alphafold-42d85953·4 events·first seen Aliases: AlphaFold, AlphaFold 3
Biohub and EvolutionaryScale released ESMFold2, a 6.2-billion-parameter open-weights model for predicting the 3D shapes of proteins, DNA, RNA, and small molecules by treating molecular sequences as language. Unlike AlphaFold 3, ESMFold2 can operate without multiple sequence alignments (MSAs) by using a transformer-based embedding model (ESMC) trained on 2.8 billion sequences, outperforming Chai-1 in MSA-free settings and matching AlphaFold 3 when MSAs are provided. The model weights are freely available on HuggingFace and via API through Biohub, making frontier-level structural biology accessible without proprietary infrastructure. The release is significant for drug discovery involving novel or synthetic molecules where MSA databases may be sparse.
DeepMind has used AlphaFold to determine the structure of a key protein implicated in heart disease. The announcement highlights a new scientific application of AlphaFold's protein structure prediction capabilities to cardiovascular research. This represents a continued expansion of AlphaFold's impact on biomedical discovery beyond its initial structural biology applications.
DeepMind published a retrospective on AlphaFold's five-year impact on biological research and scientific discovery. The post surveys how the protein structure prediction system has accelerated science globally since its initial release. As a tier-1 source anniversary piece, it likely highlights cumulative usage statistics, downstream research enabled, and future directions.
DeepMind published a retrospective marking the 10th anniversary of AlphaGo, reflecting on its influence on scientific discovery and its role in the broader path toward AGI. The piece traces how AlphaGo's reinforcement learning breakthroughs catalyzed downstream advances in biology and other domains. It frames the milestone as part of DeepMind's ongoing AGI narrative.