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()