Users are enthusiastic about the optimistic projections for AI generative models driving rapid progress in drug discovery over the next decade, cheering on Latent Labs' work.
@Latent_Labs Of course, they have an agent Latent-Y that can do all sorts of tasks: antibody design and interaction with Latent-X2, fetching info from literature. It gives up to 50x speedups #cvpr2026
The AlphaFold2 architecture in a nutshell: a beautiful mix of AI and domain experience poured there, eg. the evoformer block uses a tweaked attention to take into consideration the structure and interactions in proteins #cvpr2026
The decades-long protein folding problem and how crazy the AlphaFold series of models performed #cvpr2026
Protein mutation analogy to computer vision: as if a single wrong pixel could break an image #cvpr2026
@Latent_Labs Utimately these predictions are tested in wet labs and Latent-X1 did well #cvpr2026
Behold the prediction of Latent-X1, the first foundation model of @Latent_Labs #cvpr2026
Now a clear and quick intro to proteins (what are they, their structure, how they work). Checking badge … yes, we’re still at #cvpr2026
For the generation, the protein binders are key and to model that they treat atoms as point clouds (it’s still worth studying 3d and point clouds, folks) #cvpr2026
An analogy between how Imagenet ultimately enabled generative models and how the CASP challenge and then Alphafold let to generative protein design #cvpr2026
@Latent_Labs Much effort is invested in making producing drug-like generation but also improving screening efficiency with genAI #cvpr2026
@Latent_Labs A highly optimistic view for the next 10 years. Go go @Latent_Labs! #cvpr2026