Available Datasets

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