Create app.py

app.py

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+import gradio as gr
+import torch
+import numpy as np
+from rdkit import Chem
+from rdkit.Chem import AllChem, Descriptors
+import py3Dmol
+import json
+import os
+from huggingface_hub import hf_hub_download
+
+# Download Boltz-2 model weights
+def load_boltz_model():
+    """Load the Boltz-2 model from Hugging Face"""
+    try:
+        # Download model files
+        model_path = hf_hub_download(
+            repo_id="boltz-community/boltz-2",
+            filename="pytorch_model.bin",
+            cache_dir="./models"
+        )
+        
+        # Load configuration if available
+        config_path = hf_hub_download(
+            repo_id="boltz-community/boltz-2",
+            filename="config.json",
+            cache_dir="./models"
+        )
+        
+        return model_path, config_path
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None, None
+
+def parse_smiles(smiles_string):
+    """Parse SMILES string and generate 3D coordinates"""
+    try:
+        mol = Chem.MolFromSmiles(smiles_string)
+        if mol is None:
+            return None, "Invalid SMILES string"
+        
+        # Add hydrogens
+        mol = Chem.AddHs(mol)
+        
+        # Generate 3D coordinates
+        AllChem.EmbedMolecule(mol, randomSeed=42)
+        AllChem.MMFFOptimizeMolecule(mol)
+        
+        return mol, None
+    except Exception as e:
+        return None, str(e)
+
+def calculate_descriptors(mol):
+    """Calculate molecular descriptors"""
+    descriptors = {
+        "Molecular Weight": Descriptors.MolWt(mol),
+        "LogP": Descriptors.MolLogP(mol),
+        "H-Bond Donors": Descriptors.NumHDonors(mol),
+        "H-Bond Acceptors": Descriptors.NumHAcceptors(mol),
+        "Rotatable Bonds": Descriptors.NumRotatableBonds(mol),
+        "TPSA": Descriptors.TPSA(mol),
+        "Aromatic Rings": Descriptors.NumAromaticRings(mol)
+    }
+    return descriptors
+
+def visualize_molecule(mol):
+    """Create 3D visualization of molecule"""
+    if mol is None:
+        return None
+    
+    # Convert to PDB format for visualization
+    pdb_block = Chem.MolToPDBBlock(mol)
+    
+    # Create 3D viewer
+    viewer = py3Dmol.view(width=600, height=400)
+    viewer.addModel(pdb_block, "pdb")
+    viewer.setStyle({"stick": {"radius": 0.15}})
+    viewer.setBackgroundColor("white")
+    viewer.zoomTo()
+    
+    return viewer.js()
+
+def predict_structure(protein_sequence):
+    """Predict protein structure using Boltz-2"""
+    # This is a placeholder - actual Boltz-2 implementation would go here
+    # You'll need to implement the actual model inference
+    structure_info = {
+        "status": "Model inference placeholder",
+        "note": "Actual Boltz-2 inference needs to be implemented",
+        "sequence_length": len(protein_sequence)
+    }
+    return structure_info
+
+def analyze_binding(smiles, protein_sequence, binding_site=""):
+    """Analyze potential binding between compound and protein"""
+    results = {"status": "Analysis Started"}
+    
+    # Parse SMILES
+    mol, error = parse_smiles(smiles)
+    if error:
+        return f"Error: {error}", None, None
+    
+    # Calculate molecular properties
+    descriptors = calculate_descriptors(mol)
+    
+    # Get protein structure (placeholder)
+    structure = predict_structure(protein_sequence)
+    
+    # Prepare results
+    results_text = "## Compound Analysis\n\n"
+    results_text += f"**SMILES:** {smiles}\n\n"
+    results_text += "### Molecular Descriptors:\n"
+    for key, value in descriptors.items():
+        results_text += f"- **{key}:** {value:.2f}\n"
+    
+    results_text += "\n## Protein Structure\n"
+    results_text += f"- Sequence Length: {len(protein_sequence)}\n"
+    results_text += f"- Status: {structure['status']}\n"
+    
+    results_text += "\n## Binding Site Analysis\n"
+    if binding_site:
+        results_text += f"- Target Site: {binding_site}\n"
+    else:
+        results_text += "- No specific binding site specified\n"
+    
+    results_text += "\n⚠️ **Note:** This is a demonstration interface. "
+    results_text += "For actual binding affinity predictions, you would need:\n"
+    results_text += "1. Complete Boltz-2 structure prediction implementation\n"
+    results_text += "2. Molecular docking software (AutoDock Vina, etc.)\n"
+    results_text += "3. Binding affinity scoring functions\n"
+    
+    # Create visualization
+    mol_viz = visualize_molecule(mol)
+    
+    return results_text, mol_viz, descriptors
+
+# Create Gradio interface
+def create_interface():
+    with gr.Blocks(title="Boltz-2 Binding Affinity Analyzer") as app:
+        gr.Markdown("""
+        # 🧬 Boltz-2 Binding Affinity Analyzer
+        
+        This tool combines Boltz-2 protein structure prediction with molecular analysis for binding affinity estimation.
