CODE:
import requests
from bs4 import BeautifulSoup
import json
import os
import pandas as pd
import numpy as np # For potential numerical analysis of dose-response
# --- Configuration ---
API_KEY = "AIzaSyDE9u99ETiwJMhYGgvz6CN2uXAJLOSAeMw" # Directly embedding - use environment variables in production
# API_KEY = os.environ.get("YOUR_API_KEY_ENV_VAR") # Recommended for production
BLOG_URLS = {
'tp53_kegg_pathway': "https://mrinmoych.blogspot.com/2025/02/tp53-kegg-pathway.html",
'protein_protein_interactions': "https://mrinmoych.blogspot.com/2025/02/protein-protein-interactions-code.html",
'molecular_cellular_code': "https://mrinmoych.blogspot.com/2025/02/molcular-and-cellular-related-code.html",
'disease_gene_association_code': "https://mrinmoych.blogspot.com/2025/02/disease-gene-association-code.html",
'drug_prediction_distribution': "https://mrinmoych.blogspot.com/2025/02/drug-prediction-score-drug-distribution.html",
'drug_efficacy_dose_response': "https://mrinmoych.blogspot.com/2025/02/drug-efficacy-dose-response.html",
'binding_affinity_prediction': "https://mrinmoych.blogspot.com/2025/02/actual-vs-predicted-binding-affinity.html",
'toxicity_prediction_curcumin': "https://mrinmoych.blogspot.com/2025/02/predicted-toxicity-curcumin-vs.html",
'gnn_ml_model_optuna': "https://mrinmoych.blogspot.com/2025/03/gnn-ml-model-code-with-optuna-tuning.html",
'md_ml_gnn_svm_rf_gb': "https://mrinmoych.blogspot.com/2025/03/md-ml-gnn-svm-random-forest-gradient.html"
}
API_ENDPOINTS = {
"drugbank": "https://api.drugbank.com/v1/drugs/", # Example DrugBank API endpoint (replace with actual if used)
"clinicaltrials": "https://clinicaltrials.gov/api/query/full_studies", # Example ClinicalTrials.gov API
# ... Add other API endpoints as needed ...
}
API_KEYS = {
"drugbank": "YOUR_DRUGBANK_API_KEY", # Replace with your actual DrugBank API key if used
# ... Add API keys for other services if needed ...
}
# --- pH Data ---
curcumin_ph_data = {
'pH_values': [3, 3.6, 4.2, 4.8, 5.4, 6, 6.6, 7.2, 7.8, 8.4, 9, 9.6],
'microspecies_distribution': [
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # Query
[100, 100, 100, 100, 100, 100, 100, 99, 97, 89, 62, 32], # 1
[0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 4], # 2
[0, 0, 0, 0, 0, 0, 0, 0, 1, 5, 20, 8], # 3
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 19], # 4
[0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 19], # 5
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 10], # 6
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 34] # 7
]
}
methylene_blue_ph_data = {
'pH_ranges': ["0-3.0", "3.4", "4.2", "4.8", "5.4", "6.0", "6.6", "7.2", "7.8", "8.4", "9.0-9.6"],
'microspecies_distribution': [
{"Query1": 0, "Query2": 0, "Query3": 0, "RedQuery": 0}, # pH 0-3.0
{"Query1": 0, "Query2": 5, "Query3": 0, "RedQuery": 0}, # pH 3.4
{"Query1": 10, "Query2": 10, "Query3": 2, "RedQuery": 0},# pH 4.2
{"Query1": 20, "Query2": 15, "Query3": 4, "RedQuery": 0},# pH 4.8
{"Query1": 40, "Query2": 20, "Query3": 6, "RedQuery": 0},# pH 5.4
{"Query1": 60, "Query2": 25, "Query3": 8, "RedQuery": 0},# pH 6.0
{"Query1": 70, "Query2": 30, "Query3": 10, "RedQuery": 0},# pH 6.6
{"Query1": 80, "Query2": 35, "Query3": 12, "RedQuery": 0},# pH 7.2
{"Query1": 90, "Query2": 40, "Query3": 15, "RedQuery": 0},# pH 7.8
{"Query1": 95, "Query2": 45, "Query3": 20, "RedQuery": 0},# pH 8.4
{"Query1": 100, "Query2": "50-60", "Query3": "25-30", "RedQuery": "0-10"} # pH 9.0-9.6
]
}
# --- Dose-Response Data (PLACEHOLDER - REPLACE WITH ACTUAL DATA) ---
dose_response_data = {
"methylene_blue": {
"concentrations_molar": [1e-6, 1e-5, 1e-4, 1e-3], # Molar concentrations
"responses_percent": [10, 30, 55, 90] # Corresponding responses (%)
},
"curcumin": { # Assuming "Other Drug" is Curcumin
"concentrations_molar": [1e-6, 1e-5, 0.0001, 0.001],
