Upload app.py with huggingface_hub

app.py

@@ -0,0 +1,665 @@
+"""
+BioPrime Molecular Docking Demo
+================================
+Interactive demo showcasing AI-powered molecular docking for drug discovery.
+
+Features:
+- 3D protein structure visualization
+- Compound library selection
+- Docking simulation with binding energy results
+- BSV blockchain verification display
+
+Powered by: Origin Neural AI Docking Engine
+Sponsored by: Smartledger Solutions, Origin Neural AI, Bryan Daugherty
+Website: https://bioprime.one
+"""
+
+import gradio as gr
+import py3Dmol
+import requests
+import json
+import hashlib
+import random
+from datetime import datetime
+from typing import Optional, Dict, List, Tuple
+import time
+
+# =============================================================================
+# Configuration
+# =============================================================================
+
+BIOPRIME_API = "https://bioprime.one/api/v1"
+RCSB_PDB_URL = "https://files.rcsb.org/download"
+
+# Demo targets with sponsored research campaigns
+DEMO_TARGETS = [
+    {
+        "id": "melanoma",
+        "name": "BRAF V600E - Melanoma",
+        "pdb": "4MNE",
+        "sponsor": "Bryan Daugherty",
+        "disease": "Melanoma",
+        "description": "Mutated BRAF kinase found in ~50% of melanomas, target for vemurafenib-like inhibitors.",
+        "binding_site": {"x": 25.0, "y": 5.0, "z": 15.0},
+        "color": "#FF6B6B"
+    },
+    {
+        "id": "diabetes",
+        "name": "DPP-4 - Type 2 Diabetes",
+        "pdb": "2ONC",
+        "sponsor": "Bryan Daugherty",
+        "disease": "Type 2 Diabetes",
+        "description": "Dipeptidyl peptidase-4, target for incretin-based diabetes medications like sitagliptin.",
+        "binding_site": {"x": 35.0, "y": 40.0, "z": 45.0},
+        "color": "#4ECDC4"
+    },
+    {
+        "id": "covid",
+        "name": "COVID-19 Main Protease",
+        "pdb": "6LU7",
+        "sponsor": "BioPrime Community",
+        "disease": "COVID-19",
+        "description": "SARS-CoV-2 main protease (Mpro), essential for viral replication. Target for Paxlovid.",
+        "binding_site": {"x": -10.8, "y": 35.2, "z": 63.4},
+        "color": "#9B59B6"
+    },
+    {
+        "id": "hiv",
+        "name": "HIV-1 Protease",
+        "pdb": "1HVR",
+        "sponsor": "Origin Neural AI",
+        "disease": "HIV/AIDS",
+        "description": "Critical enzyme for HIV replication, target for protease inhibitor antiretroviral drugs.",
+        "binding_site": {"x": -6.2, "y": 20.1, "z": 41.8},
+        "color": "#E74C3C"
+    },
+    {
+        "id": "lung",
+        "name": "EGFR Kinase - Lung Cancer",
+        "pdb": "1M17",
+        "sponsor": "Smartledger & Origin Neural AI",
+        "disease": "Non-small Cell Lung Cancer",
+        "description": "Epidermal growth factor receptor, key target in NSCLC therapy. Target for erlotinib.",
+        "binding_site": {"x": 40.5, "y": 0.6, "z": 56.0},
+        "color": "#3498DB"
+    },
+    {
+        "id": "breast",
+        "name": "CDK4/6 - Breast Cancer",
+        "pdb": "5L2I",
+        "sponsor": "Smartledger",
+        "disease": "Breast Cancer",
+        "description": "Cyclin-dependent kinase 4/6 inhibitor target for hormone-receptor positive breast cancer.",
+        "binding_site": {"x": 15.0, "y": 25.0, "z": 35.0},
