chapter eleven
11 Graph Neural Networks for Predicting Drug-Target Affinity
This chapter covers
- Transform SMILES strings and proteins into graph representations.
- Master core GNN theory and message-passing mechanisms.
- Build a dual-stream GNN to predict drug-target binding affinity.
- Train and evaluate the model using real-world benchmark datasets.
- Interpret the model's performance and prediction results.
In previous chapters, we treated molecules as sequences of characters in a SMILES string. While powerful, this simplification forces a linear structure onto objects that are inherently three-dimensional. While SMILES preserves the molecule’s connectivity and can even encode stereochemistry, a sequence model may not exploit rich topological and structural information (e.g., connectivity, ring systems, bond types, local atomic neighborhoods) as directly as a model that operates on the graph itself. Relying on a purely linear representation can impair a model’s predictive power and the functional relevance of its learned representations.