Predicting Molecular Properties with Graph Neural Networks: An End-to-End Platform from SMILES to Solubility

This open-source project provides a complete end-to-end platform that represents molecules as graph structures, compares three GNN architectures (GCN, GraphSAGE, GIN) for predicting water solubility (a key property in drug development), integrates explainable AI (GNNExplainer) and production-grade deployment (FastAPI + React), and solves the problem of traditional machine learning handling molecular topological structures.

Core challenge of traditional machine learning in handling molecular data: molecules are non-tabular data, and their topological structures (atom connection patterns, rings, branches) determine chemical properties. GNN models atoms as nodes and chemical bonds as edges, preserving topological structures while learning properties. This project focuses on water solubility prediction (40% of drug candidates fail due to solubility issues).

Workflow: SMILES string → RDKit parsing → Graph construction → GNN model → Prediction → GNNExplainer interpretation.

Three GNN Architectures:

1. GCN: Aggregates neighbor node features to update its own representation;
2. GraphSAGE: Samples neighbors and learns aggregation functions (mean/LSTM/pooling);
3. GIN: Based on graph isomorphism testing theory, its expressive power is equivalent to the Weisfeiler-Lehman algorithm, capturing subtle structural differences.

ESOL dataset (1128 molecules) test results:

Model	MAE	RMSE
GCN	1.4526	1.8407
GraphSAGE	1.4160	1.7666
GIN	0.6876	0.8566

GIN's error is less than half of other models, so it is selected as the main production model.

Integrating GNNExplainer provides:

1. Output of water solubility logarithm values;
2. Marking of key atoms;
3. Heatmaps showing atom importance;
4. Highlighting of key substructures (e.g., hydroxyl groups increase solubility, hydrophobic carbon chains decrease it). This helps understand the model and provides chemical insights, suitable for high-risk fields.

Backend API (FastAPI):

• GET /health: Health check;
• POST /predict: Input SMILES to return solubility;
• POST /visualize: Generate 2D molecular structure;
• POST /explain: Return prediction and explanation visualization;
• POST /analyze: Comprehensive endpoint.

Frontend Interface (React + Vite)：Supports SMILES input for prediction, structure viewing, explanation graphs, and browsing benchmark results.

Applications:

• Drug discovery: Screen molecules with solubility issues to save costs;
• Materials science: Extend to predictions of toxicity, bioavailability, etc.

Future Directions:

• Real-time molecular hand-drawing interface;
• Expansion to more datasets;
• Hyperparameter optimization;
• Docker cloud deployment;
• Model monitoring and analysis.

Conclusion: This project changes the paradigm of molecular science, provides a toolchain for AI + chemistry, and choosing a model adapted to the data structure (e.g., GIN) is key.