Episode Details

This episode has a fun personal twist: There’s a counterfactual world where I was employee #1 at Genesis Molecular AI, the company behind today’s episode. A certain introduction happened a few weeks too late and I had already happily signed at Atomwise, another ML-for-drug-discovery startup. Same problem, different company. I was certain ML was going to transform small molecule drug discovery. Early results were underwhelming. Useful at times, but nowhere near revolutionary. In the last year I’ve seen signs that ML is finally ready to deliver on my convictions from a decade ago. Genesis is one of the places that might have finally cracked this problem. I was super excited to come full circle and catch up with co-founder Evan Feinberg and CTO Sergey Edunov.

If you are at all interested in small molecule drug discovery, we think you will find this fascinating!

In our nearly two hour chat we cover:

* What is small molecule drug discovery, and why is it hard

* Structure prediction as a hotbed of innovation in AI algorithms

* How advances in AI elsewhere have enabled stepwise improvements in predictive power

* How the community benchmarks are essentially calling AI slop good enough

* The Genesis flagship model (PEARL) can routinely hit a threshold that is necessary for real-world applications

* New agentic workflows enabled by these highly accurate models

Read on for more, and also some personal thoughts on the future at the end.

The coolest diffusion research is happening at Genesis

Sergey Edunov came to Genesis from Meta where he led Llama 2 training and Llama 3 pretraining. Sergey was a former physicist who thought he was done with physics after many years of training LLMs. Then, he discovered Genesis, and was blown away with all the novel architecture work they’ve been developing.

It probably surprises no one that modern LLM research has not resulted in fundamentally novel or exciting updates in architectures since almost the advent of the transformer — the entire field is using variants on the same idea that came out in the original “Attention is all you need” paper. Sure, some were quite useful (mixture-of-experts in particular allowed for the massive model paradigm we’re at today), but there was very little conceptually exciting.

“We sort of had to wait for the right primitive to get created, and that turned out to be diffusion… Actually, some of the most innovative diffusion research that’s happening in our field is happening in 3D structure prediction right now.” — Evan Feinberg

The field of 3D structure prediction on the other hand has been a hotbed of research. Genesis’ recent model PEARL (Place Every Atom at the Right Location) is able to understand protein flexibility, and model not just where the ligand goes, but also make small adjustments of the protein so that the two fit better than either alone. The field knew this was missing for a long time, but it was really hard to model until now.

Agentic Discovery

What makes this problem so hard? As Sergey points out, there are 10^60 possible drug-like small molecules. You’ll never be able to search them all, and trying to find the good ones is something like finding a needle in a haystack — except everything except your needle is dangerous.

“There are 10 to the 60 drug-like small molecules in the universe… it’s like finding a needle in a haystack, where everything except your needle is very, very dangerous.” — Sergey Edunov

“Or finding hay in a needle stack might be a more apt analogy.” — Evan Feinberg

Trying to solve the multi-parameter optimization problem is even worse. What makes a strong bi