Retrosynthesis

Overview

Retrosynthesis is the process of planning synthetic routes from target molecules back to commercially available starting materials. TorchDrug provides tools for learning-based retrosynthesis prediction, breaking down the complex task into manageable subtasks.

Available Datasets

USPTO-50K

The standard benchmark dataset for retrosynthesis derived from US patent literature.

Statistics: - 50,017 reaction examples - Single-step reactions - Filtered for quality and canonicalization - Contains atom mapping for reaction center identification

Reaction Types: - Diverse organic reactions - Drug-like transformations - Well-balanced across common reaction classes

Data Splits: - Training: ~40k reactions - Validation: ~5k reactions - Test: ~5k reactions

Format: - Product → Reactants - SMILES representation - Atom-mapped reactions for training

Task Types

TorchDrug decomposes retrosynthesis into a multi-step pipeline:

1. CenterIdentification

Identifies the reaction center - which bonds were formed/broken in the forward reaction.

Input: Product molecule Output: Probability for each bond of being part of reaction center

Purpose: - Locate where chemistry happened - Guide subsequent synthon generation - Reduce search space dramatically

Model Architecture: - Graph neural network on product molecule - Edge-level classification - Attention mechanisms to highlight reactive regions

Evaluation Metrics: - Top-K Accuracy: Correct reaction center in top K predictions - Bond-level F1: Precision and recall for bond predictions

2. SynthonCompletion

Given the product and identified reaction center, predict the reactant structures (synthons).

Input: - Product molecule - Identified reaction center (broken/formed bonds)

Output: - Predicted reactant molecules (synthons)

Process: 1. Break bonds at reaction center 2. Modify atom environments (valence, charges) 3. Determine leaving groups and protecting groups 4. Generate complete reactant structures

Challenges: - Multiple valid reactant sets - Stereospecificity - Atom environment changes (hybridization, charge) - Leaving group selection

Evaluation: - Exact Match: Generated reactants exactly match ground truth - Top-K Accuracy: Correct reactants in top K predictions - Chemical Validity: Generated molecules are valid

3. Retrosynthesis (End-to-End)

Combines center identification and synthon completion into a unified pipeline.

Input: Target product molecule Output: Ranked list of reactant sets (synthesis pathways)

Workflow: 1. Identify top-K reaction centers 2. For each center, generate reactant candidates 3. Rank combinations by model confidence 4. Filter for commercial availability and feasibility

Advantages: - Single model to train and deploy - Joint optimization of subtasks - Error propagation from center identification accounted for

Training Workflows

Basic Pipeline

from torchdrug import datasets, models, tasks

# Load dataset
dataset = datasets.USPTO50k("~/retro-datasets/")

# For center identification
model_center = models.RGCN(
    input_dim=dataset.node_feature_dim,
    num_relation=dataset.num_bond_type,
    hidden_dims=[256, 256, 256]
)

task_center = tasks.CenterIdentification(
    model_center,
    top_k=3  # Consider top 3 reaction centers
)

# For synthon completion
model_synthon = models.GIN(
    input_dim=dataset.node_feature_dim,
    hidden_dims=[256, 256, 256]
)

task_synthon = tasks.SynthonCompletion(
    model_synthon,
    center_topk=3,  # Use top 3 from center identification
    num_synthon_beam=5  # Beam search for synthon generation
)

# End-to-end
task_retro = tasks.Retrosynthesis(
    model=model_center,
    synthon_model=model_synthon,
    center_topk=5,
    num_synthon_beam=10
)

Transfer Learning

Pre-train on large reaction datasets (e.g., USPTO-full with 1M+ reactions), then fine-tune on specific reaction classes.

Benefits: - Better generalization to rare reaction types - Improved performance on small datasets - Learn general reaction patterns

Multi-Task Learning

Train jointly on: - Forward reaction prediction - Retrosynthesis - Reaction type classification - Yield prediction

Advantages: - Shared representations of chemistry - Better sample efficiency - Improved robustness

Model Architectures

Graph Neural Networks

RGCN (Relational Graph Convolutional Network): - Handles multiple bond types (single, double, triple, aromatic) - Edge-type-specific transformations - Good for reaction center identification

GIN (Graph Isomorphism Network): - Powerful message passing - Captures structural patterns - Works well for synthon completion

GAT (Graph Attention Network): - Attention weights highlight important atoms/bonds - Interpretable reaction center predictions - Flexible for various reaction types

Sequence-Based Models

Transformer Models: - SMILES-to-SMILES translation - Can capture long-range dependencies - Require large datasets

LSTM/GRU: - Sequence generation for reactants - Autoregressive decoding - Good for small molecules

Hybrid Approaches

Combine graph and sequence representations: - Graph encoder for products - Sequence decoder for reactants - Best of both representations

Reaction Chemistry Considerations

Reaction Classes

Common Transformations: - C-C bond formation (coupling, addition) - Functional group interconversions (oxidation, reduction) - Heterocycle synthesis (cyclizations) - Protection/deprotection - Aromatic substitutions

Rare Reactions: - Novel coupling methods - Complex rearrangements - Multi-component reactions

Selectivity Issues

Regioselectivity: - Which position reacts on molecule - Requires understanding of electronics and sterics

Stereoselectivity: - Control of stereochemistry - Diastereoselectivity and enantioselectivity - Critical for drug synthesis

Chemoselectivity: - Which functional group reacts - Requires protecting group strategies

Reaction Conditions

While TorchDrug focuses on reaction connectivity, consider: - Temperature and pressure - Catalysts and reagents - Solvents - Reaction time - Work-up and purification

Multi-Step Synthesis Planning

Single-Step Retrosynthesis

Predict immediate precursors for target molecule.