"responses_percent": [5, 25, 50, 85]
}
}
# --- Drug Properties Data ---
drug_properties_data = {
"methylene_blue": {
"molecular_weight": 319.86,
"rotatable_bonds": 1,
"alogp": -0.50,
"tpsa": 19.14,
"h_bond_acceptors": 3,
"h_bond_donors": 0
},
"curcumin": {
"molecular_weight": 368.39,
"rotatable_bonds": 8,
"alogp": 3.37,
"tpsa": 93.06,
"h_bond_acceptors": 6,
"h_bond_donors": 2
}
}
# --- Excipients Data ---
excipients_data = {
"p188": {
"name": "Poloxamer 188 (P188)",
"type": "Surfactant, Solubility Enhancer, Co-grinding Agent",
"description": "Non-ionic surfactant, can improve solubility and bioavailability, reduce aggregation. Useful in solvent-free co-grinding to enhance curcumin bioavailability.", # Updated description
# ... Add more properties if needed (e.g., concentration range, safety profile) ...
},
"p407": {
"name": "Poloxamer 407 (P407)",
"type": "Surfactant, Viscosity Modifier, Thermosensitive Gel, Co-grinding Agent",
"description": "Non-ionic surfactant, can form thermosensitive gels, used for controlled release, improve solubility. Useful in solvent-free co-grinding to enhance curcumin bioavailability.", # Updated description
# ... Add more properties if needed ...
},
"piperine": {
"name": "Piperine",
"type": "Bioavailability Enhancer",
"description": "Natural compound from black pepper, known to significantly enhance curcumin bioavailability, likely by inhibiting metabolism.", # Added Piperine
"bioavailability_enhancement": "Significant for Curcumin" # Specific property for Piperine
# ... Add more properties if needed ...
}
}
# --- Knowledge Base ---
class KnowledgeBase:
def __init__(self):
self.data = {}
def store_data(self, key, value):
self.data[key] = value
def retrieve_data(self, key):
return self.data.get(key)
# --- API Request Function with API Key (for Blog - Example - adjust as needed) ---
def api_request_with_key(url, api_key=API_KEY):
"""Fetches content from a URL, embedding API key (example - adjust based on API requirements)."""
try:
url_with_key = f"{url}?key={api_key}" # Example: API key as query parameter
response = requests.get(url_with_key)
response.raise_for_status()
return response.text
except requests.exceptions.RequestException as e:
print(f"Error fetching URL {url} with API key: {e}")
return None
# --- Data Acquisition Agent (DAA) ---
class DataAcquisitionAgent:
def __init__(self, knowledge_base, blog_urls, api_endpoints=None, api_keys=None):
self.knowledge_base = knowledge_base
self.blog_urls = blog_urls
self.api_endpoints = api_endpoints or {}
self.api_keys = api_keys or {}
def fetch_blog_data(self):
"""Fetches and stores data from blog URLs, parsing code and text."""
for name, url in self.blog_urls.items():
content = api_request_with_key(url) # Fetch using API key function
if content:
print(f"Fetched content from: {url}")
self.knowledge_base.store_data(name + '_raw_html', content)
self._parse_and_structure_blog_data(name)
def _parse_and_structure_blog_data(self, name):
"""Parses HTML content to extract code and main text (private helper method)."""
raw_html = self.knowledge_base.retrieve_data(name + '_raw_html')
if raw_html:
soup = BeautifulSoup(raw_html, 'html.parser')
code_blocks = []
for code_tag in soup.find_all(['pre', 'code']): # Find both <pre> and <code> for code
code_blocks.append(code_tag.text.strip())
self.knowledge_base.store_data(name + '_code_blocks', code_blocks)
main_text_paragraphs = []
for p_tag in soup.find_all('p'):
if not p_tag.find_parent(['pre', 'code']): # Exclude text in code blocks
main_text_paragraphs.append(p_tag.text.strip())
main_text_content = "\n".join(main_text_paragraphs)
self.knowledge_base.store_data(name + '_main_text', main_text_content)
print(f"Parsed and structured data from {name}")
def fetch_drug_data_from_api(self, drug_name):
"""Fetches drug data from DrugBank API (example - adapt as needed)."""
endpoint = self.api_endpoints.get("drugbank")
api_key = self.api_keys.get("drugbank")
if not endpoint or not api_key:
print("DrugBank API endpoint or key not configured.")
return None
headers = {'Authorization': f'Bearer {api_key}'} # Example: Bearer token auth
url = f"{endpoint}?query={drug_name}" # Example query - check API docs
try:
response = requests.get(url, headers=headers)
response.raise_for_status()
drug_api_data = response.json()
self.knowledge_base.store_data(f'drugbank_data_{drug_name}', drug_api_data) # Store in KB
print(f"Fetched DrugBank data for {drug_name}")
return drug_api_data
except requests.exceptions.RequestException as e:
print(f"Error fetching drug data from DrugBank: {e}")
return None
def fetch_clinical_trial_data_api(self, query_params):
"""Fetches clinical trial data from ClinicalTrials.gov API (example)."""
endpoint = self.api_endpoints.get("clinicaltrials")
if not endpoint:
print("ClinicalTrials.gov API endpoint not configured.")
return None
try:
response = requests.get(endpoint, params=query_params)
response.raise_for_status()
clinical_trial_data = response.json()
self.knowledge_base.store_data('clinical_trial_data', clinical_trial_data) # Store in KB
print("Fetched Clinical Trial data")
return clinical_trial_data
except requests.exceptions.RequestException as e:
print(f"Error fetching clinical trial data: {e}")
return None
# --- Data Scientist Agent ---
class DataScientistAgent:
def __init__(self, knowledge_base):
self.knowledge_base = knowledge_base
def analyze_drug_efficacy_data(self):
"""Example: Analyzes drug efficacy data from blog and potentially external sources."""
efficacy_blog_text = self.knowledge_base.retrieve_data('drug_efficacy_dose_response_main_text')
if efficacy_blog_text:
print("\n--- Data Scientist Agent: Analyzing Drug Efficacy Data from Blog ---")
# Example: Basic keyword analysis (replace with actual ML/statistical analysis)
if "dose-response" in efficacy_blog_text.lower() or "IC50" in efficacy_blog_text.upper():
print("Keywords 'dose-response' or 'IC50' found in efficacy blog text. Potentially relevant data.")
else:
print("Keywords for efficacy analysis not prominently found in blog text.")
# Example: Analyze drug prediction scores (from blog code blocks - illustrative)
prediction_code_blocks = self.knowledge_base.retrieve_data('drug_prediction_distribution_code_blocks')
if prediction_code_blocks:
print("\n--- Data Scientist Agent: Analyzing Drug Prediction Code ---")
for code in prediction_code_blocks:
if "RandomForestRegressor" in code or "GNN" in code: # Example keyword check for ML models
print("Code block potentially uses ML models (RandomForestRegressor or GNN) for drug prediction.")
# In real system, you could try to execute/analyze code, or extract model parameters
# Example: Analyze clinical trial data (if fetched and available)
clinical_trial_data = self.knowledge_base.retrieve_data('clinical_trial_data')
if clinical_trial_data and clinical_trial_data.get('FullStudiesResponse'):
studies_count = clinical_trial_data['FullStudiesResponse'].get('NStudiesFound', 0)
print(f"\n--- Data Scientist Agent: Analyzing Clinical Trial Data ---")
print(f"ClinicalTrials.gov API returned {studies_count} studies. Further analysis needed on study details.")
# ... (Further, more detailed analysis of clinical trial data using pandas, statistics, etc. would go here)
def analyze_dose_response_curves(self, dose_response_data):
"""Analyzes the provided dose-response curve data."""
print("\n--- Data Scientist Agent: Analyzing Dose-Response Curves ---")
if dose_response_data:
for drug, data in dose_response_data.items():
print(f"\n--- Dose-Response Data for {drug.capitalize()} ---")
concentrations = data["concentrations_molar"]
responses = data["responses_percent"]
print("Concentrations (M):", concentrations)
print("Responses (%):", responses)
# --- Example: Basic analysis - Calculate approximate max response difference ---
max_response = max(responses)
min_response = min(responses)
response_range = max_response - min_response
print(f"Approximate Response Range: {response_range:.2f}%")
# --- Example: Placeholder for EC50 calculation or curve fitting ---
# In a real system, you would use libraries like scipy.optimize to fit a sigmoidal curve
# and calculate EC50. For this example, just noting the placeholder.
print("Placeholder for EC50 calculation or curve fitting would go here...")
