app.py

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()