Something remarkable happened in computer vision in 2025: the fields of generative modeling and representation learning, which had developed largely independently, suddenly converged. Diffusion models started leveraging pretrained vision encoders like DINOv2 to dramatically accelerate training. Researchers discovered that aligning generative models to pretrained representations doesn’t just speed things up—it often produces better results.

As someone who works on generative models for (among other things) molecules and proteins, I’ve been watching this unfold with great interest. Could we do the same thing for molecular ML? We now have foundation models like MACE that learn powerful atomic representations. Could aligning molecular generative models to these representations provide similar benefits?

In this post, I’ll summarize what happened in vision (organized into four “phases”), and then discuss what I think are the key lessons for molecular machine learning. The punchline: many of these ideas are already starting to appear in our field, but we’re still in the early stages compared to vision.

For a more detailed treatment of the vision developments with full references and figures, see the extended blog post on my website.