ASKCOS - Retrosynthetic Template Relevance
SkillCloud & infraRetrosynthetic template relevance prediction using a locally deployed ASKCOS TorchServe service. Returns ranked precursor suggestions with confidence scores from 5 template sets (reaxys, pistachio, pistachio_ringbreaker, bkms_metabolic, reaxys_biocatalysis). Requires local deployment at http://localhost:9410.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the ASKCOS - Retrosynthetic Template Relevance skill
What this skill tells your AI
The instructions your AI receives, as published by lamm-mit/scienceclaw in skills/askcos/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 242
- Forks
- 42
- Last commit
- Aug 2026
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askcos- Source
- github.com/lamm-mit/scienceclaw