# 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

```python
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
