What ML architecture best predicts protein-ligand binding: GNNs or ensembles?

[BrianL]

I'm a computational biology PhD student starting a project to predict protein-ligand binding affinities using machine learning models trained on existing structural data. I have a strong background in molecular biology but my ML skills are more theoretical, and I'm unsure about the best model architecture to start with given the high-dimensional, sparse nature of the feature space. For researchers applying ML in biology, what has been your experience with graph neural networks versus more traditional ensemble methods for this type of problem, and how critical is domain-specific feature engineering versus letting the model learn representations directly from raw data?

[Richard46]

GNNs are a natural fit for protein–ligand graphs, but on small biology datasets they don’t always win. In practice I’ve found a strong ligand-only baseline (ECFP4 fingerprints + gradient boosting) often matches or beats a basic GNN unless you bring in good 3D features or a well-regularized architecture.

[Oliver_S]

Cross-validation caveats and architectural choices: Use leave-one-protein-out to gauge generalization to new targets, and leave-one-ligand-out to test novel chemistries. For 3D data, consider SE(3)-equivariant nets (SchNet-like, DimeNet) to exploit geometry. Regularize, early-stop, and monitor overfitting due to graph size. And remember data leakage through shared fragments.

[Benjamin92]

Practical pipeline I’d try:
- Assemble dataset (PDBbind or similar) with ligands and proteins; standardize docking pose or use best-known pose.
- Baseline ligand features: ECFP4, MACCS keys; model with XGBoost or LightGBM; evaluate MAE/RMSE.
- Basic GNN: a 2-3 layer MPBN with updated edge features (bond type, distances) on ligand graph; train with mean-squared error.
- If performance needs improvement, move to joint protein–ligand graphs or a simple protein embedding (sequence-based) fused with ligand graph via a readout MLP.
- Try 3D-aware nets if you have reliable conformations: SchNet, DimeNet/DimeNet++, or E(n)-equivariant GNNs; incorporate distance/angle features as edge attributes.
- Validation: protein- and ligand-split schemes; hold-out test set.
- Metrics: MAE, RMSE, R, and sometimes Spearman correlation; calibration curves can help for ranking.
- Tools: PyTorch Geometric, DGL-LifeSci; data loaders for PDBbind; baseline: RDKit for fingerprints.
- Data quality: ensure consistent preprocessing and splits to avoid leakage.

[OliviaT]

Start simple: ligand-only with ECFP4 + XGBoost; then add protein context; if you have 3D data, try a lightweight 3D GNN; monitor data splits to avoid leakage.

[MichaelT]

How big is your dataset? Do you have 3D coordinates for proteins and ligands, and are you planning to include docking or MD-derived features? What’s your target metric (predictive accuracy vs ranking)?