AI Discovered Antibiotics: How Small Data & Small GNNs Led to Big Results, w/ MIT Prof. Jim Collins

**Nathan Labenz** (0:00)
Hello, and welcome back to The Cognitive Revolution. Today my guest is Jim Collins, Termeer Professor of Medical Engineering at MIT, and the leader of an AI-powered project that has created several new antibiotics, which are not only effective against antibiotic resistance strains, but also work, at least in some cases, via entirely new mechanisms of action. The problem of antibiotic resistance is genuinely staggering in scale. More than one million people are estimated to die globally each year from treatment-resistant infections, and it's getting worse. In 2016, a special commission in the United Kingdom warned that if we don't address the resistance crisis soon, by 2050 we could have 10 million deaths per year, which would put the problem on par with all of cancer. Yet, pharmaceutical companies have largely abandoned antibiotic development because the economics simply haven't worked. It costs just as much to develop an antibiotic as any other drug, but people take them for only a short period of time. And critically, given a new antibiotic that's capable of treating the most drug-resistant strains, the medical system would reserve it to be used as a last line of defense, naturally limiting the size of the market. The good news is that Professor Collins and team seem to have created not just a few breakthrough drug candidates, but a multi-step AI-powered process, which they've refined for the last five-plus years, and proven by applying it to several different bacterial targets that can select candidate antibiotic molecules from the vast expanse of chemical space in silico with a high enough hit rate that it's now realistic to expect that the antibiotic crisis could be, for practical purposes, solved in just the next few years. That is obviously awesome news, but with so much AI news flying around these days, it's a story that surprisingly few people have heard. Even at The Curve last weekend, where all attendees were super well-informed AI obsessives like me, not many were aware of this development. I think that's really unfortunate, because I'm digging into Professor Collins' work. I found that this is not just a feel-good story, but an example of how even with relatively small datasets and modest compute budgets, modern machine learning techniques, cleverly applied, can drive huge value without anything looking like AGI. In concrete terms, as you'll hear in much more detail, by training small convolutional graph neural networks on datasets consisting of just a few thousand chemical structures and how effective each one was at stopping the growth of a target bacteria, the team was able to create a model that could screen tens of millions of compounds for efficacy in just a few days' time. And then by using these predictions as part of a pipeline that also scored candidate molecules for novelty as compared to known antibiotics, chemical stability, the ease or difficulty of synthesis, and safety or toxicity for human cells, they were able to identify a small set of very promising candidates. Which, upon actual synthesis and testing, did contain hits that were not only effective against the target strains, but again, at least in some cases, work via previously unknown mechanisms and without harming other types of bacteria. These compounds are now moving toward clinical trials. And while barring some sort of operation warp speed for antibiotics, it will still be years before they're broadly available, I was struck by Professor Collins' estimate that with these techniques at our disposal, the R&D costs to generate a pipeline of 15 or 20 promising new antibiotics could be as low as a few tens of millions of dollars, while the entire process, including the clinical trials required to get them approved, would cost maybe $20 billion.
By the standard of recent data center buildout deals, which have been dominating the headlines, this is extremely affordable. And the fact that this work remains relatively unknown even in the AI community suggests to me that in our haste to create, understand, tame or control, and hopefully live in harmony with fully general or even super intelligent AIs, which we hope will then turn around and cure all the diseases and otherwise benefit all humanity, we risk blinding ourselves to simpler, safer, surer wins, which themselves could still prove positively transformational for the human condition without introducing poorly understood or potentially existential risks. With that in mind, I hope you enjoy this deep dive into how AI, even with small datasets and just a few GPUs, is accelerating the discovery of life-saving drugs with MIT Professor Jim Collins. Jim Collins, Termeer Professor of Medical Engineering at MIT and creator of novel antibiotics. Welcome to The Cognitive Revolution.

**Jim Collins** (4:38)
Yeah, thanks for having me on your show.

**Nathan Labenz** (4:40)
I'm really excited about this and really excited about the work that you have done. It's an incredible thing. I often reflect on just how many groundbreaking milestone moments are passing us by all the time. I've been going around telling people literally at cocktail parties and stuff about this work, and nobody's heard of it. I swear, when I was a kid, people would have heard about this. I think it would have been like the talk of the town. But these days, there's just so much stuff flying by that people are missing it. So I'm excited to correct that.