Retrosynthetic analysis and computational reaction prediction
Plan synthetic routes for target molecules using retrosynthetic analysis principles and computational tools, from Corey's logic to modern AI-driven approaches.
Retrosynthesis works backward from a target molecule to identify simpler, commercially available precursors:
Target Molecule (TM)
|
[Disconnection 1] ← Apply transform (reverse of a known reaction)
|
Synthon A + Synthon B
| |
[Available] [Disconnection 2]
|
Synthon C + Synthon D
| |
[Available] [Available]
Key terminology:
| Strategy | Description | When to Use |
|---|---|---|
| FGI | Convert functional groups to enable disconnections | When direct disconnection is not possible |
| C-C Bond disconnection | Break carbon-carbon bonds | Building the carbon skeleton |
| C-X Bond disconnection | Break carbon-heteroatom bonds | Functional group installation |
| Ring disconnection | Open rings to identify acyclic precursors | Cyclic target molecules |
| Symmetry exploitation | Use molecular symmetry to simplify analysis | Symmetric molecules |
| Convergent synthesis | Combine two complex fragments late | Minimize linear step count |
# 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
| Tool | Developer | Method | Access |
|---|---|---|---|
| ASKCOS | MIT | Template-based + neural | Free (askcos.mit.edu) |
| IBM RXN | IBM Research | Transformer seq2seq | Free (rxn.res.ibm.com) |
| Reaxys | Elsevier | Database-backed | Subscription |
| SciFinder-n | CAS | Database + AI | Subscription |
| Spaya | Iktos | Graph neural network | Commercial |
| AiZynthFinder | AstraZeneca | Monte Carlo tree search | Open source |
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}")
SMILES (Simplified Molecular Input Line Entry System) is the standard text representation:
# 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
| Database | Coverage | Features | Access |
|---|---|---|---|
| Reaxys | 130M+ reactions | Experimental conditions, yields | Subscription |
| SciFinder / CAS | 160M+ reactions | Commercial availability, safety data | Subscription |
| USPTO | 3.7M reactions | US patent reactions | Free (open data) |
| Open Reaction Database (ORD) | Growing | Structured reaction data, conditions | Free |
| RMG (Reaction Mechanism Generator) | Kinetics | Automated mechanism generation | Free (MIT) |