Version Compatibility

Reference examples tested with: AiZynthFinder 4.4+, Chemformer 1.3+, RDKit 2024.09+, RDChiral 1.1+, Aizynthtrain 1.0+, ASKCOS Lite 0.5+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: aizynthcli --version

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Retrosynthesis

Plan synthetic routes from a target molecule back to commercially-available building blocks. AiZynthFinder 4.0 is the open-source production-grade tool: Monte Carlo Tree Search (MCTS) + template-based expansion + multi-objective scoring (MO-MCTS). Chemformer is the template-free transformer alternative. ASKCOS is the academic reference. Modern best practice combines retrosynthesis with forward validation (the predicted route should also predict the target from starting materials via Molecular Transformer) and building-block availability.

For generative design pipelines that need synthetic feasibility, see chemoinformatics/generative-design. For reaction enumeration (forward direction), see chemoinformatics/reaction-enumeration.

Method Taxonomy

Decision: For most users, AiZynthFinder 4.4 with USPTO + USPTO-50k templates is the open-source standard. For high-stakes routes, validate with Molecular Transformer forward prediction.

AiZynthFinder Setup

Goal: Configure AiZynthFinder with USPTO templates and a building-block stock; run MCTS retrosynthesis on a target SMILES.

Approach: Build a configuration dict pointing to policy templates and a stock HDF5, instantiate AiZynthFinder, set the target SMILES, then call tree_search() followed by build_routes().

from aizynthfinder.aizynthfinder import AiZynthFinder

config_dict = {
    'policy': {
        'files': {
            'uspto': ['policy/uspto_model.onnx', 'templates/uspto_templates.csv'],
        }
    },
    'stock': {
        'files': {
            'zinc': 'stock/zinc.h5',
        }
    },
    'finder': {
        'algorithm': 'mcts',
        'iteration_limit': 100,
        'time_limit': 120,
    }
}

finder = AiZynthFinder(configdict=config_dict)
finder.target_smiles = 'CC(=O)Nc1ccc(C(=O)Nc2cccc(C(F)(F)F)c2)cc1'
finder.tree_search()
finder.build_routes()

Route Output Analysis

for route in finder.routes:
    print(f'Depth: {route.depth}, Score: {route.score:.2f}')
    print(f'In-stock: {sum(node.in_stock for node in route.leafs())}')
    print(f'Building blocks: {[node.smiles for node in route.leafs()]}')

Critical metrics:

• Depth: synthetic steps (1-3 typical for medchem)
• Score: AiZynthFinder route score (0-1)
• In-stock: how many leaf nodes are commercially available

Route Scoring (MO-MCTS)

AiZynthFinder 4.0 supports multi-objective scoring:

config_dict['finder']['algorithm'] = 'mo_mcts'
config_dict['finder']['mo_mcts'] = {
    'objectives': [
        {'name': 'state_score', 'weight': 0.5},
        {'name': 'broken_bonds_score', 'weight': 0.3},
        {'name': 'route_length', 'weight': 0.2, 'maximize': False},
    ]
}

Building Block Stocks

aizynthtrain build-stock --input zinc_building_blocks.smi --output zinc.h5

Forward Validation with Molecular Transformer

from molecular_transformer import predict_forward

precursors = route.leafs()
predicted_product = predict_forward(precursors)
match = (Chem.CanonSmiles(predicted_product) ==
         Chem.CanonSmiles(finder.target_smiles))

Routes where the forward prediction reproduces the target are highest confidence. ~30-50% of AiZynthFinder routes pass forward validation.

Template-Free with Chemformer

from chemformer import Chemformer

cf = Chemformer.load_pretrained('USPTO_RETROSYNTHESIS_TEMPLATE_FREE')
predictions = cf.predict('CC(=O)Nc1ccc(C(=O)Nc2cccc(C(F)(F)F)c2)cc1',
                         beam_search=10)

Trade-off: Template-free is more flexible but harder to debug. Combining with AiZynthFinder template MCTS gives best of both.

Disconnection-Aware Design (DAD)

aizynthcli --smiles compounds.smi --output routes.json \
           --config config.yaml --policy uspto --stock zinc

For each compound, returns top-K routes. Score-feasibility for generative design:"

• "Synthesizable" = in-stock leaves >= 2 in best route
• "Routable" = at least one route depth <= 5
• "Easy" = at least one route depth <= 3 with all leaves in-stock

References

• Saigiridharan et al., J. Cheminform. 16:57 -- AiZynthFinder 4.0.
• Irwin et al., 2022 -- Chemformer.
• Schwaller et al. -- Molecular Transformer.

Related Skills

• chemoinformatics/generative-design - Generative design with feasibility scoring
• chemoinformatics/reaction-enumeration - Forward direction
• chemoinformatics/scaffold-analysis - R-group decomposition