ideally a single protein that we then make a drug against. And then we're worried that somehow when we put it back together, it won't reproduce in clinical trials. Well, it shouldn't reproduce in clinical trials, except rarely. And that's what we observe. But if you could not disintegrate the human and actually do a drug discovery or drug development using data from an entire human, better yet millions of humans. And those measurements are informative enough as to the model of what's happening. It's a different Ballgame. And that's what we're beginning to switch into and how quickly is this happening? What what what is the next decade gonna look like in terms of advancements you can make using these technologies. I mean, of course, you know, we're in the business of optimism. And so it feels like we're in an exponential period, which means that a year ago we couldn't imagine what we're doing today and two years from now, we'll laugh at what we're doing today. And that's the definition of kind of a period where things are changing very rapidly. For example, one of our most recent projects, the company that's become known as Valley Health. But it was just a platform in our labs a couple years ago has literally collected millions of humans equivalent of disease and normal data and is using that to motivate the kind of decisions it makes to what drugs to discover, not just choose and that type of vertical integration and kind of digital native version of conceiving of a pharmaceutical company was not possible two years ago, three years ago. Now we're experimenting with it. Now the version one point of that will create a lot of value, but then there'll be a two point and three point. Oh, so the learning cycles are happening. Another example I can give you is that several years ago, one of my colleagues who's in the audience of Actovegin form the company that is called now generate buy medicines. It's what it's doing is essentially trying to de novo design brand new proteins. So we're given 100,000 plus or minus proteins in our human body and that defines us. Uh and yet the biotech industry wants to bind to those proteins, change those proteins use them as vaccines like the spike protein etcetera. The question is, what can computers do in that field so far? They've been able to observe and and analyze these proteins. What we think we can now do again, based on machine learning based models is to de novo generate an arbitrary protein for an arbitrary function. Now the difference between DNA sequence and a protein function is vast when it comes to information processing and yet ourselves do that every single day. It turns out our DNA in our genome does not know what a protein is, let alone that it three dimensionally folded, let alone that it binds to something. And yet we have tons of interesting proteins. Now we're building models that can similarly correlate DNA sequence to a protein function, which means that you can specify a protein function. I want to buy into this thing at this place and not everywhere else. And it'll spit out a DNA sequence. You can try in the lab and learn in a few cycles how to make a completely novel protein that has never been possible in the 35 years. I've been in biotech and that's becoming possible as we speak, huge.

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