retrosynthesis-guide

name	retrosynthesis-guide
description	Retrosynthetic analysis and computational reaction prediction
metadata	{"openclaw":{"emoji":"🧪","category":"domains","subcategory":"chemistry","keywords":["retrosynthesis","reaction prediction","organic chemistry","computational chemistry"],"source":"wentor-research-plugins"}}

Retrosynthesis Guide

Plan synthetic routes for target molecules using retrosynthetic analysis principles and computational tools, from Corey's logic to modern AI-driven approaches.

What Is Retrosynthesis?

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:

• Target Molecule (TM): The molecule you want to synthesize
• Synthon: Idealized reactive fragment from a disconnection
• Synthetic Equivalent: Real reagent corresponding to a synthon
• Transform: Reverse of a chemical reaction (retro-reaction)
• FGI (Functional Group Interconversion): Convert one functional group to another to enable a disconnection

Corey's Retrosynthetic Strategies

Strategic Bond Disconnections

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

Common Disconnection Patterns

# 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

Computational Retrosynthesis Tools

Tool Comparison

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

Using ASKCOS

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')}")

Using IBM RXN for Chemistry

# 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')}")

Using AiZynthFinder (Open Source)

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 Notation for Chemistry

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

Reaction Databases

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)

Best Practices for Route Planning

1. Start simple: Begin with the most obvious disconnections before trying exotic transforms.
2. Consider availability: Check if precursors are commercially available (Sigma-Aldrich, TCI, Alfa Aesar).
3. Minimize steps: Convergent synthesis (combining two complex halves) is generally preferred over linear synthesis.
4. Protect and deprotect wisely: Minimize protecting group manipulations; each adds 2 steps (protection + deprotection).
5. Check literature: Search Reaxys or SciFinder for precedent before attempting novel transformations.
6. Validate computationally: Use forward reaction prediction to verify that proposed retrosynthetic steps are feasible.
7. Consider scale: Reactions that work at milligram scale may fail at gram scale. Check for scalability issues (exothermic reactions, heterogeneous mixing).