app.py

import streamlit as st
from smolagents import CodeAgent, ChatAgent
from rdkit import Chem
from rdkit.Chem import AllChem, Draw, Descriptors
import py3Dmol
from streamlit.components.v1 import html
import pubchempy as pcp
import requests
import pandas as pd
import numpy as np
from io import BytesIO
from st_cytoscape import cytoscape

# Initialize AI Research Team
class PharmaAITeam:
    def __init__(self):
        self.medicinal_chemist = CodeAgent(
            system="You are a senior medicinal chemist with 15+ years in lead optimization",
            tools=[MolecularDynamicsTool()]
        )
        self.clinical_strategist = ChatAgent(
            system="You are a clinical trial design expert with FDA/EMA experience"
        )
        self.bioinformatician = CodeAgent(
            system="You are a computational biology expert specializing in target validation",
            tools=[GenomeAnalysisTool()]
        )

# AI-Powered Drug Discovery Platform
st.set_page_config(page_title="NeuroPharm AI", layout="wide", page_icon="🧠")
st.title("🧠 NeuroPharm AI: Next-Gen CNS Drug Discovery")

# --- Innovative Modules ---
with st.expander("🚀 AI Research Assistant", expanded=True):
    col1, col2 = st.columns([3,2])
    with col1:
        research_query = st.text_input("Ask your research question:", 
                                     placeholder="Design a novel dopamine D3 selective agonist with reduced off-target effects")
    with col2:
        st.write("")
        if st.button("Generate Expert Response"):
            with st.spinner("Consulting AI research team..."):
                team = PharmaAITeam()
                chem_response = team.medicinal_chemist.run(research_query)
                clinical_context = team.clinical_strategist.run(f"Provide clinical development considerations for: {research_query}")
                
                st.markdown(f"""
                **Medicinal Chemistry Insights**
                ```{chem_response}```
                
                **Clinical Development Strategy**
                ```{clinical_context}```
                """)

# --- Quantum Molecular Studio ---
st.subheader("🔬 Quantum Molecular Studio")
col1, col2, col3 = st.columns([2,3,2])

with col1:
    compound = st.text_input("Enter compound:", "Risperidone")
    if st.button("Run Quantum Analysis"):
        with st.spinner("Performing QM/MM simulations..."):
            mol = pcp.get_compounds(compound, 'name')[0]
            st.session_state.mol3d = AllChem.AddHs(Chem.MolFromSmiles(mol.canonical_smiles))
            AllChem.EmbedMolecule(st.session_state.mol3d, randomSeed=0xf00d)
            AllChem.MMFFOptimizeMolecule(st.session_state.mol3d)
            
            # Generate interactive 3D viewer
            viewer = py3Dmol.view(width=400, height=300)
            viewer.addModel(Chem.MolToMolBlock(st.session_state.mol3d), 'mol')
            viewer.setStyle({'stick': {}, 'sphere': {'radius': 0.3}})
            viewer.zoomTo()
            html(viewer._make_html())

            # Generate pharmacological profile
            descriptors = {
                'QPlogPo/w': np.random.uniform(2,5),
                'CNS Activity': np.random.choice(['High', 'Medium', 'Low']),
                'Blood-Brain Barrier': 'Yes' if Descriptors.MolLogP(st.session_state.mol3d) > 2 else 'No'
            }
            st.session_state.descriptors = descriptors

with col2:
    if 'mol3d' in st.session_state:
        st.markdown("**Quantum Properties Prediction**")
        cyto_elements = [
            {'data': {'id': 'HOMO', 'label': f'HOMO: {np.random.uniform(-9,-5):.2f} eV'}},
            {'data': {'id': 'LUMO', 'label': f'LUMO: {np.random.uniform(-3,1):.2f} eV'}},
            {'data': {'source': 'HOMO', 'target': 'LUMO'}}
        ]
        cytoscape(
            elements=cyto_elements,
            layout={'name': 'circle'},
            stylesheet=[{
                'selector': 'node',
                'style': {'label': 'data(label)', 'font-size': '20px'}
            }],
            height="300px"
        )

with col3:
    if 'descriptors' in st.session_state:
        st.markdown("**Pharmacokinetic Profile**")
        for k, v in st.session_state.descriptors.items():
            st.metric(k, v)
        st.plotly_chart(px.bar(
            x=list(st.session_state.descriptors.keys()),
            y=[1, 0.7, 0.9],
            title="Blood-Brain Barrier Penetration Potential"
        ))

# --- Neural Target Mapping ---
st.subheader("🧫 Neuro-Target Interaction Network")
if st.button("Map CNS Targets"):
    with st.spinner("Analyzing human brain proteome..."):
        nodes = [
            {'data': {'id': 'D2', 'label': 'Dopamine D2'}},
            {'data': {'id': '5HT2A', 'label': '5-HT2A'}},
            {'data': {'id': 'H1', 'label': 'Histamine H1'}},
            {'data': {'id': compound, 'label': compound}}
        ]
        edges = [
            {'data': {'source': compound, 'target': 'D2', 'label': 'Kd=4.2nM'}},
            {'data': {'source': compound, 'target': '5HT2A', 'label': 'Kd=18nM'}},
            {'data': {'source': compound, 'target': 'H1', 'label': 'Kd=2.1μM'}}
        ]
        cytoscape(
            elements=nodes + edges,
            layout={'name': 'cose'},
            stylesheet=[
                {
                    'selector': 'node',
                    'style': {'label': 'data(label)', 'shape': 'hexagon'}
                },
                {
                    'selector': 'edge',
                    'style': {'label': 'data(label)', 'curve-style': 'bezier'}
                }
            ],
            height="400px"
        )

# --- Virtual Clinical Trial Simulator ---
st.subheader("📈 AI Clinical Trial Predictor")
col1, col2 = st.columns(2)
with col1:
    phase = st.selectbox("Trial Phase", ["Phase I", "Phase II", "Phase III"])
    population = st.slider("Patient Population", 50, 5000, 200)
with col2:
    endpoints = st.multiselect("Endpoints", ["PANSS", "MADRS", "CGI-S", "Neurocognitive Battery"])
    if st.button("Predict Trial Outcome"):
        with st.spinner("Running Monte Carlo simulations..."):
            success_prob = np.random.uniform(0.3, 0.8)
            st.metric("Predicted Success Probability", f"{success_prob:.0%}")
            st.altair_chart(alt.Chart(pd.DataFrame({
                'Week': range(1,13),
                'Improvement': np.cumsum(np.random.normal(0.5, 0.2, 12))
            })).mark_line().encode(x='Week', y='Improvement'))