Use Case: - Late-stage transformations - Simple molecules (1-2 steps from commercial) - Initial route scouting

Multi-Step Planning

Recursively apply retrosynthesis to each predicted reactant until reaching commercial building blocks.

Tree Search Strategies:

Breadth-First Search: - Explore all routes to same depth - Find shortest routes - Memory intensive

Depth-First Search: - Follow each route to completion - Memory efficient - May miss optimal routes

Monte Carlo Tree Search (MCTS): - Balance exploration and exploitation - Guided by model confidence - State-of-the-art for multi-step planning

A* Search: - Heuristic-guided search - Optimizes for cost, complexity, or feasibility - Efficient for finding best routes

Route Scoring

Rank synthetic routes by: 1. Number of Steps: Fewer is better (efficiency) 2. Convergent vs Linear: Convergent routes preferred 3. Commercial Availability: How many steps to buyable compounds 4. Reaction Feasibility: Likelihood each step works 5. Overall Yield: Estimated end-to-end yield 6. Cost: Reagents, labor, equipment 7. Green Chemistry: Environmental impact, safety

Stopping Criteria

Stop retrosynthesis when reaching: - Commercial Compounds: Available from vendors (e.g., Sigma-Aldrich, Enamine) - Building Blocks: Standard synthetic intermediates - Max Depth: e.g., 6-10 steps - Low Confidence: Model uncertainty too high

Validation and Filtering

Chemical Validity

Check each predicted reaction: - Reactants are valid molecules - Reaction is chemically reasonable - Atom mapping is consistent - Stoichiometry balances

Synthetic Feasibility

Filters: - Reaction precedent (literature examples) - Functional group compatibility - Typical reaction conditions - Expected yield ranges

Expert Systems: - Rule-based validation (e.g., ARChem Route Designer) - Check for incompatible functional groups - Identify protection/deprotection needs

Commercial Availability

Databases: - eMolecules: 10M+ commercial compounds - ZINC: Annotated with vendor info - Reaxys: Commercially available building blocks

Considerations: - Cost per gram - Purity and quality - Lead time for delivery - Minimum order quantities

Integration with Other Tools

Reaction Prediction (Forward)

Train forward reaction prediction models to validate retrosynthetic proposals: - Predict products from proposed reactants - Validate reaction feasibility - Estimate yields

Retrosynthesis Software

Integration with: - SciFinder (CAS) - Reaxys (Elsevier) - ARChem Route Designer - IBM RXN for Chemistry

TorchDrug as Component: - Use TorchDrug models within larger planning systems - Combine ML predictions with rule-based systems - Hybrid AI + expert system approaches

Experimental Validation

High-Throughput Screening: - Rapid testing of predicted reactions - Automated synthesis platforms - Feedback loop to improve models

Robotic Synthesis: - Automated execution of planned routes - Real-time optimization - Data generation for model improvement

Best Practices

1. Ensemble Predictions: Use multiple models for robustness
2. Reaction Validation: Always validate with chemistry rules
3. Commercial Check: Verify building block availability early
4. Diversity: Generate multiple diverse routes, not just top-1
5. Expert Review: Have chemists evaluate proposed routes
6. Literature Search: Check for precedents of key steps
7. Iterative Refinement: Update models with experimental feedback
8. Interpretability: Understand why model suggests each step
9. Edge Cases: Handle unusual functional groups and scaffolds
10. Benchmarking: Compare against known synthesis routes

Common Applications

Drug Synthesis Planning

• Small molecule drugs
• Natural product total synthesis
• Late-stage functionalization strategies

Library Enumeration

• Virtual library design
• Retrosynthetic filtering of generated molecules
• Prioritize synthesizable compounds

Process Chemistry

• Route scouting for large-scale synthesis
• Cost optimization
• Green chemistry alternatives

Synthetic Method Development

• Identify gaps in synthetic methodology
• Guide development of new reactions
• Expand retrosynthesis model capabilities

Challenges and Future Directions

Current Limitations

• Limited to single-step predictions (most models)
• Doesn't consider reaction conditions explicitly
• Stereochemistry handling is challenging
• Rare reaction types underrepresented

Active Research Areas

• End-to-end multi-step planning
• Incorporation of reaction conditions
• Stereoselective retrosynthesis
• Integration with robotics for closed-loop optimization
• Semi-template methods (balance templates and templates-free)
• Uncertainty quantification for predictions

Emerging Techniques

• Large language models for chemistry (ChemGPT, MolT5)
• Reinforcement learning for route optimization
• Graph transformers for long-range interactions
• Self-supervised pre-training on reaction databases