Retrosynthesis Guide

Target Molecule (TM)
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   [Disconnection 1] ← Apply transform (reverse of a known reaction)
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   Synthon A + Synthon B
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   [Available]  [Disconnection 2]
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                Synthon C + Synthon D
                    |            |
                [Available]  [Available]

# Alcohol (C-OH) → Carbonyl reduction
R-CH(OH)-R' ⟹ R-CO-R' + NaBH4/LiAlH4

# Amine (C-N) → Reductive amination
R-CH2-NH-R' ⟹ R-CHO + R'-NH2

# C-C Bond (aldol) → Aldol retro
R-CH(OH)-CH2-CO-R' ⟹ R-CHO + CH3-CO-R'

# C-C Bond (Grignard) → Grignard retro
R-CH(OH)-R' ⟹ R-CHO + R'-MgBr

# Ester (C-O) → Fischer esterification retro
R-COO-R' ⟹ R-COOH + R'-OH

# Amide (C-N) → Amide coupling retro
R-CO-NH-R' ⟹ R-COOH + R'-NH2

# Diels-Alder → Retro Diels-Alder
Cyclohexene derivative ⟹ Diene + Dienophile

# Wittig → Retro Wittig
R-CH=CH-R' ⟹ R-CHO + R'-CH2-PPh3

import requests

# ASKCOS API for retrosynthetic planning
# (requires running ASKCOS locally or using the hosted version)

target_smiles = "CC(=O)Oc1ccccc1C(=O)O"  # Aspirin

# One-step retrosynthesis
response = requests.post(
    "https://askcos.mit.edu/api/retro/",
    json={
        "smiles": target_smiles,
        "num_results": 10,
        "max_depth": 5
    }
)

results = response.json()
for i, result in enumerate(results.get("precursors", [])[:5]):
    print(f"Route {i+1}:")
    print(f"  Precursors: {result['smiles']}")
    print(f"  Template: {result.get('template', 'N/A')}")
    print(f"  Score: {result.get('score', 'N/A')}")

# IBM RXN API
from rxn4chemistry import RXN4ChemistryWrapper

api_key = os.environ["RXN4CHEM_API_KEY"]
rxn = RXN4ChemistryWrapper(api_key=api_key)
rxn.create_project("retrosynthesis_example")

# Predict retrosynthesis
response = rxn.predict_automatic_retrosynthesis(
    product="CC(=O)Oc1ccccc1C(=O)O",  # Aspirin
    max_steps=3
)

# Get results
results = rxn.get_predict_automatic_retrosynthesis_results(response["prediction_id"])
for route in results.get("retrosynthetic_paths", []):
    print(f"Route confidence: {route.get('confidence', 'N/A')}")
    for step in route.get("steps", []):
        print(f"  Reaction: {step.get('reaction_smiles', 'N/A')}")

from aizynthfinder.aizynthfinder import AiZynthFinder

# Configure the finder
finder = AiZynthFinder()
finder.stock.load("zinc_stock.hdf5")  # Commercial building blocks
finder.expansion_policy.load("expansion_policy_model.onnx")  # Retro model

# Set target
finder.target_smiles = "CC(=O)Oc1ccccc1C(=O)O"  # Aspirin

# Run tree search
finder.config.search.time_limit = 120  # seconds
finder.config.search.iteration_limit = 500
finder.tree_search()

# Extract and analyze routes
finder.build_routes()
for i, route in enumerate(finder.routes):
    print(f"Route {i+1} (score: {route.score:.3f}):")
    print(f"  Steps: {len(route.reactions)}")
    for rxn in route.reactions:
        print(f"    {rxn}")

# Common SMILES patterns
Water:          O
Ethanol:        CCO
Benzene:        c1ccccc1
Aspirin:        CC(=O)Oc1ccccc1C(=O)O
Caffeine:       Cn1c(=O)c2c(ncn2C)n(C)c1=O
Ibuprofen:      CC(C)Cc1ccc(cc1)C(C)C(=O)O

# SMILES rules
# Atoms: C, N, O, S, P, F, Cl, Br, I
# Bonds: - (single, implicit), = (double), # (triple)
# Branches: () for branching
# Rings: numbers for ring closure (c1ccccc1 = benzene)
# Aromatic: lowercase letters
# Stereochemistry: / \ for E/Z, @ @@ for R/S