gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/torchdrug/references/molecular_generation.md

Molecular Generation

Overview

Molecular generation involves creating novel molecular structures with desired properties. TorchDrug supports both unconditional generation (exploring chemical space) and conditional generation (optimizing for specific properties).

Task Types

AutoregressiveGeneration

Generates molecules step-by-step by sequentially adding atoms and bonds. This approach enables fine-grained control and property optimization during generation.

Key Features:

• Sequential atom-by-bond construction
• Supports property optimization during generation
• Can incorporate chemical validity constraints
• Enables multi-objective optimization

Generation Strategies:

1. Beam Search: Keep top-k candidates at each step
2. Sampling: Probabilistic selection for diversity
3. Greedy: Always select highest probability action

Property Optimization:

• Reward shaping based on desired properties
• Real-time constraint satisfaction
• Multi-objective balancing (e.g., potency + drug-likeness)

GCPNGeneration (Graph Convolutional Policy Network)

Uses reinforcement learning to generate molecules optimized for specific properties.

Components:

1. Policy Network: Decides which action to take (add atom, add bond)
2. Reward Function: Evaluates generated molecule quality
3. Training: Reinforcement learning with policy gradient

Advantages:

• Direct optimization of non-differentiable objectives
• Can incorporate complex domain knowledge
• Balances exploration and exploitation

Applications:

• Drug design with specific targets
• Material discovery with property constraints
• Multi-objective molecular optimization

Generative Models

GraphAutoregressiveFlow

Normalizing flow model for molecular generation with exact likelihood computation.

Architecture:

• Coupling layers transform simple distribution to complex molecular distribution
• Invertible transformations enable density estimation
• Supports conditional generation

Key Features:

• Exact likelihood computation (vs. VAE's approximate likelihood)
• Stable training (vs. GAN's adversarial training)
• Efficient sampling through invertible transformations
• Can generate molecules with specified properties

Training:

• Maximum likelihood on molecule dataset
• Optional property prediction head for conditional generation
• Typically trained on ZINC or QM9

Use Cases:

• Generating diverse drug-like molecules
• Interpolation between known molecules
• Density estimation for molecular space

Generation Workflows

Unconditional Generation

Generate diverse molecules without specific property targets.

Workflow:

1. Train generative model on molecule dataset (e.g., ZINC250k)
2. Sample from learned distribution
3. Post-process for validity and uniqueness
4. Evaluate diversity metrics

Evaluation Metrics:

• Validity: Percentage of chemically valid molecules
• Uniqueness: Percentage of unique molecules among valid
• Novelty: Percentage not in training set
• Diversity: Internal diversity using fingerprint similarity

Conditional Generation

Generate molecules optimized for specific properties.

Property Targets:

• Drug-likeness: LogP, QED, Lipinski's rule of five
• Synthesizability: SA score, retrosynthesis feasibility
• Bioactivity: Predicted IC50, binding affinity
• ADMET: Absorption, distribution, metabolism, excretion, toxicity
• Multi-objective: Balance multiple properties simultaneously

Workflow:

1. Define reward function combining property objectives
2. Train GCPN or condition flow model on properties
3. Generate molecules with desired property ranges
4. Validate generated molecules (in silico → wet lab)

Scaffold-Based Generation

Generate molecules around a fixed scaffold or core structure.

Applications:

• Lead optimization keeping core pharmacophore
• R-group enumeration for SAR studies
• Fragment linking and growing

Approaches:

• Mask scaffold during training
• Condition generation on scaffold
• Post-generation grafting

Fragment-Based Generation

Build molecules from validated fragments.

Benefits:

• Ensures drug-like substructures
• Reduces search space
• Incorporates medicinal chemistry knowledge

Methods:

• Fragment library as building blocks
• Vocabulary-based generation
• Fragment linking with learned linkers

Property Optimization Strategies

Single-Objective Optimization

Maximize or minimize a single property (e.g., binding affinity).

Approach:

• Define scalar reward function
• Use GCPN with RL training
• Generate and rank candidates

Challenges:

• May sacrifice other important properties
• Risk of adversarial examples (valid but non-drug-like)
• Need constraints on drug-likeness

Multi-Objective Optimization

Balance multiple competing objectives (e.g., potency, selectivity, synthesizability).

