Single-Step Models
Syntheseus currently supports 11 single-step models: RetroChimera (which we recommend as default), its two submodels, and 8 additional external models.
For convenience, for each model we include a default checkpoint (trained on Pistachio in the case of RetroChimera, and on USPTO-50K otherwise).
If no checkpoint directory is provided during model loading, syntheseus will automatically download a default checkpoint and cache it on disk for future use.
The default path for the cache is $HOME/.cache/torch/syntheseus, but it can be overriden by setting the SYNTHESEUS_CACHE_DIR environment variable.
See tables below for the links to the default checkpoints.
Recommended models (trained on Pistachio)
| Model checkpoint link | Source |
|---|---|
| RetroChimera | trained by us |
| RetroChimeraEdit | trained by us |
| RetroChimeraDeNovo | trained by us |
Other models (trained on USPTO-50K)
| Model checkpoint link | Source |
|---|---|
| Chemformer | finetuned by us starting from checkpoint released by authors |
| GLN | released by authors |
| Graph2Edits | released by authors |
| LocalRetro | trained by us |
| MEGAN | trained by us |
| MHNreact | trained by us |
| RetroKNN | trained by us |
| RootAligned | released by authors |
Choice of dataset
The USPTO-50K dataset is well-established but relatively small. Advanced users may prefer to either use our Pistachio-trained models, or retrain any model class of interest on their own data. To do that, please follow the instructions in the original model repositories.
In reaction_prediction/cli/eval.py a forward model can be used for computing back-translation (round-trip) accuracy.
See here for a Chemformer checkpoint finetuned for forward prediction on USPTO-50K. As for the backward direction, pretrained weights released by original authors were used as a starting point.
Licenses
All checkpoints were produced in a way that involved external model repositories, hence may be affected by the exact license each model was released with.
For more details about a particular model see the top of the corresponding model wrapper file in reaction_prediction/inference/.