Organization score is the sum of its models
Chai Discovery
Benefit
+0.32
Harm
0.00
Net Good
0.32
Timeline
- Benefit
- Harm
- Net
Models
| Rank | Model | Organization | Benefit | Harm | Net | Events | Last impact |
|---|---|---|---|---|---|---|---|
| 1 | Chai-1 | Chai Discovery | +0.32 | 0.00 | 0.32 | 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
Chai-1 offers an open model for biomolecular complex prediction
Chai Discovery released Chai-1 in September–October 2024 as an open multi-modal structure model reporting PoseBusters and CASP15 scores near AlphaFold 3, with a web server usable for commercial drug discovery.