+        
+        **Note:** This is a demonstration interface. Full implementation requires:
+        - Complete Boltz-2 model integration
+        - Molecular docking algorithms
+        - Binding affinity scoring functions
+        """)
+        
+        with gr.Tabs():
+            with gr.Tab("Binding Analysis"):
+                with gr.Row():
+                    with gr.Column():
+                        smiles_input = gr.Textbox(
+                            label="Compound SMILES",
+                            placeholder="Enter SMILES notation (e.g., CCCCCCc1cc2OC(C)(C)[C@@H]3CCC(C)C[C@H]3c2c(O)c1)",
+                            value="CCCCCCc1cc2OC(C)(C)[C@@H]3CCC(C)C[C@H]3c2c(O)c1"  # HHCh example
+                        )
+                        
+                        protein_input = gr.Textbox(
+                            label="Protein Sequence",
+                            placeholder="Enter protein sequence in FASTA format",
+                            lines=5
+                        )
+                        
+                        binding_site = gr.Textbox(
+                            label="Binding Site (Optional)",
+                            placeholder="Specify binding site residues or region"
+                        )
+                        
+                        analyze_btn = gr.Button("Analyze Binding", variant="primary")
+                    
+                    with gr.Column():
+                        results_output = gr.Markdown(label="Analysis Results")
+                        mol_viewer = gr.HTML(label="3D Molecule Visualization")
+                
+                with gr.Row():
+                    descriptors_output = gr.JSON(label="Molecular Properties")
+                
+                analyze_btn.click(
+                    fn=analyze_binding,
+                    inputs=[smiles_input, protein_input, binding_site],
+                    outputs=[results_output, mol_viewer, descriptors_output]
+                )
+            
+            with gr.Tab("Batch Analysis"):
+                gr.Markdown("### Batch Processing (Coming Soon)")
+                gr.Markdown("Upload multiple compounds for batch analysis")
+            
+            with gr.Tab("Documentation"):
+                gr.Markdown("""
+                ## How to Use
+                
+                1. **Enter Compound SMILES**: Input the SMILES notation for your compound
+                2. **Enter Protein Sequence**: Provide the target protein sequence
+                3. **Specify Binding Site** (Optional): Define specific binding regions
+                4. **Click Analyze**: Run the binding analysis
+                
+                ## Interpreting Results
+                
+                - **Molecular Descriptors**: Key properties affecting binding
+                - **Lipinski's Rule of Five**: Drug-likeness assessment
+                - **Predicted Binding Affinity**: Estimated binding strength (when fully implemented)
+                
+                ## Limitations
+                
+                - This is a demonstration interface
+                - Actual binding predictions require full model implementation
+                - GPU resources recommended for faster processing
+                """)
+        
+        # Load model on startup
+        gr.Markdown("### Model Status")
+        model_status = gr.Textbox(value="Checking model availability...", interactive=False)
+        
+        def check_model():
+            model_path, config_path = load_boltz_model()
+            if model_path:
+                return "✅ Model loaded successfully"
+            else:
+                return "⚠️ Model not fully loaded - using demo mode"
+        
+        app.load(check_model, outputs=model_status)
+    
+    return app
+
+# Launch the app
+if __name__ == "__main__":
+    app = create_interface()
+    app.launch()