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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() |