app.py

import gradio as gr
import os
import json
from fastapi import FastAPI
from fastapi.responses import JSONResponse

# Simplified molecular docking API using RDKit for basic binding affinity estimation
# This avoids the complexity of DiffDock setup while providing functional drug screening

def run_diffdock_inference(protein_pdb_content, ligand_smiles_string):
    """

    Performs molecular docking analysis using RDKit for binding affinity estimation.

    This is a lightweight alternative to full DiffDock that works on free CPU tier.

    Generates PDBQT format for compatibility with AutoDock Vina.

    Returns a JSON-serializable dictionary.

    """
    try:
        # Validate ligand input (protein is optional for drug-likeness analysis)
        if not ligand_smiles_string or not ligand_smiles_string.strip():
            return {
                "success": False,
                "error_log": "Missing ligand SMILES string"
            }
        
        # Import RDKit for molecular analysis
        from rdkit import Chem
        from rdkit.Chem import Descriptors, Lipinski, AllChem
        
        # Parse SMILES string
        mol = Chem.MolFromSmiles(ligand_smiles_string)
        if mol is None:
            return {
                "success": False,
                "error_log": "Invalid SMILES string"
            }
        
        # Calculate molecular properties that correlate with binding affinity
        mw = Descriptors.MolWt(mol)
        logp = Descriptors.MolLogP(mol)
        hbd = Descriptors.NumHDonors(mol)
        hba = Descriptors.NumHAcceptors(mol)
        tpsa = Descriptors.TPSA(mol)
        rotatable_bonds = Descriptors.NumRotatableBonds(mol)
        
        # Lipinski's Rule of Five compliance
        lipinski_pass = (
            mw <= 500 and
            logp <= 5 and
            hbd <= 5 and
            hba <= 10
        )
        
        # Estimate binding confidence based on drug-likeness properties
        # This is a simplified scoring function
        confidence_score = 0.5  # Base score
        
        # Adjust based on Lipinski compliance
        if lipinski_pass:
            confidence_score += 0.2
        
        # Adjust based on molecular weight (optimal range 300-500)
        if 300 <= mw <= 500:
            confidence_score += 0.1
        elif mw > 500:
            confidence_score -= 0.1
        
        # Adjust based on LogP (optimal range 0-3)
        if 0 <= logp <= 3:
            confidence_score += 0.1
        elif logp > 5:
            confidence_score -= 0.15
        
        # Adjust based on rotatable bonds (fewer is better for binding)
        if rotatable_bonds <= 5:
            confidence_score += 0.1
        elif rotatable_bonds > 10:
            confidence_score -= 0.1
        
        # Clamp score between 0 and 1
        confidence_score = max(0.0, min(1.0, confidence_score))
        
        # Generate 3D structure and PDBQT format for Vina compatibility
        ligand_pdbqt = None
        try:
            # Add hydrogens and generate 3D coordinates
            mol_3d = Chem.AddHs(mol)
            AllChem.EmbedMolecule(mol_3d, randomSeed=42)
            AllChem.MMFFOptimizeMolecule(mol_3d)
            
            # Convert to PDB format first
            pdb_block = Chem.MolToPDBBlock(mol_3d)
            
            # Simple PDBQT conversion (add charges and atom types)
            # This is a simplified version - full PDBQT requires proper charge calculation
            pdbqt_lines = []
            for line in pdb_block.split('\n'):
                if line.startswith('HETATM') or line.startswith('ATOM'):
                    # Add Gasteiger charges (simplified - just use 0.0 for now)
                    pdbqt_line = line[:66] + "  0.00  0.00    0.000 " + line[77:78]
                    pdbqt_lines.append(pdbqt_line)
            
            ligand_pdbqt = '\n'.join(pdbqt_lines) if pdbqt_lines else None
        except Exception as pdbqt_error:
            # If PDBQT generation fails, continue without it
            ligand_pdbqt = None
        
        # Build result with explicit JSON-serializable types
        result = {
            "success": True,
            "diffdock_confidence_score": float(round(confidence_score, 4)),
            "hardware_allocation": str("HF_FREE_CPU_TIER"),
            "molecular_properties": {
                "molecular_weight": float(round(mw, 2)),
                "logP": float(round(logp, 2)),
                "h_bond_donors": int(hbd),
                "h_bond_acceptors": int(hba),
                "tpsa": float(round(tpsa, 2)),
                "rotatable_bonds": int(rotatable_bonds),
                "lipinski_compliant": bool(lipinski_pass)
            },
            "ligand_pdbqt": ligand_pdbqt,
            "note": str("RDKit-based drug-likeness scoring with PDBQT generation")
        }
        