Weighting Approaches:

• Linear combination: w1×prop1 + w2×prop2 + ...
• Pareto optimization: Find non-dominated solutions
• Constraint satisfaction: Threshold on secondary objectives

Example Objectives:

• High binding affinity (target)
• Low binding affinity (off-targets)
• High synthesizability (SA score)
• Drug-like properties (QED)
• Low molecular weight

Workflow:

from torchdrug import tasks

# Define multi-objective reward
def reward_function(mol):
    affinity_score = predict_binding(mol)
    druglikeness = calculate_qed(mol)
    synthesizability = sa_score(mol)

    # Weighted combination
    reward = 0.5 * affinity_score + 0.3 * druglikeness + 0.2 * (1 - synthesizability)
    return reward

# GCPN task with custom reward
task = tasks.GCPNGeneration(
    model,
    reward_function=reward_function,
    criterion="ppo"  # Proximal policy optimization
)

Constraint-Based Generation

Generate molecules satisfying hard constraints.

Common Constraints:

• Molecular weight range
• LogP range
• Number of rotatable bonds
• Ring count limits
• Substructure inclusion/exclusion
• Synthetic accessibility threshold

Implementation:

• Validity checking during generation
• Early stopping for invalid molecules
• Penalty terms in reward function

Training Considerations

Dataset Selection

ZINC (Drug-like compounds):

• ZINC250k: 250,000 compounds
• ZINC2M: 2 million compounds
• Pre-filtered for drug-likeness
• Good for drug discovery applications

QM9 (Small organic molecules):

• 133,885 molecules
• Includes quantum properties
• Good for property prediction models

ChEMBL (Bioactive molecules):

• Millions of bioactive compounds
• Activity data available
• Target-specific generation

Custom Datasets:

• Focus on specific chemical space
• Include expert knowledge
• Domain-specific constraints

Data Augmentation

SMILES Augmentation:

• Generate multiple SMILES for same molecule
• Helps model learn canonical representations
• Improves robustness

Graph Augmentation:

• Random node/edge masking
• Subgraph sampling
• Motif substitution

Model Architecture Choices

For Small Molecules (<30 atoms):

• Simpler architectures sufficient
• Faster training and generation
• GCN or GIN backbone

For Drug-like Molecules:

• Deeper architectures (4-6 layers)
• Attention mechanisms help
• Consider molecular fingerprints

For Macrocycles/Polymers:

• Handle larger graphs
• Ring closure mechanisms important
• Long-range dependencies

Validation and Filtering

In Silico Validation

Chemical Validity:

• Valence rules
• Aromaticity rules
• Charge neutrality
• Stable substructures

Drug-likeness Filters:

• Lipinski's rule of five
• Veber's rules
• PAINS filters (pan-assay interference compounds)
• BRENK filters (toxic/reactive substructures)

Synthesizability:

• SA score (synthetic accessibility)
• Retrosynthesis prediction
• Commercial availability of precursors

Property Prediction:

• ADMET properties
• Toxicity prediction
• Off-target binding
• Metabolic stability

Ranking and Selection

Criteria:

1. Predicted target affinity
2. Drug-likeness score
3. Synthesizability
4. Novelty (dissimilarity to known actives)
5. Diversity (within generated set)
6. Predicted ADMET properties

Selection Strategies:

• Pareto frontier selection
• Weighted scoring
• Clustering and representative selection
• Active learning for wet lab validation

Best Practices

1. Start Simple: Begin with unconditional generation, then add constraints
2. Validate Chemistry: Always check for valid molecules and drug-likeness
3. Diverse Training Data: Use large, diverse datasets for better generalization
4. Multi-Objective: Consider multiple properties from the start
5. Iterative Refinement: Generate → validate → retrain with feedback
6. Domain Expert Review: Consult medicinal chemists before synthesis
7. Benchmark: Compare against known actives and random samples
8. Synthesizability: Prioritize molecules that can actually be made
9. Explainability: Understand why model generates certain structures
10. Wet Lab Validation: Ultimately validate promising candidates experimentally

Common Applications

Drug Discovery

• Lead generation for novel targets
• Lead optimization around active scaffolds
• Bioisostere replacement
• Fragment elaboration

Materials Science

• Polymer design with target properties
• Catalyst discovery
• Energy storage materials
• Photovoltaic materials

Chemical Biology

• Probe molecule design
• Degrader (PROTAC) design
• Molecular glue discovery

Integration with Other Tools

Docking:

• Generate molecules → Dock to target → Retrain with docking scores

Retrosynthesis:

• Filter generated molecules by synthetic accessibility
• Plan synthesis routes for top candidates

Property Prediction:

• Use trained property prediction models as reward functions
• Multi-task learning with generation and prediction

Active Learning:

• Generate candidates → Predict properties → Synthesize best → Retrain