+        "color": "#E91E63"
+    },
+]
+
+# Demo compound library
+COMPOUND_LIBRARY = [
+    {"id": "aspirin", "name": "Aspirin", "smiles": "CC(=O)OC1=CC=CC=C1C(=O)O", "mw": 180.16, "category": "Anti-inflammatory"},
+    {"id": "ibuprofen", "name": "Ibuprofen", "smiles": "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O", "mw": 206.29, "category": "Anti-inflammatory"},
+    {"id": "caffeine", "name": "Caffeine", "smiles": "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "mw": 194.19, "category": "Stimulant"},
+    {"id": "paracetamol", "name": "Acetaminophen", "smiles": "CC(=O)NC1=CC=C(O)C=C1", "mw": 151.16, "category": "Analgesic"},
+    {"id": "metformin", "name": "Metformin", "smiles": "CN(C)C(=N)NC(=N)N", "mw": 129.17, "category": "Antidiabetic"},
+    {"id": "atorvastatin", "name": "Atorvastatin", "smiles": "CC(C)C1=C(C(=C(N1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4", "mw": 558.64, "category": "Statin"},
+    {"id": "nirmatrelvir", "name": "Nirmatrelvir", "smiles": "CC1(CC1)C(=O)NC(CC2CCNC2=O)C(=O)NC(CC(F)(F)F)C#N", "mw": 499.53, "category": "Antiviral (COVID-19)"},
+    {"id": "oseltamivir", "name": "Oseltamivir", "smiles": "CCOC(=O)C1=CC(OC(CC)CC)C(NC(C)=O)C(N)C1", "mw": 312.41, "category": "Antiviral (Flu)"},
+    {"id": "remdesivir", "name": "Remdesivir", "smiles": "CCC(CC)COC(=O)C(C)NP(=O)(OCC1C(C(C(O1)N2C=CC(=O)NC2=O)O)O)OC3=CC=CC=C3", "mw": 602.58, "category": "Antiviral"},
+    {"id": "sitagliptin", "name": "Sitagliptin", "smiles": "NC(CC(=O)N1CCN2C(C1)=NN=C2C(F)(F)F)CC1=C(F)C=C(F)C(F)=C1F", "mw": 407.31, "category": "Antidiabetic (DPP-4)"},
+    {"id": "vemurafenib", "name": "Vemurafenib", "smiles": "CCCS(=O)(=O)NC1=CC=C(C=C1)C2=NC(=C(S2)C3=CC(=NC=C3)NC4=CC=C(C=C4)Cl)C#N", "mw": 489.93, "category": "Kinase Inhibitor (Melanoma)"},
+    {"id": "erlotinib", "name": "Erlotinib", "smiles": "COCCOC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)OCCOC", "mw": 393.44, "category": "EGFR Inhibitor"},
+]
+
+# Pre-computed docking results (simulated but realistic)
+PRECOMPUTED_RESULTS = {
+    "melanoma": {
+        "vemurafenib": -9.8,
+        "erlotinib": -7.2,
+        "caffeine": -4.1,
+        "aspirin": -5.3,
+        "ibuprofen": -5.8,
+    },
+    "diabetes": {
+        "sitagliptin": -10.2,
+        "metformin": -6.8,
+        "caffeine": -4.5,
+        "aspirin": -4.9,
+        "ibuprofen": -5.1,
+    },
+    "covid": {
+        "nirmatrelvir": -8.9,
+        "remdesivir": -7.6,
+        "caffeine": -5.2,
+        "aspirin": -4.8,
+        "oseltamivir": -6.4,
+    },
+    "hiv": {
+        "remdesivir": -7.8,
+        "oseltamivir": -6.2,
+        "caffeine": -4.3,
+        "aspirin": -4.5,
+        "atorvastatin": -6.9,
+    },
+    "lung": {
+        "erlotinib": -9.4,
+        "vemurafenib": -7.1,
+        "caffeine": -4.0,
+        "aspirin": -4.7,
+        "atorvastatin": -6.5,
+    },
+    "breast": {
+        "atorvastatin": -7.3,
+        "erlotinib": -6.8,
+        "caffeine": -4.2,
+        "aspirin": -4.4,
+        "metformin": -5.1,
+    },
+}
+
+# =============================================================================
+# Helper Functions
+# =============================================================================
+
+def fetch_pdb_structure(pdb_id: str) -> Optional[str]:
+    """Fetch PDB structure from RCSB."""
+    try:
+        # Try BioPrime API first
+        response = requests.get(f"{BIOPRIME_API}/docking/demo/pdb/{pdb_id}", timeout=10)
+        if response.status_code == 200:
+            data = response.json()
+            return data.get("pdb_content", data.get("pdb_data", None))
+    except:
+        pass
+
+    # Fallback to RCSB
+    try:
+        response = requests.get(f"{RCSB_PDB_URL}/{pdb_id}.pdb", timeout=10)
+        if response.status_code == 200:
+            return response.text
+    except:
+        pass
+
+    return None
+
+
+def create_3d_viewer(pdb_content: str, binding_site: dict = None, style: str = "cartoon") -> str:
+    """Create 3D molecular viewer HTML."""
+    view = py3Dmol.view(width=700, height=500)
+    view.addModel(pdb_content, "pdb")
+
+    if style == "cartoon":
+        view.setStyle({'cartoon': {'color': 'spectrum'}})
+    elif style == "surface":
+        view.setStyle({'cartoon': {'color': 'spectrum'}})
+        view.addSurface(py3Dmol.SAS, {'opacity': 0.7, 'color': 'white'})
+    elif style == "stick":
+        view.setStyle({'stick': {'colorscheme': 'Jmol'}})
+    elif style == "sphere":
+        view.setStyle({'sphere': {'colorscheme': 'Jmol', 'scale': 0.3}})
+
+    # Highlight binding site if provided
+    if binding_site:
+        view.addSphere({
+            'center': {'x': binding_site['x'], 'y': binding_site['y'], 'z': binding_site['z']},
+            'radius': 8,
+            'color': 'red',
+            'opacity': 0.3
+        })
+
+    view.zoomTo()
+    view.spin(False)
+
+    return view._make_html()
+
+
+def generate_docking_result(target_id: str, compound_ids: List[str]) -> Tuple[str, str, str]:
+    """
+    Simulate docking and return results.
+    Returns: (results_text, binding_chart_data, receipt)
+    """
+    target = next((t for t in DEMO_TARGETS if t["id"] == target_id), None)
+    if not target:
+        return "Target not found", "", ""
+
+    results = []
+    precomputed = PRECOMPUTED_RESULTS.get(target_id, {})
+
+    for comp_id in compound_ids:
+        compound = next((c for c in COMPOUND_LIBRARY if c["id"] == comp_id), None)
+        if not compound:
+            continue
+
+        # Use precomputed or generate realistic random
+        if comp_id in precomputed:
+            energy = precomputed[comp_id]
+        else:
+            # Generate realistic binding energy based on molecular weight
+            base_energy = -4.0 - (compound["mw"] / 100)
+            energy = round(base_energy + random.uniform(-1.5, 1.5), 2)
+
+        results.append({
+            "compound": compound["name"],
+            "smiles": compound["smiles"],
+            "energy": energy,
+            "mw": compound["mw"],
+            "category": compound["category"]
+        })
+
+    # Sort by binding energy (more negative = better)
+    results.sort(key=lambda x: x["energy"])
+
+    # Generate results text
+    results_text = f"""
+## Docking Results for {target['name']}
+
+**Target Disease:** {target['disease']}
+**Sponsor:** {target['sponsor']}
+**PDB ID:** {target['pdb']}
+
+---
+
+### Top Binding Compounds
+
+| Rank | Compound | Binding Energy | Category |
+|------|----------|----------------|----------|
+"""
+
+    for i, r in enumerate(results[:10], 1):
+        emoji = "🏆" if i == 1 else "🥈" if i == 2 else "🥉" if i == 3 else "  "
+        results_text += f"| {emoji} {i} | **{r['compound']}** | {r['energy']:.2f} kcal/mol | {r['category']} |\n"
+
+    results_text += f"""
+---
+
+### Interpretation
+
+- **Best Hit:** {results[0]['compound']} with {results[0]['energy']:.2f} kcal/mol
+- **Binding energies < -7 kcal/mol** indicate strong binding potential
+- **Binding energies < -9 kcal/mol** suggest drug-like affinity
+
+---
+
+*Powered by Origin Neural AI Docking Engine*
+*Results simulated for demonstration - actual BioPrime uses GPU-accelerated physics*
+"""
+
+    # Generate chart data
+    chart_labels = [r["compound"][:12] for r in results[:8]]
+    chart_values = [abs(r["energy"]) for r in results[:8]]
+
+    chart_html = f"""
+<div style="background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%); padding: 20px; border-radius: 12px; margin-top: 10px;">
+    <h3 style="color: #4ECDC4; margin-bottom: 15px; text-align: center;">Binding Affinity Comparison</h3>
+    <div style="display: flex; align-items: flex-end; justify-content: space-around; height: 200px; padding: 10px;">
+"""
+
+    max_val = max(chart_values) if chart_values else 1
+    colors = ["#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4", "#FFEAA7", "#DDA0DD", "#98D8C8", "#F7DC6F"]
+
+    for i, (label, val) in enumerate(zip(chart_labels, chart_values)):
+        height = int((val / max_val) * 150)
+        color = colors[i % len(colors)]
+        chart_html += f"""
+        <div style="display: flex; flex-direction: column; align-items: center; width: 60px;">
+            <span style="color: white; font-size: 11px; margin-bottom: 5px;">-{val:.1f}</span>
+            <div style="width: 40px; height: {height}px; background: {color}; border-radius: 4px 4px 0 0;"></div>
+            <span style="color: #888; font-size: 9px; margin-top: 5px; text-align: center; word-wrap: break-word; width: 55px;">{label}</span>
+        </div>
+"""
+
+    chart_html += """
+    </div>
+    <p style="color: #666; font-size: 11px; text-align: center; margin-top: 10px;">Binding Energy (kcal/mol) - Higher bars = stronger binding</p>
+</div>
+"""
+
+    # Generate blockchain receipt
+    job_id = f"DEMO-{target_id.upper()}-{hashlib.md5(str(compound_ids).encode()).hexdigest()[:8]}"
+    data_hash = hashlib.sha256(json.dumps(results, sort_keys=True).encode()).hexdigest()
+
+    receipt = generate_demo_receipt(
+        job_id=job_id,
+        target_name=target['name'],
+        compounds_screened=len(compound_ids),
+        top_hits=len(results),
+        best_energy=results[0]['energy'] if results else 0,
+        data_hash=data_hash
+    )
+
+    return results_text, chart_html, receipt
+
+
+def generate_demo_receipt(job_id: str, target_name: str, compounds_screened: int,
+                          top_hits: int, best_energy: float, data_hash: str) -> str:
+    """Generate a demo blockchain receipt."""
+    timestamp = datetime.utcnow().strftime("%Y-%m-%d %H:%M:%S UTC")
+    demo_txid = f"demo_{hashlib.md5(data_hash.encode()).hexdigest()[:48]}"
+
+    receipt = f"""
+╔══════════════════════════════════════════════════════════════════════════╗
+║                    BIOPRIME DISCOVERY CERTIFICATE                        ║
+║                         [DEMO - NOT ON CHAIN]                            ║
+╠══════════════════════════════════════════════════════════════════════════╣
+║                                                                          ║
+║  JOB ID:      {job_id:<57} ║
+║  TARGET:      {target_name[:47]:<57} ║
+║                                                                          ║
+╠══════════════════════════════════════════════════════════════════════════╣
+║                          SCREENING RESULTS                               ║
+╠══════════════════════════════════════════════════════════════════════════╣
+║                                                                          ║
+║  Compounds Screened:     {compounds_screened:<45} ║
+║  Top Poses Generated:    {top_hits:<45} ║
+║  Best Binding Energy:    {best_energy:.2f} kcal/mol{' ':<36} ║
+║                                                                          ║
+╠══════════════════════════════════════════════════════════════════════════╣
+║                        VERIFICATION DETAILS                              ║
+╠══════════════════════════════════════════════════════════════════════════╣
+║                                                                          ║
+║  Timestamp:      {timestamp:<53} ║
+║  Data Hash:      sha256:{data_hash[:49]:<46} ║
+║                                                                          ║
+╠══════════════════════════════════════════════════════════════════════════╣
+║                       BLOCKCHAIN VERIFICATION                            ║
+╠══════════════════════════════════════════════════════════════════════════╣
+║                                                                          ║
+║  Network:        BSV (Bitcoin SV) - Demo Mode                            ║
+║  Status:         Demo certificate - not anchored to blockchain           ║
+║                                                                          ║
+║  For real blockchain-verified results, visit:                            ║
+║  https://bioprime.one                                                    ║
+║                                                                          ║
+╠══════════════════════════════════════════════════════════════════════════╣
+║                                                                          ║
+║  BioPrime anchors real docking results to the BSV blockchain for         ║
+║  immutable proof of discovery. Sign up to run verified experiments.      ║
+║                                                                          ║
+║                         ━━━ bioprime.one ━━━                              ║
+║                                                                          ║
+╚══════════════════════════════════════════════════════════════════════════╝
+"""
+    return receipt.strip()
+
+
+# =============================================================================
+# Gradio Interface
+# =============================================================================
+
+def view_protein(target_name: str, view_style: str) -> Tuple[str, str]:
+    """View selected protein structure."""
+    target = next((t for t in DEMO_TARGETS if t["name"] == target_name), None)
+    if not target:
+        return "<p>Please select a target</p>", ""
+
+    pdb_content = fetch_pdb_structure(target["pdb"])
+    if not pdb_content:
+        return f"<p style='color: red;'>Failed to fetch PDB structure for {target['pdb']}</p>", ""
+
+    viewer_html = create_3d_viewer(pdb_content, target.get("binding_site"), view_style.lower())
+
+    info_html = f"""
+<div style="background: linear-gradient(135deg, #0f0c29 0%, #302b63 50%, #24243e 100%); padding: 20px; border-radius: 12px; color: white;">
+    <h3 style="color: {target['color']}; margin-bottom: 10px;">{target['name']}</h3>
+    <p><strong>PDB ID:</strong> <a href="https://www.rcsb.org/structure/{target['pdb']}" target="_blank" style="color: #4ECDC4;">{target['pdb']}</a></p>
+    <p><strong>Disease:</strong> {target['disease']}</p>
+    <p><strong>Sponsor:</strong> {target['sponsor']}</p>
+    <p style="margin-top: 10px; color: #aaa;">{target['description']}</p>
+    <div style="margin-top: 15px; padding: 10px; background: rgba(78, 205, 196, 0.1); border-radius: 8px; border-left: 3px solid #4ECDC4;">
+        <p style="font-size: 12px; color: #4ECDC4; margin: 0;">
+            💡 The red sphere indicates the active binding site where drug candidates interact with the protein.
+        </p>
+    </div>
+</div>
+"""
+
+    return viewer_html, info_html
+
+
+def run_docking(target_name: str, compounds: List[str], progress=gr.Progress()) -> Tuple[str, str, str]:
+    """Run docking simulation."""
+    if not target_name:
+        return "Please select a target protein", "", ""
+    if not compounds:
+        return "Please select at least one compound", "", ""
+
+    target = next((t for t in DEMO_TARGETS if t["name"] == target_name), None)
+    if not target:
+        return "Invalid target", "", ""
+
+    # Get compound IDs from names
+    compound_ids = []
+    for comp_name in compounds:
+        comp = next((c for c in COMPOUND_LIBRARY if c["name"] == comp_name), None)
+        if comp:
+            compound_ids.append(comp["id"])
+
+    # Simulate docking progress
+    progress(0, desc="Initializing docking engine...")
+    time.sleep(0.5)
+
+    progress(0.2, desc="Loading protein structure...")
+    time.sleep(0.3)
+
+    progress(0.4, desc="Preparing compound library...")
+    time.sleep(0.3)
+
+    progress(0.6, desc="Running molecular docking...")
+    time.sleep(0.5)
+
+    progress(0.8, desc="Analyzing binding poses...")
+    time.sleep(0.3)
+
+    progress(0.95, desc="Generating results...")
+    results_text, chart_html, receipt = generate_docking_result(target["id"], compound_ids)
+
+    progress(1.0, desc="Complete!")
+
+    return results_text, chart_html, receipt
+
+
+# Create the Gradio interface
+with gr.Blocks(
+    title="BioPrime Molecular Docking Demo",
+    theme=gr.themes.Base(
+        primary_hue="teal",
+        secondary_hue="purple",
+        neutral_hue="slate",
+        font=gr.themes.GoogleFont("Inter")
+    ),
+    css="""
+    .gradio-container {
+        max-width: 1400px !important;
+        background: linear-gradient(135deg, #0f0c29 0%, #302b63 50%, #24243e 100%) !important;
+    }
+    .gr-button-primary {
+        background: linear-gradient(135deg, #4ECDC4 0%, #44A08D 100%) !important;
+    }
+    .header-text {
+        text-align: center;
+        color: white;
+    }
+    footer {display: none !important;}
+    """
+) as demo:
+
+    # Header
+    gr.HTML("""
+    <div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 12px; margin-bottom: 20px;">
+        <h1 style="color: white; margin: 0; font-size: 2.5em;">🧬 BioPrime</h1>
+        <p style="color: rgba(255,255,255,0.9); margin: 10px 0 0 0; font-size: 1.2em;">
+            AI-Powered Molecular Docking for Drug Discovery
+        </p>
+        <p style="color: rgba(255,255,255,0.7); margin: 5px 0 0 0;">
+            10,000x Faster • $5 per Million Compounds • Blockchain-Verified Results
+        </p>
+    </div>
+    """)
+
+    with gr.Tabs():
+        # Tab 1: Interactive Docking Demo
+        with gr.TabItem("🔬 Try Docking", id="docking"):
+            gr.Markdown("""
+            ### Dock Drug Candidates Against Disease Targets
+            Select a protein target and compounds to simulate molecular docking. See binding energies and get a blockchain-ready certificate.
+            """)
+
+            with gr.Row():
+                with gr.Column(scale=1):
+                    target_dropdown = gr.Dropdown(
+                        choices=[t["name"] for t in DEMO_TARGETS],
+                        label="🎯 Select Disease Target",
+                        info="Choose from sponsored research targets"
+                    )
+
+                    compound_select = gr.CheckboxGroup(
+                        choices=[c["name"] for c in COMPOUND_LIBRARY],
+                        label="💊 Select Compounds to Test",
+                        info="Choose multiple compounds for screening"
+                    )
+
+                    dock_btn = gr.Button("🚀 Run Docking Simulation", variant="primary", size="lg")
+
+                    gr.HTML("""
+                    <div style="margin-top: 15px; padding: 15px; background: rgba(78, 205, 196, 0.1); border-radius: 8px; border: 1px solid rgba(78, 205, 196, 0.3);">
+                        <h4 style="color: #4ECDC4; margin: 0 0 10px 0;">💡 Quick Start</h4>
+                        <ol style="color: #aaa; margin: 0; padding-left: 20px; font-size: 13px;">
+                            <li>Select <strong>BRAF V600E - Melanoma</strong></li>
+                            <li>Check <strong>Vemurafenib</strong> (the actual drug!)</li>
+                            <li>Add a few other compounds to compare</li>
+                            <li>Click Run Docking</li>
+                        </ol>
+                    </div>
+                    """)
+
+                with gr.Column(scale=2):
+                    results_md = gr.Markdown("*Results will appear here after docking...*")
+                    chart_html = gr.HTML()
+
+            with gr.Accordion("📜 Blockchain Certificate (Demo)", open=False):
+                receipt_text = gr.Code(label="Discovery Certificate", language=None, lines=30)
+
+            dock_btn.click(
+                fn=run_docking,
+                inputs=[target_dropdown, compound_select],
+                outputs=[results_md, chart_html, receipt_text]
+            )
+
+        # Tab 2: 3D Protein Viewer
+        with gr.TabItem("🔮 3D Protein Viewer", id="viewer"):
+            gr.Markdown("""
+            ### Explore Protein Structures in 3D
+            Visualize the molecular targets for drug discovery. The red sphere indicates the binding site.
+            """)
+
+            with gr.Row():
+                with gr.Column(scale=1):
+                    viewer_target = gr.Dropdown(
+                        choices=[t["name"] for t in DEMO_TARGETS],
+                        label="🎯 Select Protein",
+                        value=DEMO_TARGETS[0]["name"]
+                    )
+
+                    view_style = gr.Radio(
+                        choices=["Cartoon", "Surface", "Stick", "Sphere"],
+                        value="Cartoon",
+                        label="🎨 Visualization Style"
+                    )
+
+                    view_btn = gr.Button("👁️ View Structure", variant="primary")
+
+                    target_info = gr.HTML()
+
+                with gr.Column(scale=2):
+                    viewer_output = gr.HTML(
+                        value="<div style='height: 500px; display: flex; align-items: center; justify-content: center; color: #666; background: #1a1a2e; border-radius: 12px;'><p>Select a protein and click 'View Structure'</p></div>"
+                    )
+
+            view_btn.click(
+                fn=view_protein,
+                inputs=[viewer_target, view_style],
+                outputs=[viewer_output, target_info]
+            )
+
+        # Tab 3: About BioPrime
+        with gr.TabItem("ℹ️ About", id="about"):
+            gr.Markdown("""
+            ## About BioPrime Network
+
+            BioPrime is a **decentralized molecular docking platform** that makes drug discovery accessible to everyone.
+
+            ### Key Features
+
+            | Feature | Traditional | BioPrime |
+            |---------|-------------|----------|
+            | Speed | Days-Weeks | Minutes |
+            | Cost | $10,000+ | $5/million |
+            | Verification | Manual | Blockchain |
+            | Access | Limited | Open |
+
+            ### How It Works
+
+            1. **Submit** - Upload your protein target or select from our library
+            2. **Screen** - Our Origin Neural AI engine docks millions of compounds
+            3. **Discover** - Get ranked binding poses with energy scores
+            4. **Verify** - Results anchored to BSV blockchain for immutable proof
+
+            ### Sponsored Research Campaigns
+
+            BioPrime features sponsored research targets where community members can contribute to drug discovery:
+
+            - **Bryan Daugherty** - Melanoma (BRAF V600E), Type 2 Diabetes (DPP-4)
+            - **Smartledger** - Breast Cancer (CDK4/6)
+            - **Origin Neural AI** - HIV-1 Protease
+            - **Greg Ward** - Tuberculosis, Dengue Fever
+            - **Shawn Ryan** - Alzheimer's, Parkinson's
+
+            ### Technology Stack
+
+            - **Docking Engine**: Origin Neural AI (GPU-accelerated)
+            - **Blockchain**: BSV (Bitcoin SV) for immutable verification
+            - **Backend**: FastAPI, Python
+            - **Frontend**: React, TypeScript
+
+            ---
+
+            ### Get Started
+
+            **🌐 Visit [bioprime.one](https://bioprime.one) to run real docking experiments!**
+
+            - Sign up for free (10 free credits)
+            - Screen up to 1 million compounds per job
+            - Get blockchain-verified discovery certificates
+            - Participate in sponsored research campaigns
+
+            ---
+
+            <div style="text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 12px;">
+                <p style="color: white; font-size: 1.2em; margin: 0;">
+                    <strong>Ready to discover the next breakthrough drug?</strong>
+                </p>
+                <p style="color: rgba(255,255,255,0.8); margin: 10px 0 0 0;">
+                    <a href="https://bioprime.one" target="_blank" style="color: #4ECDC4; text-decoration: none; font-size: 1.3em;">
+                        🚀 Launch BioPrime →
+                    </a>
+                </p>
+            </div>
+            """)
+
+    # Footer
+    gr.HTML("""
+    <div style="text-align: center; padding: 20px; margin-top: 20px; border-top: 1px solid rgba(255,255,255,0.1);">
+        <p style="color: #666; margin: 0;">
+            <a href="https://bioprime.one" target="_blank" style="color: #4ECDC4;">bioprime.one</a> •
+            <a href="https://twitter.com/BioPrimeNetwork" target="_blank" style="color: #4ECDC4;">@BioPrimeNetwork</a> •
+            <a href="https://github.com/bioprime-network" target="_blank" style="color: #4ECDC4;">GitHub</a>
+        </p>
+        <p style="color: #444; margin: 5px 0 0 0; font-size: 12px;">
+            Powered by Origin Neural AI • Blockchain verification on BSV
+        </p>
+    </div>
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch()