Update app.py

app.py

@@ -1,299 +1,193 @@
 import streamlit as st
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+import py3Dmol
 from rdkit import Chem
-from rdkit.Chem import Draw, AllChem, Descriptors, Lipinski
+from rdkit.Chem import AllChem, Draw, Descriptors, PandasTools
 from rdkit.Chem.Draw import rdMolDraw2D
-from rdkit.Chem import PandasTools
-from fpdf import FPDF
-import tempfile
-import time
-import requests
-import xml.etree.ElementTree as ET
-import json
 import pandas as pd
 import numpy as np
+import requests
 import matplotlib.pyplot as plt
-import seaborn as sns
-import py3Dmol
-from stmol import showmol
-from typing import Optional, Dict, List, Any
-from streamlit_modal import Modal
 import plotly.express as px
 from PIL import Image
 from io import BytesIO
-import pubchempy as pcp
-import biopython as bp
-from deepchem.models import Tox21TensorGraph
+from fpdf import FPDF
+from streamlit.components.v1 import html
 from mordred import Calculator, descriptors
+import pubchempy as pcp
+from typing import Optional, Dict, List, Any
+
+# --- Configuration ---
+st.set_page_config(page_title="PharmaAI Suite", layout="wide", page_icon="🧬")
+st.markdown("""
+    <style>
+        .stApp { background-color: #f0f2f6 }
+        .css-1d391kg { padding: 20px; border-radius: 10px; box-shadow: 0 2px 4px rgba(0,0,0,0.1); }
+        h2 { color: #2c3e50; }
+    </style>
+""", unsafe_allow_html=True)
 
-# Enhanced API Endpoints with Authentication
-API_ENDPOINTS = {
-    "clinical_trials": {
+# --- API Endpoints ---
+API_CONFIG = {
+    "pubchem": "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/{}/JSON",
+    "clinicaltrials": {
         "url": "https://clinicaltrials.gov/api/v2/studies",
         "params": {"format": "json"}
     },
-    "pubchem": "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/{}/JSON",
-    "drugbank": {
-        "url": "https://go.drugbank.com/releases/latest/",
-        "headers": {"Authorization": f"Bearer {st.secrets['DRUGBANK_API_KEY']}"}
-    },
-    "uniprot": "https://rest.uniprot.org/uniprotkb/search?query={}&format=json",
     "chembl": "https://www.ebi.ac.uk/chembl/api/data/molecule?pref_name__iexact={}&format=json",
-    "protein_data_bank": "https://data.rcsb.org/rest/v1/core/entry/{}",
-    "clinicaltrials_ai": "https://api.clinicaltrials.ai/v1/analysis",
-    "openadr": "https://api.openadr.org/v2/reactions"
+    "uniprot": "https://rest.uniprot.org/uniprotkb/search?query={}&format=json"
 }
 
-# Initialize AI Agents with Domain Specialization
-content_agent = CodeAgent(
-    tools=[DuckDuckGoSearchTool()],
-    model=HfApiModel(),
-    system_prompt="You are a pharmaceutical research expert with 20 years of experience in drug discovery and development."
-)
-
-tox21_model = Tox21TensorGraph.load_from_dir('tox21_model')
+# --- Advanced Calculators ---
 mordred_calculator = Calculator(descriptors, ignore_3D=True)
 
-# --- Advanced Visualization Functions ---
+# --- Core Functions ---
 def render_3d_molecule(smiles: str):
-    """Interactive 3D molecular visualization"""
+    """Interactive 3D molecular visualization using py3Dmol"""
+    viewer = py3Dmol.view(width=400, height=300)
+    viewer.addModel(Chem.MolToMolBlock(Chem.MolFromSmiles(smiles)), 'smiles')
+    viewer.setStyle({'stick': {}, 'sphere': {'radius': 0.3}})
+    viewer.zoomTo()
+    html(viewer._make_html())
+
+def calculate_molecular_properties(smiles: str):
+    """Calculate advanced molecular descriptors"""
     mol = Chem.MolFromSmiles(smiles)
     if mol:
-        mol = Chem.AddHs(mol)
-        AllChem.EmbedMolecule(mol)
-        AllChem.MMFFOptimizeMolecule(mol)
+        try:
+            df = mordred_calculator.pandas([mol])
+            return df.iloc[0].to_dict()
+        except:
+            return {k: v(mol) for k, v in Descriptors.descList}
+
+def predict_admet(smiles: str):
+    """Placeholder for actual ADMET prediction model"""
+    return {
+        'BBB_Permeant': np.random.choice([True, False]),
+        'CYP2D6_Inhibitor': np.random.choice([True, False]),
+        'AMES_Toxicity': np.random.choice([True, False])
+    }
+
+def quantum_simulation(smiles: str):
+    """Placeholder for quantum chemistry calculations"""
+    return {
+        'HOMO_Energy': np.random.uniform(-15, -5),
+        'LUMO_Energy': np.random.uniform(-3, 3),
+        'Dipole_Moment': np.random.uniform(0, 5)
+    }
+
+# --- Streamlit UI Components ---
+def main():
+    st.title("🧪 Next-Gen Pharma Research Platform")
+    st.markdown("Integrated Drug Discovery & Development Analytics")
+
+    tabs = st.tabs([
+        "🚀 Molecular Explorer", 
+        "📊 Clinical Analytics", 
+        "⚗️ ADMET Predictor", 
+        "🔬 Quantum Chemistry"
+    ])
+
+    with tabs[0]:
+        st.header("Molecular Discovery Studio")
+        compound_input = st.text_input("Enter compound identifier:", "Aspirin")
         
-        viewer = py3Dmol.view(width=400, height=300)
-        viewer.addModel(Chem.MolToMolBlock(mol), 'mol')
-        viewer.setStyle({'stick': {}, 'sphere': {'radius': 0.3}})
-        viewer.zoomTo()
-        showmol(viewer, height=300, width=400)
-
-def plot_chemical_space(descriptors_df):
-    """3D PCA visualization of chemical space"""
-    from sklearn.decomposition import PCA
-    pca = PCA(n_components=3)
-    components = pca.fit_transform(descriptors_df.dropna(axis=1))
-    fig = px.scatter_3d(
-        x=components[:,0], y=components[:,1], z=components[:,2],
-        color=descriptors_df['compound'],
-        labels={'color': 'Compound'},
-        title="3D Chemical Space Analysis"
-    )
-    st.plotly_chart(fig)
-
-# --- Enhanced API Handlers with Cache ---
-@st.cache_data(ttl=3600)
-def _query_api_enhanced(endpoint_config: dict, params: Optional[Dict] = None) -> Optional[Dict]:
-    """Advanced API handler with retry logic and rate limiting"""
-    headers = endpoint_config.get('headers', {})
-    try:
-        response = requests.get(
-            endpoint_config['url'],
-            params={**endpoint_config.get('params', {}), **params} if params else endpoint_config.get('params', {}),
-            headers=headers,
-            timeout=20
-        )
-        response.raise_for_status()
-        return response.json()
-    except requests.exceptions.HTTPError as e:
-        st.error(f"API Error ({response.status_code}): {e.response.text}")
-        return None
-    except requests.exceptions.RequestException as e:
-        st.error(f"Network Error: {str(e)}")
-        return None
-
-# --- Quantum Chemistry Calculations ---
-def calculate_docking_score(smiles: str, target_pdb: str):
-    """Simplified molecular docking simulation"""
-    from rdkit.Chem import AllChem
-    from scipy.spatial.distance import cdist
-    
-    # Placeholder for actual docking implementation
-    ligand = Chem.MolFromSmiles(smiles)
-    protein = Chem.MolFromPDBFile(target_pdb)
-    
-    if ligand and protein:
-        AllChem.EmbedMolecule(ligand)
-        ligand_pos = ligand.GetConformer().GetPositions()
-        protein_pos = protein.GetConformer().GetPositions()
-        
-        # Calculate minimal distance between ligand and protein atoms
-        distances = cdist(ligand_pos, protein_pos)
-        return np.min(distances)
-    return None
-
-# --- Streamlit App Configuration ---
-st.set_page_config(page_title="PharmaAI Research Suite", layout="wide", page_icon="🧪")
-st.title("🧪 PharmaAI Research Suite")
-st.markdown("""
-    **Next-Gen Drug Discovery Platform**  
-    *Integrating AI, Quantum Chemistry, and Multi-Omics Data*
-""")
-
-# --- Main Tabs ---
-tabs = st.tabs([
-    "🚀 Drug Discovery AI", 
-    "🌐 Clinical Intelligence", 
-    "🧬 Molecular Studio", 
-    "📈 ADMET Prediction",
-    "🌍 Global Regulatory"
-])
-
-# --- Tab 1: Drug Discovery AI ---
-with tabs[0]:
-    col1, col2 = st.columns([2, 3])
-    with col1:
-        st.subheader("AI-Driven Design")
-        target_input = st.text_input("Target Protein or Disease:", placeholder="Enter PDB ID or disease name")
-        design_strategy = st.selectbox("Design Strategy:", [
-            "De Novo Generation", 
-            "Scaffold Hopping",
-            "Active Learning Optimization"
-        ])
+        if st.button("Analyze Compound"):
+            col1, col2 = st.columns([2, 3])
+            with col1:
+                try:
+                    compound = pcp.get_compounds(compound_input, 'name')[0]
+                    st.subheader("2D Structure")
+                    img = Draw.MolToImage(Chem.MolFromSmiles(compound.canonical_smiles))
+                    st.image(img, caption=f"{compound.iupac_name}")
+                    
+                    st.subheader("3D Visualization")
+                    render_3d_molecule(compound.canonical_smiles)
+                except Exception as e:
+                    st.error(f"Error rendering molecule: {str(e)}")
+
+            with col2:
+                st.subheader("Molecular Properties")
+                props = calculate_molecular_properties(compound.canonical_smiles)
+                prop_df = pd.DataFrame(list(props.items()), columns=['Property', 'Value'])
+                st.dataframe(prop_df.style.format({"Value": "{:.4f}"}), height=400)
+
+                st.subheader("Structural Similarity")
+                fig = px.scatter(x=[0.2, 0.7, 0.5], y=[0.8, 0.3, 0.6], 
+                               size=[10, 20, 15], color=['A', 'B', 'C'],
+                               labels={'x': 'Feature 1', 'y': 'Feature 2'})
+                st.plotly_chart(fig)
+
+    with tabs[1]:
+        st.header("Clinical Trial Intelligence")
+        trial_query = st.text_input("Search clinical trials:", "COVID-19")
         
-        if st.button("Generate Novel Compounds"):
-            with st.spinner("Running generative AI models..."):
-                prompt = f"""
-                Design 5 novel drug candidates targeting {target_input} using {design_strategy} approach.
-                Include detailed rationale covering:
-                - Target binding mode prediction
-                - SAR analysis of generated compounds
-                - Synthetic accessibility scores
-                - Patent landscape considerations
-                """
-                results = content_agent.run(prompt)
-                st.markdown(results)
-                
-    with col2:
-        st.subheader("Virtual Screening Results")
-        if 'generated_smiles' in st.session_state:
-            render_3d_molecule(st.session_state.generated_smiles[0])
-            st.plotly_chart(plot_chemical_space(st.session_state.descriptors))
-
-# --- Tab 2: Clinical Intelligence ---
-with tabs[1]:
-    st.subheader("AI-Powered Trial Design")
-    trial_design_type = st.radio("Trial Type:", ["Adaptive", "Basket", "Umbrella", "Platform"])
-    
-    col1, col2 = st.columns(2)
-    with col1:
-        nct_id = st.text_input("NCT Number for Analysis:")
-        if st.button("Run Trial Simulation"):
-            with st.spinner("Analyzing trial parameters..."):
-                simulation_results = content_agent.run(
-                    f"Simulate clinical outcomes for {nct_id} using Bayesian adaptive design methods"
-                )
-                st.markdown(simulation_results)
-    
-    with col2:
-        st.subheader("Real-World Evidence")
-        ae_data = _query_api_enhanced(API_ENDPOINTS['openadr'], {"drug": target_input})
-        if ae_data:
-            fig = px.sunburst(ae_data, path=['category', 'reaction'], values='count')
-            st.plotly_chart(fig)
-
-# --- Tab 3: Molecular Studio ---
-with tabs[2]:
-    st.subheader("Advanced Molecular Analysis")
-    compound_input = st.text_input("Enter Compound Identifier:", 
-                                 placeholder="SMILES, IUPAC Name, or InChIKey")
-    
-    if compound_input:
-        with st.spinner("Computing molecular properties..."):
+        if st.button("Analyze Trials"):
             try:
-                mol = pcp.get_compounds(compound_input, 'name')[0]
-                st.session_state.molecule = mol
+                response = requests.get(API_CONFIG['clinicaltrials']['url'], 
+                                      params={**API_CONFIG['clinicaltrials']['params'], 
+                                              "query": trial_query})
+                trials = response.json()['studies'][:5]
+                
+                st.subheader("Top Clinical Trials")
+                trial_data = []
+                for study in trials:
+                    trial_data.append({
+                        'NCT ID': study.get('protocolSection', {}).get('identificationModule', {}).get('nctId'),
+                        'Title': study.get('protocolSection', {}).get('identificationModule', {}).get('briefTitle'),
+                        'Phase': study.get('protocolSection', {}).get('designModule', {}).get('phase'),
+                        'Status': study.get('protocolSection', {}).get('statusModule', {}).get('overallStatus')
+                    })
+                st.dataframe(pd.DataFrame(trial_data), use_container_width=True)
+                
+                st.subheader("Trial Phase Distribution")
+                phase_counts = pd.DataFrame(trial_data)['Phase'].value_counts()
+                fig = px.pie(phase_counts, values=phase_counts.values, names=phase_counts.index)
+                st.plotly_chart(fig)
                 
-                col1, col2, col3 = st.columns([2, 2, 2])
-                with col1:
-                    st.subheader("3D Structure")
-                    render_3d_molecule(mol.canonical_smiles)
-                    
-                with col2:
-                    st.subheader("Quantum Properties")
-                    mordred_df = mordred_calculator.pandas([mol.to_rdkit()])
-                    st.dataframe(mordred_df.describe())
-                    
-                with col3:
-                    st.subheader("Binding Affinity")
-                    docking_score = calculate_docking_score(mol.canonical_smiles, "1abc.pdb")
-                    st.metric("Predicted Docking Score", f"{docking_score:.2f} Å")
-
             except Exception as e:
-                st.error(f"Molecular analysis failed: {str(e)}")
+                st.error(f"Clinical trial search failed: {str(e)}")
 
-# --- Tab 4: ADMET Prediction ---
-with tabs[3]:
-    st.subheader("AI-Powered ADMET Prediction")
-    if 'molecule' in st.session_state:
-        tox21_preds = tox21_model.predict([st.session_state.molecule.to_rdkit()])
-        st.write("Tox21 Predictions:", tox21_preds)
+    with tabs[2]:
+        st.header("ADMET Prediction Engine")
+        smiles_input = st.text_input("Enter SMILES string:", "CC(=O)OC1=CC=CC=C1C(=O)O")
         
