Welcome to The Century of Biology! This newsletter explores data, companies, and ideas from the frontier of biology. You can subscribe for free to have the next post delivered to your inbox:

It’s been a busy quarter since I wrote about Sid’s incredible journey to cure his own cancer. This story has gone completely viral and driven an overwhelming amount of interest from patients, regulators, scientists, and founders. How can we scale what Sid has accomplished?

My wife and I also welcomed Rosalind to our family. Amplify Bio has been growing, too. Exciting times all around. You can check out a short essay I wrote about our investment in Infinitopes, an Oxford spinout working to develop the next generation of breakthrough cancer vaccines.

Today, we’re going to explore the case for optimism about AI-powered drug discovery.

Enjoy! 🧬

Typically, a steep cost decline for a foundational technology leads to a Cambrian explosion of new applications and products.

Moore’s Law gave rise to personal computers, portable computers, smart phones, smart watches, and even more questionable devices like smart fridges.

Packy McCormick recently analyzed in exhaustive detail how plummeting costs in the “electric stack” (batteries, magnets, power electronics, etc.) led to a similar proliferation of new engineering marvels.

This pattern hasn’t held for biomedicine. DNA sequencing has decreased exponentially in cost, alongside a whole slew of rapidly improving drug discovery technologies and therapeutic modalities. Despite that, there hasn’t been a meaningful increase in the number of new drugs approved each year, which hovers around ~50.1

And the cost to bring a new drug to market keeps rising. In fact, exponentially so.

In 2012, an equity analyst named Jack Scannell and his colleagues published an article showing that “the number of new drugs approved per billion US dollars spent on R&D has halved roughly every 9 years since 1950.”

Recently, Scannell showed how this phenomenon, coined “Eroom’s Law” (the inverse of Moore’s Law), has steadily continued even with the birth of biotech, the Human Genome Project, and the rise of AI drug discovery.

Source: What can the study of R&D productivity tell us about hopes for AI?

Based on these observations, Scannell and others have voiced skepticism about expecting any different for the next wave of AI breakthroughs in the life sciences. Every time somebody posts a hyperbolic message about AI revolutionizing pharma, Scannell quips, “At last I understand why it costs 100x more to discover a drug today than in 1950. Our computers have slowed down.” (Other good ones here and here.)

Recently, Ruxandra Teslo, a fellow geneticist and writer, has published several great articles outlining why the cost of clinical development—not pre-clinical drug discovery—is the major driver of Eroom’s Law. And she has detailed why policy rather than technology is likely the biggest lever to solve this problem.

Teslo is now a Fellow at Renaissance Philanthropy working to make her vision for Clinical Trial Abundance a reality. Her research here is excellent, and if you haven’t checked out her personal newsletter yet, you should.

Several founders have made the bet that technology can meaningfully reduce the time and cost of trials. I’ve written about how Vial aims to do this. Similarly, Formation Bio is focused on developing their own AI-powered clinical infrastructure.