else:
print("No dose-response data provided for analysis.")
def perform_gnn_model_analysis(self):
"""Example: Analysis focused on GNN ML models from blog content."""
gnn_model_code = self.knowledge_base.retrieve_data('gnn_ml_model_optuna_code_blocks')
if gnn_model_code:
print("\n--- Data Scientist Agent: Analyzing GNN ML Model Code ---")
for code in gnn_model_code:
if "GraphNeuralNetwork" in code or "DGL" in code or "PyTorch Geometric" in code: # Keywords for GNN frameworks
print("Code block appears to contain Graph Neural Network model implementation (using DGL or PyTorch Geometric).")
# ... (Further analysis: extract model architecture, layers, optimization methods, etc. from code)
else:
print("No GNN ML model code blocks found in Knowledge Base.")
# --- Research Agent --- (No changes in ResearchAgent and ClinicalAgent for this step, but they can be enhanced later to use dose-response data)
class ResearchAgent:
def __init__(self, knowledge_base):
self.knowledge_base = knowledge_base
def research_tp53_pathway(self):
"""Researches TP53 pathway using blog text content."""
tp53_text = self.knowledge_base.retrieve_data('tp53_kegg_pathway_main_text')
if tp53_text:
print("\n--- Research Agent: TP53 Pathway Research ---")
# Example: Basic keyword-based research simulation
keywords = ["TP53", "apoptosis", "cell cycle arrest", "DNA repair", "cancer"]
relevant_sentences = []
for sentence in tp53_text.split('.'): # Very basic sentence splitting
for keyword in keywords:
if keyword.lower() in sentence.lower():
relevant_sentences.append(sentence.strip())
break # Avoid adding sentence multiple times if multiple keywords present
if relevant_sentences:
print(f"Found {len(relevant_sentences)} sentences related to TP53 pathway keywords:")
for sentence in relevant_sentences[:5]: # Print first few relevant sentences
print(f"- {sentence}...")
else:
print("No sentences with TP53 pathway keywords found in blog text.")
else:
print("No TP53 pathway text data available for research.")
def research_curcumin_methylene_blue_synergy(self):
"""Example: Simulates research on Curcumin and Methylene Blue synergy (using blog texts)."""
toxicity_text = self.knowledge_base.retrieve_data('toxicity_prediction_curcumin_main_text') # Example blog for synergy clues
if toxicity_text:
print("\n--- Research Agent: Curcumin & Methylene Blue Synergy Research ---")
synergy_keywords = ["synergy", "combination therapy", "combined effect", "enhance efficacy", "reduce toxicity"]
synergy_mentions = 0
for keyword in synergy_keywords:
if keyword.lower() in toxicity_text.lower():
synergy_mentions += 1
if synergy_mentions > 0:
print(f"Keywords related to synergy found {synergy_mentions} times in toxicity blog text. Potential indication of synergy.")
else:
print("No strong indications of synergy keywords found in toxicity blog text.")
else:
print("No toxicity prediction blog text available for synergy research.")
# --- Clinical Agent ---
class ClinicalAgent:
def __init__(self, knowledge_base):
self.knowledge_base = knowledge_base
def analyze_clinical_trial_relevance(self):
"""Analyzes clinical trial data for relevance to Curcumin & Methylene Blue in cancer."""
clinical_trial_data = self.knowledge_base.retrieve_data('clinical_trial_data')
if clinical_trial_data and clinical_trial_data.get('FullStudiesResponse'):
studies = clinical_trial_data['FullStudiesResponse'].get('FullStudies', [])
relevant_trial_count = 0
for study in studies:
study_data = study['Study']
if 'BriefTitle' in study_data and ('curcumin' in study_data['BriefTitle'].lower() or 'methylene blue' in study_data['BriefTitle'].lower()):
relevant_trial_count += 1
print(f"\n--- Clinical Agent: Potentially Relevant Clinical Trial Found ---")
print(f"NCT ID: {study_data['NCTId']}")
print(f"Title: {study_data['BriefTitle']}")
print(f"Status: {study_data['OverallStatus']}")
# ... (Further analysis: extract study phase, conditions, interventions, outcomes etc.)
if relevant_trial_count > 0:
print(f"\nClinical Agent: Found {relevant_trial_count} potentially relevant clinical trials involving Curcumin or Methylene Blue.")
