lamm-mit ASKCOS - Retrosynthetic Template Relevance
Overview
ASKCOS template_relevance predicts retrosynthetic disconnections using reaction template libraries.
The service runs locally as a TorchServe container (retro_template_relevance) and requires a
SMILES input, returning ranked precursor SMILES with template match scores.
Deployment: https://gitlab.com/mlpds_mit/askcosv2/retro/template_relevance Docs: https://askcos-docs.mit.edu/guide/4-Deployment/4.2-Standalone-deployment-of-individual-modules.html
Requirements
- Docker container
retro_template_relevancerunning athttp://localhost:9410 - Start/stop:
docker start retro_template_relevance/docker stop retro_template_relevance
Usage
Basic Retrosynthesis (JSON output — default)
python3 skills/askcos/scripts/askcos_retro.py \
--smiles "CC(C)C1CCC(C)CC1O"
Human-readable summary
python3 skills/askcos/scripts/askcos_retro.py \
--smiles "CC(C)C1CCC(C)CC1O" \
--model reaxys \
--top 10 \
--format summary
Select template set
python3 skills/askcos/scripts/askcos_retro.py \
--smiles "CC(C)C1CCC(C)CC1O" \
--model pistachio
Parameters
| Flag | Default | Description |
|---|---|---|
--smiles / -s |
required | Target molecule SMILES |
--model / -m |
reaxys |
Template set: reaxys, pistachio, pistachio_ringbreaker, bkms_metabolic, reaxys_biocatalysis |
--top / -n |
10 |
Number of top suggestions to return |
--base-url |
http://localhost:9410 |
TorchServe base URL |
--format / -f |
json |
Output format: json or summary |
Environment Variables
| Variable | Default | Description |
|---|---|---|
ASKCOS_BASE_URL |
http://localhost:9410 |
Override TorchServe URL |
ASKCOS_MODEL |
reaxys |
Default template set |
Output Format (JSON)
{
"target": "CC(C)C1CCC(C)CC1O",
"model": "reaxys",
"total_templates_matched": 191,
"status": "success",
"suggestions": [
{
"rank": 1,
"reactants_smiles": "CC1CCC(C(C)C)C(=O)C1",
"score": 0.4562,
"template_smarts": "[C:1]-[CH;D3;+0:2](-[C:3])-[OH;D1;+0:4]>>[C:1]-[C;H0;D3;+0:2](-[C:3])=[O;H0;D1;+0:4]",
"template_id": "5e1f4b6e6348832850995dbf",
"template_count": 8688,
"necessary_reagent": ""
}
]
}
Example Output (menthol)
ASKCOS (reaxys) — CC(C)C1CCC(C)CC1O
Templates matched: 191
# 1 score=0.4562 n= 8688 precursors: CC1CCC(C(C)C)C(=O)C1
# 2 score=0.0387 n= 20 precursors: CC1CCC2C(C1)OC(=O)C2C
# 3 score=0.0387 n= 20 precursors: CC(C)C1CCC2CC1OC2=O
# 4 score=0.0321 n= 245 precursors: CC1C=CC(C(C)C)CC1 reagent: [O]
# 5 score=0.0279 n=26868 precursors: CC(=O)OC1CC(C)CCC1C(C)C
Top hit (menthone → menthol via reduction) correctly recovers the industrial Takasago process.
Integration with Other Skills
# Get SMILES from RDKit, then run retrosynthesis
SMILES="CC(C)C1CCC(C)CC1O"
# Retrosynthesis
python3 skills/askcos/scripts/askcos_retro.py --smiles "$SMILES" --top 5 --format json
# Analyse top precursor with RDKit
PRECURSOR="CC1CCC(C(C)C)C(=O)C1"
python3 skills/rdkit/scripts/molecular_properties.py --smiles "$PRECURSOR"
References
- ASKCOS: Coley et al., Science 2019. DOI: 10.1126/science.aax1566
- Template relevance: Coley et al., ACS Central Science 2017. DOI: 10.1021/acscentsci.7b00355
- GitLab: https://gitlab.com/mlpds_mit/askcosv2/retro/template_relevance