The AI Revolution in Biology: From Vaccines to Protein Engineering with Amelie Schreiber

**Nathan Labenz** (0:00)
Hello, and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week, we'll explore their revolutionary ideas, and together, we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz, joined by my co-host, Erik Torenberg. Hello, and welcome back to The Cognitive Revolution. Today, I'm speaking with Amelie Schreiber, a computational biochemist and AI researcher working at the forefront of applying the latest AI models and research techniques to some of the most complex and impactful domains imaginable. Drug design, protein network engineering, and broadly unlocking the secrets of biological systems.
In all honesty, this episode has given me more future shock than perhaps any other episode we've done. While traditional analytical methods are not well suited to the crazy complexity of biological systems, and as a result, there's still much more that we don't understand about biology than that we do. As Amelie makes clear, modern AI architectures are perfectly suited to take advantage of biology's massive data sets. And thus, a new wave of AI models are poised to dramatically accelerate the process of scientific discovery in biology. Starting with how we understand protein and other cellular structures and the physical interactions between them, and likely soon zooming out to help us understand how our cells, tissues, and bodies function at higher levels.
As you might expect, given the incredible complexity of biology, if you're not already well-versed in the subject, the first hour will feature a number of new technical concepts. And while Amelie and I do our best to explain them all clearly, this episode, for me at least, did require multiple rewinds for full comprehension. I think it might help to keep in mind the distinction between static structural analysis and dynamic conformational analysis, which allows for molecules to change shape as they interact with one another as you listen. Remember, too, that all the models we discussed today are really rather narrow in scope. Foundation models for biology along the lines of the LLMs that many of us are most familiar with are just now starting to be trained. One upshot of this is that we discuss different models for predicting shapes versus for predicting sequences. Obviously, in reality, these are two sides of the same coin, but for our purposes, they are often modeled separately.
In the second hour, we discussed the implications of all this for human health, longevity, and biosecurity. I had heard, as you probably have, the story of how the first COVID vaccine was designed in just a few days after the right scientist received the required information, but I had never really considered what the world might look like if that pace of medical R&D were to become the norm. The impact would seem to be a near certain revolution, not just in biology, but also in practical medicine. One particularly striking theme is the potential for AI to change the way that we do biological research on humans. The difficulty and danger of experimenting directly on humans has always been a major bottleneck, but the latest models are quickly approaching the point where we should be able to run meaningful digital experiments, and that could radically accelerate the pace of discovery, both for the utopian better and at least with some probability, perhaps catastrophically, for the worse.
And all this, by the way, was before Alpha Fold 3, which dropped shortly after we recorded and which will certainly get its due attention in future episodes. With the full range of possibility in mind, and noting just how small many of these biology models are relative to the latest language models, I definitely want to give a shout out to the policy wonks at the White House who set a lower reporting threshold of 10 to the 23 flops for reporting on biological models as compared to 10 to the 26 flops for language models. That and their recent policy requiring DNA synthesis companies to screen orders against known dangerous sequences are looking extremely smart right now. By comparison, the now familiar question of whether today's chat bots are more helpful than Google for the purposes of making a bioweapon to me, honestly, already feels quite quaint.
For what it's worth, because the vision of the future sketched out here was so far outside my previous understanding, I did take some time to double check my high level interpretation of Amelie's claims with some very credible people in the field. One of my very smartest friends who is also most deeply enmeshed in these issues said simply, that is exactly what is going to happen.
If you find this work valuable, I would appreciate it if you'd take a moment to share it with friends. This episode in particular took a lot more work than a typical CEO interview, but it will be very well worth it if I can help our high value audience get up to speed on such a critical area. And as always, we invite your feedback and suggestions either via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. Now, I hope you enjoy this introductory deep dive into a genuinely awesome area of emerging research that may soon impact all of our lives. This is the AI Revolution in Biology with Amelie Schreiber. Amelie Schreiber, computational biochemist and AI researcher, welcome to The Cognitive Revolution.