else:
print("\nClinical Agent: No directly relevant clinical trials for Curcumin or Methylene Blue found in initial search.")
else:
print("Clinical Agent: No Clinical Trial data available for analysis.")
def assess_dosage_and_side_effects(self):
"""Example: Assesses dosage and side effect information (using blog - illustrative)."""
efficacy_dose_text = self.knowledge_base.retrieve_data('drug_efficacy_dose_response_main_text')
toxicity_text = self.knowledge_base.retrieve_data('toxicity_prediction_curcumin_main_text')
print("\n--- Clinical Agent: Dosage and Side Effect Assessment ---")
if efficacy_dose_text:
if "dosage" in efficacy_dose_text.lower() or "dose" in efficacy_dose_text.lower():
print("Efficacy/Dose blog text mentions dosage information. Further extraction needed.") # More sophisticated extraction needed
else:
print("Efficacy/Dose blog text does not explicitly mention dosage information.")
if toxicity_text:
if "toxicity" in toxicity_text.lower() or "side effect" in toxicity_text.lower() or "adverse event" in toxicity_text.lower():
print("Toxicity blog text discusses toxicity and/or side effects. Further analysis needed.") # More sophisticated analysis
else:
print("Toxicity blog text does not explicitly mention toxicity or side effects.")
else:
print("No toxicity blog text available for side effect assessment.")
# --- Formulation Agent (Example - from previous responses, can be expanded) ---
class FormulationAgent:
def __init__(self, knowledge_base, curcumin_ph_data, methylene_blue_ph_data, excipients_data): # Added excipients_data
self.knowledge_base = knowledge_base
self.curcumin_ph_data = curcumin_ph_data
self.methylene_blue_ph_data = methylene_blue_ph_data
self.excipients_data = excipients_data # Store excipients data
def analyze_ph_data(self):
print("\n--- Formulation Agent: pH Data Analysis ---")
print("Curcumin pH Data:", self.curcumin_ph_data)
print("Methylene Blue pH Data:", self.methylene_blue_ph_data)
optimal_ph = 6.5 # Example - determine based on more detailed analysis
self.knowledge_base.store_data('optimal_formulation_ph', optimal_ph)
print(f"Determined optimal formulation pH (example): {optimal_ph}")
def analyze_drug_properties(self, drug_properties):
"""Analyzes drug properties (TPSA, AlogP) for formulation and dose considerations (basic example)."""
print("\n--- Formulation Agent: Analyzing Drug Properties ---")
if drug_properties:
for drug_name, properties in drug_properties.items():
print(f"\n--- Properties for {drug_name.capitalize()} ---")
alogp = properties.get("alogp")
tpsa = properties.get("tpsa")
mw = properties.get("molecular_weight")
print(f"AlogP: {alogp}, TPSA: {tpsa}, Molecular Weight: {mw}")
# --- Very simplistic dose consideration based on AlogP and TPSA ---
dose_considerations = []
if alogp > 3: # Example rule: Higher AlogP, potential lower oral bioavailability
dose_considerations.append("AlogP suggests potential lower oral bioavailability, may require higher dose or alternative delivery.")
if tpsa > 90: # Example rule: High TPSA, potentially lower membrane permeability
dose_considerations.append("High TPSA suggests potentially lower membrane permeability, consider formulation for enhanced absorption.")
if dose_considerations:
print("\nDose Formulation Considerations:")
for consideration in dose_considerations:
print(f"- {consideration}")
self.knowledge_base.store_data(f'{drug_name}_dose_considerations', dose_considerations) # Store in KB
else:
print("\nNo specific dose/formulation considerations identified based on AlogP and TPSA (using basic rules).")
else:
print("No drug properties data provided for analysis.")
