---
license: mit
pipeline_tag: graph-ml
tags:
- chemistry
- biology
---

# ProteinBindingGNN (44M Parameters)

**ProteinBindingGNN** is a 44-million parameter hybrid Graph Neural Network (GNN) and Transformer architecture designed for node-level protein binding site prediction. It processes both the 3D geometric structure of a protein and its biochemical sequence context (via ESM-2 embeddings) to predict the probability of each amino acid residue belonging to a binding pocket.

The model combines the local geometric awareness of **Equivariant Graph Neural Networks (EGNNs)** with the global, pairwise reasoning capabilities of **Evoformer blocks** (inspired by AlphaFold 2), executed through a multi-step recycling mechanism.

## Usage
You can find everything related to this model at [GraphBind](https://github.com/Labrapuerta/GraphBind)

## 🧬 Model Architecture

The architecture is explicitly designed to balance rich feature preservation (high hidden dimensionality) with controlled reasoning depth to prevent over-smoothing on 3D protein graphs.

* **Parameters:** ~44.1 Million
* **Local Geometry:** 3 EGNN Layers (prevents message-passing over-smoothing, strictly capturing the 3-hop micro-environment).
* **Global Reasoning:** 8 Evoformer Blocks with Coordinate-Aware Edge Updates.
* **Recycling:** 5 iterations (the representations and coordinates are iteratively refined).

### Full Configuration
```python
CONFIG = {
    "node_input_dim":        1305,   # Designed for pre-trained ESM-2 embeddings
    "edge_input_dim":        4,      # e.g., distances, bond types, angles
    "hidden_dim":            512,    # Preserves rich biochemical context
    "num_egnn_layers":       3,      # Local 3D message passing
    "num_evoformer_blocks":  8,      # Global pairwise attention
    "num_heads":             16,     # Attention heads (d_k = 32)
    "dropout":               0.3,    # High regularization for generalization
    "update_coords":         True,   # Dynamic coordinate refinement
    "num_recycles":          5,      # AlphaFold 2 style recycling loop
    "alpha":                 0.3,    # Edge update mixing coefficient
}
```

## Performance & Training Details
This model was trained on a highly curated dataset of 6,700 protein complexes.
You can see the full run and details at [Weights and Biases](https://wandb.ai/a01700257/Graph_binding/workspace?nw=nwusera01700257)

### Class Imbalance Handling
Protein binding datasets suffer from extreme class imbalance (typically ~5% of residues are true binders, 95% are non-binders). The model is optimized for high ranking capability, achieving:

- Validation AUROC: 0.8898

- Validation F1 (Standard Threshold): 0.3875 (Note: F1 is highly sensitive to the massive negative class; threshold tuning is recommended for downstream applications).

- Validation Recall: ~0.50 - 0.57



## Future work
Due to the limitations on gpu's, the model wans't trained enough to show the expected results, in the future with a sponsorship it is planned to keep training and fine tunning.