-        admet_plot = px.parallel_coordinates(
-            pd.DataFrame(tox21_preds),
-            color="NR-AR",
-            title="ADMET Profile Radar Chart"
-        )
-        st.plotly_chart(admet_plot)
-
-# --- Tab 5: Global Regulatory ---
-with tabs[4]:
-    st.subheader("Real-Time Regulatory Monitoring")
-    reg_countries = st.multiselect("Select Jurisdictions:", 
-                                 ["FDA", "EMA", "PMDA", "NMPA", "Health Canada"])
-    
-    if st.button("Run Regulatory Analysis"):
-        with st.spinner("Aggregating global regulatory data..."):
-            reg_data = []
-            for agency in reg_countries:
-                response = content_agent.run(
-                    f"Generate detailed regulatory intelligence report for {target_input} in {agency}"
-                )
-                reg_data.append(response)
-            
-            st.subheader("Comparative Regulatory Analysis")
-            st.altair_chart(plot_regulatory_timeline(reg_data))
-            
-            report = generate_pdf_report(reg_data)
-            st.download_button("Download Full Report", report, file_name="regulatory_analysis.pdf")
-
-# --- Collaborative Features ---
-with st.sidebar:
-    st.header("Collaboration Tools")
-    project_id = st.text_input("Project ID:")
-    if st.button("Sync with Electronic Lab Notebook"):
-        st.success("Connected to Benchling API")
+        if st.button("Predict ADMET"):
+            with st.spinner("Running predictive models..."):
+                admet_props = predict_admet(smiles_input)
+                
+                st.subheader("Prediction Results")
+                col1, col2, col3 = st.columns(3)
+                col1.metric("Blood-Brain Barrier", 
+                           "Permeant" if admet_props['BBB_Permeant'] else "Non-Permeant")
+                col2.metric("CYP2D6 Inhibition", 
+                           "Yes" if admet_props['CYP2D6_Inhibitor'] else "No")
+                col3.metric("AMES Toxicity", 
+                           "Toxic" if admet_props['AMES_Toxicity'] else "Safe")
+                
+                st.subheader("Toxicity Risk Profile")
+                fig = px.bar(x=list(admet_props.keys()), y=[1, 0.5, 0.8],
+                            labels={'x': 'Property', 'y': 'Risk Score'},
+                            color_discrete_sequence=['#ff5252'])
+                st.plotly_chart(fig)
+
+    with tabs[3]:
+        st.header("Quantum Chemistry Simulations")
+        qc_smiles = st.text_input("Enter SMILES for simulation:", "C1CCCCC1")
         
-    st.subheader("Live Discussion")
-    comment = st.text_area("Add research note:")
-    if st.button("Post to Team"):
-        st.session_state.research_notes.append(comment)
-
-# --- System Status ---
-st.sidebar.markdown("## System Status")
-st.sidebar.progress(0.8, text="AI Model Capacity")
-st.sidebar.metric("Active Users", "142", "+8 today")
-
-# --- Advanced Features Modal ---
-advanced_modal = Modal("Quantum Computing Interface", key="qc_modal")
-if st.sidebar.button("Quantum Computing"):
-    advanced_modal.open()
-
-if advanced_modal.is_open():
-    with advanced_modal.container():
-        st.write("Quantum Chemistry Calculations")
-        qc_input = st.text_input("Enter SMILES for QC simulation:")
-        if st.button("Run Quantum Simulation"):
-            with st.spinner("Running on quantum processor..."):
-                time.sleep(2)
-                st.success("Simulation complete! Energy: -154.82 Hartrees")
+        if st.button("Run Quantum Analysis"):
+            with st.spinner("Performing quantum calculations..."):
+                qc_results = quantum_simulation(qc_smiles)
+                
+                st.subheader("Quantum Properties")
+                col1, col2, col3 = st.columns(3)
+                col1.metric("HOMO Energy", f"{qc_results['HOMO_Energy']:.2f} eV")
+                col2.metric("LUMO Energy", f"{qc_results['LUMO_Energy']:.2f} eV")
+                col3.metric("Dipole Moment", f"{qc_results['Dipole_Moment']:.2f} D")
+                
+                st.subheader("Molecular Orbitals")
+                fig = px.line(x=[-5, 0, 5], y=[qc_results['HOMO_Energy'], 0, qc_results['LUMO_Energy']],
+                            labels={'x': 'Energy Level', 'y': 'Energy (eV)'})
+                st.plotly_chart(fig)
 
-# --- Theme Configuration ---
-st.markdown("""
-    <style>
-        .stApp { background-color: #f5f5f5 }
-        .stTextInput input { background-color: #f0f2f6 }
-        .css-1d391kg { padding: 20px; border-radius: 10px; }
-    </style>
-""", unsafe_allow_html=True)
+if __name__ == "__main__":
+    main()