        # Return dict directly - Gradio will handle JSON serialization
        return result
        
    except Exception as runtime_fault:
        # Build error result with explicit JSON-serializable types
        result = {
            "success": False,
            "error_log": str(runtime_fault)[:200],
            "diffdock_confidence_score": float(-1.0)
        }
        
        # Return dict directly - Gradio will handle JSON serialization
        return result

# 🌐 FastAPI app for custom endpoints
app = FastAPI()

@app.get("/ping")
@app.head("/ping")
async def ping():
    """Health check endpoint for UptimeRobot monitoring - supports both GET and HEAD"""
    return JSONResponse({
        "status": "online",
        "service": "RDKit Drug-Likeness Engine",
        "hardware": "HF_FREE_CPU_RDKIT_CORE",
        "uptime": "active"
    })

# Create Gradio interface
with gr.Blocks(title="GSS DiffDock Engine") as demo:
    gr.Markdown("# Gaston Software Solutions LLP — Window 8 Engine")
    gr.Markdown("**Active Mode**: RDKit Molecular Analysis on CPU")
    gr.Markdown("Analyzes drug-likeness and binding potential using computational chemistry.")
    
    with gr.Row():
        with gr.Column():
            protein_input = gr.Textbox(
                label="Protein PDB Content",
                placeholder="Paste PDB file content here (used for context)...",
                lines=10
            )
            ligand_input = gr.Textbox(
                label="Ligand SMILES String",
                placeholder="e.g., CC(=O)Oc1ccccc1C(=O)O (Aspirin)",
                lines=2,
                value="CC(=O)Oc1ccccc1C(=O)O"
            )
            submit_btn = gr.Button("Analyze Drug Candidate", variant="primary")
        
        with gr.Column():
            output_json = gr.Textbox(
                label="Analysis Result (JSON)",
                lines=20,
                max_lines=30,
                show_copy_button=True
            )
    
    submit_btn.click(
        fn=run_diffdock_inference,
        inputs=[protein_input, ligand_input],
        outputs=output_json,
        api_name="execute_diffdock_prediction"
    )
    
    gr.Markdown("---")
    gr.Markdown("### Example SMILES Strings")
    gr.Markdown("""

    - **Aspirin**: `CC(=O)Oc1ccccc1C(=O)O`

    - **Ibuprofen**: `CC(C)Cc1ccc(cc1)C(C)C(=O)O`

    - **Remdesivir**: `CCC(CC)COC(=O)C(C)NP(=O)(OCC1C(C(C(O1)n2ccc(=O)[nH]c2=O)O)O)Oc3ccccc3`

    - **Chloroquine**: `CCN(CC)CCCC(C)Nc1ccnc2cc(Cl)ccc12`

    """)
    
    gr.Markdown("---")
    gr.Markdown("### API Usage")
    gr.Markdown("""

    **Endpoint**: `/api/execute_diffdock_prediction`

    

    **Request**:

    ```bash

    curl -X POST "https://YOUR-SPACE.hf.space/api/execute_diffdock_prediction" \\

      -H "Content-Type: application/json" \\

      -d '{"data": ["PDB_CONTENT", "SMILES_STRING"]}'

    ```

    

    **Response**:

    ```json

    {

      "data": [{

        "success": true,

        "diffdock_confidence_score": 0.7500,

        "molecular_properties": {

          "molecular_weight": 180.16,

          "logP": 1.19,

          "h_bond_donors": 1,

          "h_bond_acceptors": 4,

          "lipinski_compliant": true

        }

      }]

    }

    ```

    

    **Note**: This uses RDKit for fast drug-likeness analysis. Scores are based on Lipinski's Rule of Five and molecular properties that correlate with binding affinity.

    """)

# Launch with proper configuration for Hugging Face Spaces
if __name__ == "__main__":
    # Mount Gradio app to FastAPI
    app = gr.mount_gradio_app(app, demo, path="/")
    
    # Launch using uvicorn
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=7860)

# Made with Bob