Update Readme

README.md

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----
-license: mit
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+---
+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.
+
+## 🧬 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.