def suggest_drug_formulation(self):
optimal_ph = self.knowledge_base.retrieve_data('optimal_formulation_ph')
formulation_considerations_mb = self.knowledge_base.retrieve_data('methylene_blue_dose_considerations') # Example - retrieve dose considerations
formulation_considerations_curcumin = self.knowledge_base.retrieve_data('curcumin_dose_considerations')
excipient_suggestion = f"Considering excipients:\n" # Introduce excipient section
suggested_excipients_curcumin = [] # Initialize list to hold suggestions
if "curcumin" in self.excipients_data:
if "p188" in self.excipients_data and "p407" in self.excipients_data:
suggested_excipients_curcumin.extend(["p188", "p407"]) # Suggest Poloxamers for co-grinding and solubility
excipient_suggestion += f"- For Curcumin: Solvent-free co-grinding with {excipients_data['p188']['name']} or {excipients_data['p407']['name']} can enhance bioavailability.\n" # Co-grinding suggestion
if "piperine" in self.excipients_data:
suggested_excipients_curcumin.append("piperine") # Suggest Piperine for bioavailability
excipient_suggestion += f"- For Curcumin: {excipients_data['piperine']['name']} is known to significantly enhance bioavailability.\n" # Piperine suggestion
if suggested_excipients_curcumin:
excipient_suggestion += f" Consider excipients: {', '.join([excipients_data[excipient]['name'] for excipient in suggested_excipients_curcumin])} for solubility and/or bioavailability enhancement.\n"
else:
excipient_suggestion += "- For Curcumin: No specific excipients currently suggested based on analysis.\n" # Default if no excipients are suggested
else:
excipient_suggestion += "- For Curcumin: No excipient data available.\n"
if optimal_ph is not None:
formulation_suggestion = f"Suggesting formulation of Curcumin and Methylene Blue at pH {optimal_ph} for potentially improved stability and efficacy.\n" \
f"Further excipient selection needed based on solubility, delivery pathway, and considering:\n"
if formulation_considerations_mb:
formulation_suggestion += f"- Methylene Blue: {', '.join(formulation_considerations_mb)}\n"
if formulation_considerations_curcumin:
formulation_suggestion += f"- Curcumin: {', '.join(formulation_considerations_curcumin)}\n"
formulation_suggestion += "\n" + excipient_suggestion # Append excipient suggestions
self.knowledge_base.store_data('formulation_suggestion', formulation_suggestion)
print("\n--- Formulation Agent: Formulation Suggestion ---")
print(formulation_suggestion)
else:
print("Formulation Agent: Optimal pH not determined, cannot suggest formulation yet.")
# --- Orchestration Agent (Orchestrator) ---
class Orchestrator:
def __init__(self, knowledge_base, data_agent, data_scientist_agent, research_agent, clinical_agent, formulation_agent):
self.knowledge_base = knowledge_base
self.data_agent = data_agent
self.data_scientist_agent = data_scientist_agent
self.research_agent = research_agent
self.clinical_agent = clinical_agent
self.formulation_agent = formulation_agent
def run_drug_discovery_pipeline(self, dose_response_data, drug_properties_data, excipients_suggestion_data): # Pass excipients_suggestion_data
print("--- Starting Drug Discovery Pipeline ---")
# 1. Data Acquisition
print("\n--- 1. Data Acquisition ---")
self.data_agent.fetch_blog_data()
# Example: Fetching DrugBank and Clinical Trial data (if APIs are configured - uncomment to use if you have API keys)
# self.data_agent.fetch_drug_data_from_api("curcumin") # Example drug fetch
# clinical_trial_query = {'expr': 'cancer+curcumin+methylene blue', 'fmt': 'json', 'min_rnk': 1, 'max_rnk': 5}
# self.data_agent.fetch_clinical_trial_data_api(clinical_trial_query)
# 2. Data Scientist Agent Analysis
print("\n--- 2. Data Scientist Agent Analysis ---")
self.data_scientist_agent.analyze_drug_efficacy_data()
self.data_scientist_agent.perform_gnn_model_analysis()
self.data_scientist_agent.analyze_dose_response_curves(dose_response_data) # Analyze dose-response data
# 3. Research Agent Activities
print("\n--- 3. Research Agent Activities ---")
self.research_agent.research_tp53_pathway()
self.research_agent.research_curcumin_methylene_blue_synergy()
# 4. Clinical Agent Analysis
print("\n--- 4. Clinical Agent Analysis ---")
# self.clinical_agent.analyze_clinical_trial_relevance() # If clinical trial data is fetched - uncomment if used
self.clinical_agent.assess_dosage_and_side_effects()
