retrocast 0.5.1

a framework for ingesting, validating, canonicalizing, and adapting retrosynthesis model outputs to a unified benchmark standard.

RetroCast: A Unified Format for Multistep Retrosynthesis

RetroCast is a comprehensive toolkit for standardizing, scoring, and analyzing multistep retrosynthesis models. It decouples prediction from evaluation, allowing rigorous, apples-to-apples comparison of disparate algorithms on a unified playing field.

The Crisis of Evaluation

The field of retrosynthesis is fragmented.

1. Incompatible Outputs: AiZynthFinder outputs bipartite graphs; Retro* outputs precursor maps; DirectMultiStep outputs recursive dictionaries. Comparing them requires writing bespoke parsers for every paper.
2. Ad-Hoc Metrics: "Solvability" is often calculated differently across publications, with varying definitions of commercial stock (e.g., using eMolecules screening sets vs. actual buyable catalogs).
3. Flawed Benchmarks: The standard PaRoutes n5 dataset is heavily skewed (74% of routes are length 3-4), masking performance failures on complex targets. Furthermore, the standard stock definition for PaRoutes creates synthetic "ground truths" that are often physically unobtainable.

RetroCast solves this. It provides a canonical schema, adapters for 10+ models, and a rigorous statistical pipeline to turn retrosynthesis from a qualitative art into a quantitative science.

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Key Features

• Universal Adapters: "Air-gapped" translation layers for AiZynthFinder, Retro*, DirectMultiStep, SynPlanner, Syntheseus, ASKCOS, RetroChimera, DreamRetro, MultiStepTTL, SynLlama, and PaRoutes.
• Canonical Schema: All routes are cast into a strict, recursive Molecule / ReactionStep Pydantic model.
• Curated Benchmarks: Includes the Reference Series (for algorithm comparison) and Market Series (for practical utility), stratified by route length and topology to eliminate statistical noise.
• Rigorous Statistics: Built-in bootstrapping (95% CI), pairwise tournaments, and probabilistic ranking. No more "Model A is 0.1% better than Model B" without significance testing.
• Reproducibility: Every artifact is tracked via cryptographic manifests (SHA256).

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Installation

We recommend using uv for fast, reliable dependency management.

# Install as a standalone tool
uv tool install retrocast

# Or add to your project
uv add retrocast

Get Publication Data

whole data/ folder with benchmarks, stocks, raw predictions, processed routes, scores, and results (with accompanying manifests) is available at files.ischemist.com/retrocast/publication-data. You can get any file from the index, or download whole folders using a bash script:

curl -fsSL https://files.ischemist.com/retrocast/get-pub-data.sh | bash -s

which will show you available folders and their sizes. To get all benchmark files, Run

curl -fsSL https://files.ischemist.com/retrocast/get-pub-data.sh | bash -s -- definitions

you can verify the integrity of downloaded files against the manifests by running

retrocast verify --all

that command might warn you about missing files---that is expected. Manifests for, say 4-scored, contain hashes of input files from 3-results, and if you downloaded only 4-scored, you will get warnings about missing 3-results files.

a dump of the sqlite db with the stocks, routes, and results loaded into SynthArena can be found in ischemist/syntharena repo.

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Quick Start

1. The Ad-Hoc Workflow

Have a raw output file from a model? Score it immediately.

# Convert raw AiZynthFinder JSON to RetroCast format
retrocast adapt \
    --input raw_predictions.json.gz \
    --adapter aizynth \
    --output routes.json.gz

# Score against a stock file
retrocast score-file \
    --benchmark data/1-benchmarks/definitions/ref-lin-600.json.gz \
    --routes routes.json.gz \
    --stock data/1-benchmarks/stocks/n5-stock.txt \
    --output scores.json.gz \
    --model-name "My-Experimental-Model"

2. The Project Workflow

For full-scale benchmarking, RetroCast enforces a structured data lifecycle: Ingest $\to$ Score $\to$ Analyze.

Initialize a project:

retrocast init

Configure your model in retrocast-config.yaml:

models:
  dms-explorer:
    adapter: dms
    raw_results_filename: predictions.json
    sampling: { strategy: top-k, k: 50 }

Run the pipeline:

# 1. Ingest: Standardize raw outputs from data/2-raw/
retrocast ingest --model dms-explorer --dataset ref-lin-600

# 2. Score: Evaluate against the benchmark's defined stock
retrocast score --model dms-explorer --dataset ref-lin-600

# 3. Analyze: Generate bootstrap statistics and HTML plots
retrocast analyze --model dms-explorer --dataset ref-lin-600 --make-plots

Output: Interactive diagnostic plots (Solvability vs Depth, Top-K) and a Markdown report in data/5-results/.

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The Benchmarks

RetroCast introduces two new benchmark series derived from PaRoutes, fixing the skew and stock issues of the original dataset. These subsets were selected via seed stability analysis to ensure they are statistically representative of the underlying difficulty distribution.

The Reference Series (ref-)

Target Audience: Algorithm Developers Designed to compare search algorithms (e.g., MCTS vs. Retro* vs. Transformers). Uses the internal PaRoutes stock to isolate search failures from stock availability issues.

Benchmark	Targets	Description
ref-lin-600	600	Linear routes stratified by length (100 each for lengths 2–7).
ref-cnv-400	400	Convergent routes stratified by length (100 each for lengths 2–5).
ref-lng-84	84	All available routes of extreme length (8–10 steps).

The Market Series (mkt-)

Target Audience: Computational Chemists Designed to assess practical utility. Targets are filtered to be solvable using Buyables, a curated catalog of 300k compounds available for <$100/g.

Benchmark	Targets	Description
mkt-lin-500	500	Linear routes solvable with commercial buyables (Stratified).
mkt-cnv-160	160	Convergent routes solvable with commercial buyables (Stratified).

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Python API

RetroCast is also a library. You can use it to integrate standardization directly into your training or inference loops.

from retrocast import adapt_single_route, TargetInput

# Define the target
target = TargetInput(id="t1", smiles="CC(=O)Oc1ccccc1C(=O)O")

# Your model's raw output (any supported format)
raw_output = {
    "smiles": "CC(=O)Oc1ccccc1C(=O)O",
    "children": [...]
}

# Cast to the canonical Route object
route = adapt_single_route(raw_output, target, adapter_name="dms")

print(f"Depth: {route.length}")
print(f"Leaves: {[m.smiles for m in route.leaves]}")

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Visualization: SynthArena

RetroCast powers SynthArena, an open-source web platform for visualizing and comparing retrosynthetic routes.

• Compare predictions from any two models side-by-side.
• Visualize ground truth vs. predicted routes with diff overlays.
• Inspect stratified performance metrics interactively.

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Vision: Structural AI for Chemistry

Applications of ML to Chemistry have mostly centered on quantitative problems (predicting toxicity, pKd, yield)—tasks constrained by the scarcity of labeled data.

However, we observe that the most transformative breakthroughs in AI (LLMs, AlphaFold) have occurred in structural problems: tasks that require generating complex, structured outputs (like language or protein folding) rather than regression scalars.

Retrosynthesis is the premier structural problem of organic chemistry. But effectively solving it requires a fundamental shift: we must move beyond fragmented data formats and inconsistent evaluation methods. We need a unified, rigorous infrastructure to standardize, track, and measure progress in this domain.

RetroCast is that infrastructure.

Citation

If you use RetroCast in your research, please cite:

@misc{retrocast,
  title         = {Procrustean Bed for AI-Driven Retrosynthesis: A Unified Framework for Reproducible Evaluation},
  author        = {Anton Morgunov and Victor S. Batista},
  year          = {2025},
  eprint        = {2512.07079},
  archiveprefix = {arXiv},
  primaryclass  = {cs.LG},
  url           = {https://arxiv.org/abs/2512.07079}
}

License

MIT License. See LICENSE for details.