Organization score is the sum of its models
EvolutionaryScale
Benefit
+2.10
Harm
0.00
Net Good
2.10
Timeline
- Benefit
- Harm
- Net
Models
| Rank | Model | Organization | Benefit | Harm | Net | Events | Last impact |
|---|---|---|---|---|---|---|---|
| 1 | ESM3 | EvolutionaryScale | +2.10 | 0.00 | 2.10 | 2 | 21 Sep 2026 |
Domains
Events
Reaction-aware hypergraph learning of chemical representations with LARK
Molecular pretraining offers a route to learning transferable chemical representations from unlabelled data. However, existing approaches pretrained on isolated molecular structures struggle to generalize to reaction-specific tasks because their pretraining objectives provide limited information about chemical transformations. A key challenge is to integrate this reaction information into chemical representations. Here we introduce LARK, a unified framework for learning atom-molecule-reaction knowledge from chemical transformations. Using approximately 0.6 million reactions encompassing 0.88 m
ESM3 generates esmGFP, a distant functional fluorescent protein
EvolutionaryScale’s ESM3 language model produced esmGFP, a fluorescent protein about 58 percent identical to known GFPs, described in a 2024 preprint and a 2025 Science paper.