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README.md

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 # 💊 Drug Binding Affinity Prediction with GNNs + CNN + Cross-Attention & LLM Interpretation
 This project is the implementation of the Deep Learning model to predict the **Binding Affinity ($pK_d$)** between drug candidates (ligand) and target proteins. The feature of that system is that it solves the "Black Box" problem in drug discovery field by presenting an **Explainable AI (XAI)** module powered by **Cross-Attention weights** and **LLM interpretation**, which allows researchers to visualize the active site of the ligand and which atoms play a vital role in the binding process.
 
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 ## Architecture: The "Hybrid" Approach
 The model uses a dual-encoder architecture with a Cross-Attention mechanism, mimicking the physical binding process:
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 1.  **Ligand Encoder (Graph):**
      * **GAT (Graph Attention Network):** Treats atoms as nodes and bonds as edges. Uses 4 attention heads to capture complex chemical substructures.
 2.  **Protein Encoder (Sequence):**