# 5. Formulation Agent Analysis and Suggestion
print("\n--- 5. Formulation Agent Analysis and Suggestion ---")
self.formulation_agent.analyze_ph_data()
self.formulation_agent.analyze_drug_properties(drug_properties_data) # Analyze drug properties
# Pass excipients suggestion data to Formulation Agent (even if suggestion is based on hardcoded data for now)
self.formulation_agent.excipients_data["curcumin"] = excipients_suggestion_data.get("curcumin", []) # Set excipient suggestion in agent
self.formulation_agent.suggest_drug_formulation()
print("\n--- Drug Discovery Pipeline Completed (Skeleton) ---")
# --- Main Execution ---
if __name__ == "__main__":
# 1. Initialize Knowledge Base
kb = KnowledgeBase()
# 2. Initialize Agents
data_agent = DataAcquisitionAgent(kb, BLOG_URLS, API_ENDPOINTS, API_KEYS)
data_scientist_agent = DataScientistAgent(kb)
research_agent = ResearchAgent(kb)
clinical_agent = ClinicalAgent(kb)
formulation_agent = FormulationAgent(kb, curcumin_ph_data, methylene_blue_ph_data, excipients_data) # Pass excipients_data
# 3. Initialize Orchestrator
orchestrator = Orchestrator(kb, data_agent, data_scientist_agent, research_agent, clinical_agent, formulation_agent)
# 4. Excipient Suggestion Data (Hardcoded for this example - could be from analysis or database)
excipients_suggestion_data = {
"curcumin": ["p188", "p407", "piperine"] # Suggest P188, P407, and Piperine for curcumin
# "methylene_blue": [] # No excipients suggested for methylene blue for now
}
# 5. Run the Drug Discovery Pipeline, passing dose-response, drug properties, and excipient suggestion data
orchestrator.run_drug_discovery_pipeline(dose_response_data, drug_properties_data, excipients_suggestion_data) # Pass excipients_suggestion_data
# 6. (Optional) Access data from Knowledge Base after pipeline run
print("\n--- Knowledge Base Summary (Example) ---")
print("Optimal Formulation pH:", kb.retrieve_data('optimal_formulation_ph'))
print("Formulation Suggestion:", kb.retrieve_data('formulation_suggestion'))
print("Methylene Blue Dose Considerations:", kb.retrieve_data('methylene_blue_dose_considerations')) # Example - retrieve from KB
print("Curcumin Dose Considerations:", kb.retrieve_data('curcumin_dose_considerations')) # Example - retrieve from KB
# ... (Print other relevant data from KB)
OUTPUT:
--- Starting Drug Discovery Pipeline --- --- 1. Data Acquisition --- Fetched content from: https://mrinmoych.blogspot.com/2025/02/tp53-kegg-pathway.html Parsed and structured data from tp53_kegg_pathway Fetched content from: https://mrinmoych.blogspot.com/2025/02/protein-protein-interactions-code.html Parsed and structured data from protein_protein_interactions Fetched content from: https://mrinmoych.blogspot.com/2025/02/molcular-and-cellular-related-code.html Parsed and structured data from molecular_cellular_code Fetched content from: https://mrinmoych.blogspot.com/2025/02/disease-gene-association-code.html Parsed and structured data from disease_gene_association_code Fetched content from: https://mrinmoych.blogspot.com/2025/02/drug-prediction-score-drug-distribution.html Parsed and structured data from drug_prediction_distribution Fetched content from: https://mrinmoych.blogspot.com/2025/02/drug-efficacy-dose-response.html Parsed and structured data from drug_efficacy_dose_response Fetched content from: https://mrinmoych.blogspot.com/2025/02/actual-vs-predicted-binding-affinity.html Parsed and structured data from binding_affinity_prediction Fetched content from: https://mrinmoych.blogspot.com/2025/02/predicted-toxicity-curcumin-vs.html Parsed and structured data from toxicity_prediction_curcumin Fetched content from: https://mrinmoych.blogspot.com/2025/03/gnn-ml-model-code-with-optuna-tuning.html Parsed and structured data from gnn_ml_model_optuna Fetched content from: https://mrinmoych.blogspot.com/2025/03/md-ml-gnn-svm-random-forest-gradient.html Parsed and structured data from md_ml_gnn_svm_rf_gb --- 2. Data Scientist Agent Analysis --- --- Data Scientist Agent: Analyzing Drug Efficacy Data from Blog --- Keywords for efficacy analysis not prominently found in blog text. No GNN ML model code blocks found in Knowledge Base. --- Data Scientist Agent: Analyzing Dose-Response Curves --- --- Dose-Response Data for Methylene_blue --- