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"""
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BioPrime Molecular Docking Demo
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================================
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Interactive demo showcasing AI-powered molecular docking for drug discovery.
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Features:
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- 3D protein structure visualization
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- Compound library selection
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- Docking simulation with binding energy results
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- BSV blockchain verification display
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Powered by: Origin Neural AI Docking Engine
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Sponsored by: Smartledger Solutions, Origin Neural AI, Bryan Daugherty
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Website: https://bioprime.one
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"""
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import gradio as gr
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import requests
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import json
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import hashlib
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import random
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from datetime import datetime
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from typing import Optional, Dict, List, Tuple
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import time
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BIOPRIME_API = "https://bioprime.one/api/v1"
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RCSB_PDB_URL = "https://files.rcsb.org/download"
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DEMO_TARGETS = [
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{
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"id": "melanoma",
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"name": "BRAF V600E - Melanoma",
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"pdb": "4MNE",
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"sponsor": "Bryan Daugherty",
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"disease": "Melanoma",
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"description": "Mutated BRAF kinase found in ~50% of melanomas, target for vemurafenib-like inhibitors.",
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"binding_site": {"x": 25.0, "y": 5.0, "z": 15.0},
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"color": "#FF6B6B"
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},
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{
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"id": "diabetes",
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"name": "DPP-4 - Type 2 Diabetes",
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"pdb": "2ONC",
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"sponsor": "Bryan Daugherty",
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"disease": "Type 2 Diabetes",
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"description": "Dipeptidyl peptidase-4, target for incretin-based diabetes medications like sitagliptin.",
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"binding_site": {"x": 35.0, "y": 40.0, "z": 45.0},
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"color": "#4ECDC4"
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},
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{
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"id": "covid",
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"name": "COVID-19 Main Protease",
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"pdb": "6LU7",
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"sponsor": "BioPrime Community",
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"disease": "COVID-19",
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"description": "SARS-CoV-2 main protease (Mpro), essential for viral replication. Target for Paxlovid.",
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"binding_site": {"x": -10.8, "y": 35.2, "z": 63.4},
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"color": "#9B59B6"
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},
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{
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"id": "hiv",
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"name": "HIV-1 Protease",
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"pdb": "1HVR",
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"sponsor": "Origin Neural AI",
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"disease": "HIV/AIDS",
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"description": "Critical enzyme for HIV replication, target for protease inhibitor antiretroviral drugs.",
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"binding_site": {"x": -6.2, "y": 20.1, "z": 41.8},
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"color": "#E74C3C"
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},
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{
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"id": "lung",
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"name": "EGFR Kinase - Lung Cancer",
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"pdb": "1M17",
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"sponsor": "Smartledger & Origin Neural AI",
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"disease": "Non-small Cell Lung Cancer",
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"description": "Epidermal growth factor receptor, key target in NSCLC therapy. Target for erlotinib.",
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"binding_site": {"x": 40.5, "y": 0.6, "z": 56.0},
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"color": "#3498DB"
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},
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{
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"id": "breast",
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"name": "CDK4/6 - Breast Cancer",
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"pdb": "5L2I",
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"sponsor": "Smartledger",
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"disease": "Breast Cancer",
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"description": "Cyclin-dependent kinase 4/6 inhibitor target for hormone-receptor positive breast cancer.",
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"binding_site": {"x": 15.0, "y": 25.0, "z": 35.0},
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"color": "#E91E63"
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},
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]
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COMPOUND_LIBRARY = [
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{"id": "aspirin", "name": "Aspirin", "smiles": "CC(=O)OC1=CC=CC=C1C(=O)O", "mw": 180.16, "category": "Anti-inflammatory"},
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{"id": "ibuprofen", "name": "Ibuprofen", "smiles": "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O", "mw": 206.29, "category": "Anti-inflammatory"},
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{"id": "caffeine", "name": "Caffeine", "smiles": "CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "mw": 194.19, "category": "Stimulant"},
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{"id": "paracetamol", "name": "Acetaminophen", "smiles": "CC(=O)NC1=CC=C(O)C=C1", "mw": 151.16, "category": "Analgesic"},
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{"id": "metformin", "name": "Metformin", "smiles": "CN(C)C(=N)NC(=N)N", "mw": 129.17, "category": "Antidiabetic"},
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{"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"},
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{"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)"},
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{"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)"},
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{"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"},
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{"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)"},
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{"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)"},
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{"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"},
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|
|
]
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|
|
PRECOMPUTED_RESULTS = {
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|
|
"melanoma": {
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|
|
"vemurafenib": -9.8,
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"erlotinib": -7.2,
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"caffeine": -4.1,
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|
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"aspirin": -5.3,
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|
|
"ibuprofen": -5.8,
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|
|
},
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|
|
"diabetes": {
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|
|
"sitagliptin": -10.2,
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|
|
"metformin": -6.8,
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"caffeine": -4.5,
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|
|
"aspirin": -4.9,
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|
"ibuprofen": -5.1,
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},
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|
"covid": {
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"nirmatrelvir": -8.9,
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|
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"remdesivir": -7.6,
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"caffeine": -5.2,
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"aspirin": -4.8,
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"oseltamivir": -6.4,
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},
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"hiv": {
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"remdesivir": -7.8,
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"oseltamivir": -6.2,
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"caffeine": -4.3,
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"aspirin": -4.5,
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"atorvastatin": -6.9,
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},
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"lung": {
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"erlotinib": -9.4,
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|
"vemurafenib": -7.1,
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"caffeine": -4.0,
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"aspirin": -4.7,
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|
"atorvastatin": -6.5,
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},
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"breast": {
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"atorvastatin": -7.3,
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|
"erlotinib": -6.8,
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"caffeine": -4.2,
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"aspirin": -4.4,
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|
"metformin": -5.1,
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},
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|
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}
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def fetch_pdb_structure(pdb_id: str) -> Optional[str]:
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|
"""Fetch PDB structure from RCSB."""
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|
try:
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|
response = requests.get(f"{BIOPRIME_API}/docking/demo/pdb/{pdb_id}", timeout=10)
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|
if response.status_code == 200:
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|
|
data = response.json()
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|
return data.get("pdb_content", data.get("pdb_data", None))
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|
except:
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|
|
pass
|
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|
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|
try:
|
|
|
response = requests.get(f"{RCSB_PDB_URL}/{pdb_id}.pdb", timeout=10)
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|
if response.status_code == 200:
|
|
|
return response.text
|
|
|
except:
|
|
|
pass
|
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|
|
return None
|
|
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|
|
|
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|
|
def create_3d_viewer(pdb_content: str, binding_site: dict = None, style: str = "cartoon") -> str:
|
|
|
"""Create 3D molecular viewer HTML using iframe with srcdoc for JS execution."""
|
|
|
import html
|
|
|
import base64
|
|
|
|
|
|
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|
|
pdb_escaped = pdb_content.replace('\\', '\\\\').replace('\n', '\\n').replace('\r', '').replace("'", "\\'").replace('"', '\\"')
|
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|
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|
|
if style == "cartoon":
|
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|
style_js = "viewer.setStyle({}, {cartoon: {color: 'spectrum'}});"
|
|
|
elif style == "surface":
|
|
|
style_js = "viewer.setStyle({}, {cartoon: {color: 'spectrum'}}); viewer.addSurface($3Dmol.SAS, {opacity: 0.7, color: 'white'});"
|
|
|
elif style == "stick":
|
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|
style_js = "viewer.setStyle({}, {stick: {colorscheme: 'Jmol'}});"
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|
elif style == "sphere":
|
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|
style_js = "viewer.setStyle({}, {sphere: {colorscheme: 'Jmol', scale: 0.3}});"
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else:
|
|
|
style_js = "viewer.setStyle({}, {cartoon: {color: 'spectrum'}});"
|
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|
|
binding_site_js = ""
|
|
|
if binding_site:
|
|
|
binding_site_js = f"viewer.addSphere({{center: {{x: {binding_site['x']}, y: {binding_site['y']}, z: {binding_site['z']}}}, radius: 8, color: 'red', opacity: 0.3}});"
|
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|
|
iframe_html = f'''<!DOCTYPE html>
|
|
|
<html>
|
|
|
<head>
|
|
|
<script src="https://3dmol.org/build/3Dmol-min.js"></script>
|
|
|
<style>
|
|
|
body {{ margin: 0; padding: 0; overflow: hidden; background: #1a1a2e; }}
|
|
|
#viewer {{ width: 100%; height: 100%; }}
|
|
|
</style>
|
|
|
</head>
|
|
|
<body>
|
|
|
<div id="viewer"></div>
|
|
|
<script>
|
|
|
document.addEventListener('DOMContentLoaded', function() {{
|
|
|
var pdbData = "{pdb_escaped}";
|
|
|
var element = document.getElementById('viewer');
|
|
|
var config = {{ backgroundColor: '0x1a1a2e' }};
|
|
|
var viewer = $3Dmol.createViewer(element, config);
|
|
|
viewer.addModel(pdbData, "pdb");
|
|
|
{style_js}
|
|
|
{binding_site_js}
|
|
|
viewer.zoomTo();
|
|
|
viewer.render();
|
|
|
}});
|
|
|
</script>
|
|
|
</body>
|
|
|
</html>'''
|
|
|
|
|
|
|
|
|
iframe_srcdoc = html.escape(iframe_html)
|
|
|
|
|
|
html_content = f'''
|
|
|
<div style="width: 100%; display: flex; justify-content: center;">
|
|
|
<iframe srcdoc="{iframe_srcdoc}"
|
|
|
style="width: 700px; height: 500px; border: none; border-radius: 12px; background: #1a1a2e;"
|
|
|
sandbox="allow-scripts allow-same-origin">
|
|
|
</iframe>
|
|
|
</div>
|
|
|
'''
|
|
|
|
|
|
return html_content
|
|
|
|
|
|
|
|
|
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
|
|
|
|
|
|
|
|
|
if comp_id in precomputed:
|
|
|
energy = precomputed[comp_id]
|
|
|
else:
|
|
|
|
|
|
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"]
|
|
|
})
|
|
|
|
|
|
|
|
|
results.sort(key=lambda x: x["energy"])
|
|
|
|
|
|
|
|
|
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*
|
|
|
"""
|
|
|
|
|
|
|
|
|
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>
|
|
|
"""
|
|
|
|
|
|
|
|
|
share_text = f"I just screened compounds against {target['name']} using BioPrime! Best hit: {results[0]['compound']} at {results[0]['energy']:.2f} kcal/mol. Try AI-powered drug discovery:"
|
|
|
share_url = "https://bioprime.one"
|
|
|
|
|
|
import urllib.parse
|
|
|
encoded_text = urllib.parse.quote(share_text)
|
|
|
encoded_url = urllib.parse.quote(share_url)
|
|
|
|
|
|
chart_html += f"""
|
|
|
<div style="background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%); padding: 15px; border-radius: 12px; margin-top: 10px;">
|
|
|
<p style="color: #4ECDC4; font-size: 13px; text-align: center; margin-bottom: 12px; font-weight: 600;">Share Your Discovery</p>
|
|
|
<div style="display: flex; justify-content: center; gap: 12px; flex-wrap: wrap;">
|
|
|
<a href="https://twitter.com/intent/tweet?text={encoded_text}&url={encoded_url}" target="_blank"
|
|
|
style="display: inline-flex; align-items: center; gap: 6px; padding: 8px 16px; background: #1DA1F2; color: white; text-decoration: none; border-radius: 6px; font-size: 13px; font-weight: 500;">
|
|
|
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor"><path d="M18.244 2.25h3.308l-7.227 8.26 8.502 11.24H16.17l-5.214-6.817L4.99 21.75H1.68l7.73-8.835L1.254 2.25H8.08l4.713 6.231zm-1.161 17.52h1.833L7.084 4.126H5.117z"/></svg>
|
|
|
Post on X
|
|
|
</a>
|
|
|
<a href="https://www.linkedin.com/sharing/share-offsite/?url={encoded_url}" target="_blank"
|
|
|
style="display: inline-flex; align-items: center; gap: 6px; padding: 8px 16px; background: #0A66C2; color: white; text-decoration: none; border-radius: 6px; font-size: 13px; font-weight: 500;">
|
|
|
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor"><path d="M20.447 20.452h-3.554v-5.569c0-1.328-.027-3.037-1.852-3.037-1.853 0-2.136 1.445-2.136 2.939v5.667H9.351V9h3.414v1.561h.046c.477-.9 1.637-1.85 3.37-1.85 3.601 0 4.267 2.37 4.267 5.455v6.286zM5.337 7.433c-1.144 0-2.063-.926-2.063-2.065 0-1.138.92-2.063 2.063-2.063 1.14 0 2.064.925 2.064 2.063 0 1.139-.925 2.065-2.064 2.065zm1.782 13.019H3.555V9h3.564v11.452zM22.225 0H1.771C.792 0 0 .774 0 1.729v20.542C0 23.227.792 24 1.771 24h20.451C23.2 24 24 23.227 24 22.271V1.729C24 .774 23.2 0 22.222 0h.003z"/></svg>
|
|
|
LinkedIn
|
|
|
</a>
|
|
|
<a href="https://www.facebook.com/sharer/sharer.php?u={encoded_url}" target="_blank"
|
|
|
style="display: inline-flex; align-items: center; gap: 6px; padding: 8px 16px; background: #1877F2; color: white; text-decoration: none; border-radius: 6px; font-size: 13px; font-weight: 500;">
|
|
|
<svg width="16" height="16" viewBox="0 0 24 24" fill="currentColor"><path d="M24 12.073c0-6.627-5.373-12-12-12s-12 5.373-12 12c0 5.99 4.388 10.954 10.125 11.854v-8.385H7.078v-3.47h3.047V9.43c0-3.007 1.792-4.669 4.533-4.669 1.312 0 2.686.235 2.686.235v2.953H15.83c-1.491 0-1.956.925-1.956 1.874v2.25h3.328l-.532 3.47h-2.796v8.385C19.612 23.027 24 18.062 24 12.073z"/></svg>
|
|
|
Facebook
|
|
|
</a>
|
|
|
</div>
|
|
|
<p style="color: #666; font-size: 11px; text-align: center; margin-top: 12px;">
|
|
|
Want real blockchain-verified results? <a href="https://bioprime.one" target="_blank" style="color: #4ECDC4;">Sign up at bioprime.one</a>
|
|
|
</p>
|
|
|
</div>
|
|
|
"""
|
|
|
|
|
|
|
|
|
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()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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]) -> 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", "", ""
|
|
|
|
|
|
|
|
|
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"])
|
|
|
|
|
|
|
|
|
time.sleep(1.5)
|
|
|
|
|
|
results_text, chart_html, receipt = generate_docking_result(target["id"], compound_ids)
|
|
|
|
|
|
return results_text, chart_html, receipt
|
|
|
|
|
|
|
|
|
|
|
|
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:
|
|
|
|
|
|
|
|
|
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():
|
|
|
|
|
|
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]
|
|
|
)
|
|
|
|
|
|
|
|
|
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]
|
|
|
)
|
|
|
|
|
|
|
|
|
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>
|
|
|
""")
|
|
|
|
|
|
|
|
|
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>
|
|
|
</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()
|
|
|
|
