🔬 The Coolest Diffusion Research Isn't in LLMs — Evan Feinberg & Sergey Edunov, Genesis Molecular AI

July 1, 2026

AI Summary

5 min read

In 2017, Evan Feinberg and Sergey Edunov were convinced that generative adversarial networks were the future of image generation. They were wrong. But the wait for a better primitive—diffusion—turned out to be the key to unlocking a much harder problem: predicting how small molecules bind to proteins. Now, as Feinberg puts it, "some of the most innovative diffusion research is happening in our field," in 3D structure prediction for drug discovery. Their company, Genesis Molecular AI, has built a model called Pearl that they claim achieves sub-angstrom accuracy on protein-ligand complex prediction, a threshold they argue is necessary for the model to be useful in real drug development.

The Protein-Ligand Binding Problem

Drug discovery is often described as finding a key for a lock. The lock is a protein (or sometimes a nucleic acid) that causes a disease when it is overactive or malfunctioning. The key is a small molecule drug that binds to that protein and changes its behavior—usually stopping an enzyme from functioning or activating a receptor. A necessary but not sufficient part of this process is predicting whether a given molecule will bind well to a given protein, and exactly how it will fit into the protein's binding pocket. This 3D arrangement of the drug bound to the protein is called a "pose."

Continue reading the full summary in the app — free to try.

Read Full Summary →

Free • No credit card required

Listen to Audio Summary Open in App

Never miss an episode of Latent Space: The AI Engineer Podcast

Get every new episode summarized in your inbox — free, ~5 minutes to read.

No spam. Unsubscribe anytime.

Show Notes

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 (Pl