Update README.md

README.md

@@ -9,7 +9,7 @@ license: mit
 ---
 
 # 💊 Drug Binding Affinity Prediction with GNNs + CNN + Cross-Attention & LLM Interpretation
-<img width="1284" height="582" alt="image" src="https://github.com/user-attachments/assets/cb814000-5ae2-4967-8b9f-f25869dd5d53" />
+
 
 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.
 
@@ -17,7 +17,7 @@ This project is the implementation of the Deep Learning model to predict the **B
 ## Architecture: The "Hybrid" Approach
 The model uses a dual-encoder architecture with a Cross-Attention mechanism, mimicking the physical binding process:
 
-<img width="3756" height="1797" alt="binding_affinity drawio" src="https://github.com/user-attachments/assets/1e510205-c9c2-468d-8372-2a8a0b45aae7" />
+<img width="100%" alt="binding_affinity drawio" src="https://github.com/user-attachments/assets/1e510205-c9c2-468d-8372-2a8a0b45aae7" />
 
 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.
@@ -31,7 +31,7 @@ We compared multiple architectures on the **PDBbind Refined** dataset. The Hybri
 |--------------|---------------|---------------|---------------|
 | GCN + Transformer for proteins | 1.5190 | 1.1957 | 0.6285  |
 | GAT + Transformer for proteins | 1.5117  | 1.2074  | 0.6310  |
-| GAT + 1 CNN for proteins + Cross-Attention | 1.3867  | 1.0947  | 0.7013  |
+| **GAT + Deep CNN + Cross-Attention** | **1.3867** | **1.0947** | **0.7013** |
 
 
 ## Explainability (XAI)
@@ -41,5 +41,49 @@ The key moment is that the model does not give only a number, but an asnwer why
 3.  Check the drug likeliness of the ligand according to the Lipinski's Rule of 5.
 4.  Uses **Google Gemini API** to generate a chemical explanation of *why* these atoms are critical (e.g., hydrogen bonds, hydrophobic interactions).
 
+## 🧪 Case Study: HIV-1 Protease Inhibitor (PDB: 6e9a)
+To validate the model on high-complexity ligands, we tested it on a potent HIV-1 protease inhibitor (Darunavir analog, PDB: 6e9a).
+* **Ligand: Sulfonamide-based inhibitor ($C_{29}H_{37}N_3O_7S$, MW 575.7 Da). SMILES:** `COc1ccc(S(=O)(=O)N(CC(C)C)C[C@@H](O)[C@H](Cc2ccccc2)NC(=O)O[C@@H]2C[C@@H]3NC(=O)O[C@@H]3C2)cc1`
+* **Target:** HIV-1 Protease Chain A.
+
+* **Molecule:** Sulfonamide-based inhibitor ($C_{29}H_{37}N_3O_7S$, MW 575.7 Da).
+* **Predicted Affinity ($pK_d$):** `7.22` (Classified as **Strong Binder**)
+* **Real Affinity:** High potency confirmed by PDB data.
+
+The Cross-Attention mechanism identified the key pharmacophore features without prior knowledge:
+1. **Polar Anchors (Oxygen #16, #34):** The model assigned high attention scores to the oxygen atoms. Chemically, these act as hydrogen bond acceptors, critical for anchoring the drug to the protein's backbone (Asp29/Asp30 residues).
+2.  **Hydrophobic Core (Carbon #0):** The model highlighted the aromatic carbon in the terminal ring, which is essential for hydrophobic packing in the S2' pocket of the protease.
+<img width="800" alt="Molecule Visualization" src="https://github.com/user-attachments/assets/245be900-41ff-44e9-b31a-69be6d42be8e" />
+
+### 3. Top Critical Atoms
+Below are the atoms with the highest attention weights contributed to the decision:
+
+| Rank | Atom Index | Type | Attention Score | Interpretation |
+| :--- | :--- | :--- | :--- | :--- |
+| 1 | #57 | H | 1.000 | Hydrogen Bond Donor |
+| 2 | #16 | O | 0.729 | **Sulfonamide Oxygen (Key Anchor)** |
+| 3 | #0 | C | 0.676 | **Aromatic Ring (Hydrophobic)** |
+| 4 | #34 | O | 0.581 | Ether Oxygen (H-bond Acceptor) |
+| 5 | #22, #23 | C | ~0.600 | Hydrophobic Scaffold |
+
+### 4. Drug-Likeness & Gemini Report
+The system automatically generates a report to assist chemists:
+
+#### 💊 Lipinski's Rule of 5 Analysis
+* **Status:** Poor (2 violations) 🔴
+* **Mass:** 575.68 Da (Violation: > 500)
+* **H-Acceptors:** 11 (Violation: > 10)
+* *Note: HIV protease inhibitors are often large molecules that break these rules but remain effective.*
+
+#### 🤖 Google Gemini Analysis
+>Affinity Analysis: The predicted binding affinity (pKd = 7.22) suggests moderate to strong binding for this ligand to the target protein. A pKd > 7 generally indicates a promising starting point for drug >discovery, implying significant interaction.
+>
+>Structural Basis: The highlighted atoms, particularly Oxygen (idx 16) and Nitrogen (implicit in the sulfonamide and carbamate groups), likely participate in hydrogen bonding as donors or acceptors. Aromatic >carbons (e.g., idx 0 for the methoxy-substituted phenyl ring) are key for pi-pi stacking interactions within the protein's binding pocket. The specific arrangement of these functional groups and their positions >relative to the protein are critical for recognition.
+>
+>Drug-Likeness: With 2 Lipinski violations, this molecule exhibits poor drug-likeness, particularly concerning oral bioavailability. It may face challenges with absorption and membrane permeability.
+>
+>Conclusion: While the binding affinity is encouraging, the poor drug-likeness warrants caution. Further structural optimization to improve Lipinski compliance would be essential before proceeding with this >molecule as a drug candidate.
+
+
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 *Created by Alex Sychov*