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

Molecular Property Prediction

Overview

Molecular property prediction involves predicting chemical, physical, or biological properties of molecules from their structure. TorchDrug provides comprehensive support for both classification and regression tasks on molecular graphs.

Available Datasets

Drug Discovery Datasets

Classification Tasks:

• BACE (1,513 molecules): Binary classification for β-secretase inhibition
• BBBP (2,039 molecules): Blood-brain barrier penetration prediction
• HIV (41,127 molecules): Ability to inhibit HIV replication
• Tox21 (7,831 molecules): Toxicity prediction across 12 targets
• ToxCast (8,576 molecules): Toxicology screening
• ClinTox (1,478 molecules): Clinical trial toxicity
• SIDER (1,427 molecules): Drug side effects (27 system organ classes)
• MUV (93,087 molecules): Maximum unbiased validation for virtual screening

Regression Tasks:

• ESOL (1,128 molecules): Water solubility prediction
• FreeSolv (642 molecules): Hydration free energy
• Lipophilicity (4,200 molecules): Octanol/water distribution coefficient
• SAMPL (643 molecules): Solvation free energies

Large-Scale Datasets

• QM7 (7,165 molecules): Quantum mechanical properties
• QM8 (21,786 molecules): Electronic spectra and excited state properties
• QM9 (133,885 molecules): Geometric, energetic, electronic, and thermodynamic properties
• PCQM4M (3,803,453 molecules): Large-scale quantum chemistry dataset
• ZINC250k/2M (250k/2M molecules): Drug-like compounds for generative modeling

Task Types

PropertyPrediction

Standard task for graph-level property prediction supporting both classification and regression.

Key Parameters:

• model: Graph representation model (GNN)
• task: "node", "edge", or "graph" level prediction
• criterion: Loss function ("mse", "bce", "ce")
• metric: Evaluation metrics ("mae", "rmse", "auroc", "auprc")
• num_mlp_layer: Number of MLP layers for readout

Example Workflow:

import torch
from torchdrug import core, models, tasks, datasets

# Load dataset
dataset = datasets.BBBP("~/molecule-datasets/")

# Define model
model = models.GIN(input_dim=dataset.node_feature_dim,
                   hidden_dims=[256, 256, 256, 256],
                   edge_input_dim=dataset.edge_feature_dim,
                   batch_norm=True, readout="mean")

# Define task
task = tasks.PropertyPrediction(model, task=dataset.tasks,
                                 criterion="bce",
                                 metric=("auprc", "auroc"))

MultipleBinaryClassification

Specialized task for multi-label scenarios where each molecule can have multiple binary labels (e.g., Tox21, SIDER).

Key Features:

• Handles missing labels gracefully
• Computes metrics per label and averaged
• Supports weighted loss for imbalanced datasets

Model Selection

Recommended Models by Task

Small Molecules (< 1000 molecules):

• GIN (Graph Isomorphism Network)
• SchNet (for 3D structures)

Medium Datasets (1k-100k molecules):

• GCN, GAT, or GIN
• NFP (Neural Fingerprint)
• MPNN (Message Passing Neural Network)

Large Datasets (> 100k molecules):

• Pre-trained models with fine-tuning
• InfoGraph or MultiviewContrast for self-supervised pre-training
• GIN with deeper architectures

3D Structure Available:

• SchNet (continuous-filter convolutions)
• GearNet (geometry-aware relational graph)

Feature Engineering

Node Features

TorchDrug automatically extracts atom features:

• Atom type
• Formal charge
• Explicit/implicit hydrogens
• Hybridization
• Aromaticity
• Chirality

Edge Features

Bond features include:

• Bond type (single, double, triple, aromatic)
• Stereochemistry
• Conjugation
• Ring membership

Custom Features

Add custom node/edge features using transforms:

from torchdrug import data, transforms

# Add custom features
transform = transforms.VirtualNode()  # Add virtual node
dataset = datasets.BBBP("~/molecule-datasets/",
                        transform=transform)

Training Workflow

Basic Pipeline

1. Load Dataset: Choose appropriate dataset
2. Split Data: Use scaffold split for drug discovery
3. Define Model: Select GNN architecture
4. Create Task: Configure loss and metrics
5. Setup Optimizer: Adam typically works well
6. Train: Use PyTorch Lightning or custom loop

Data Splitting Strategies

Random Split: Standard train/val/test split Scaffold Split: Group molecules by Bemis-Murcko scaffolds (recommended for drug discovery) Stratified Split: Maintain label distribution across splits

Best Practices

• Use scaffold splitting for realistic drug discovery evaluation
• Apply data augmentation (virtual nodes, edges) for small datasets
• Monitor multiple metrics (AUROC, AUPRC for classification; MAE, RMSE for regression)
• Use early stopping based on validation performance
• Consider ensemble methods for critical applications
• Pre-train on large datasets before fine-tuning on small datasets

Common Issues and Solutions

Issue: Poor performance on imbalanced datasets

• Solution: Use weighted loss, focal loss, or over/under-sampling

Issue: Overfitting on small datasets

• Solution: Increase regularization, use simpler models, apply data augmentation, or pre-train on larger datasets

Issue: Large memory consumption

• Solution: Reduce batch size, use gradient accumulation, or implement graph sampling

Issue: Slow training

• Solution: Use GPU acceleration, optimize data loading with multiple workers, or use mixed precision training