Algorithms
Browse retrosynthesis algorithms and their model versions
| Name | Description | Versions |
|---|---|---|
| Version | Instance Name | Description | Prediction Runs | Date Added |
|---|---|---|---|---|
| Version | Instance Name | Description | Prediction Runs | Date Added |
|---|---|---|---|---|
Current state-of-the-art achieved by any version of this algorithm.
95% CI: [26.3%, 41.3%]
Top-1 Accuracy achieved by Retro* (High) (v1.0.0)
95% CI: [26.3%, 41.3%]
Top-10 Accuracy achieved by Retro* (High) (v1.0.0)
95% CI: [18.6%, 25.8%]
Top-1 Accuracy achieved by Retro* (v1.0.0)
95% CI: [18.6%, 25.8%]
Top-10 Accuracy achieved by Retro* (v1.0.0)
| version | slug | description | prediction runs | date added |
|---|---|---|---|---|
v1.0.0 | og-r-v1-1-0 | — | 8 runs | 2 days ago |
| version | slug | description | prediction runs | date added |
|---|---|---|---|---|
v1.0.0 | og-rh-v1-1-0 | — | 8 runs | 2 days ago |
if you use this model in your work, cite it as
@inproceedings{retrostar,
title={Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search},
author={Chen, Binghong and Li, Chengtao and Dai, Hanjun and Song, Le},
booktitle={The 37th International Conference on Machine Learning (ICML 2020)},
year={2020},
publisher={PMLR}
}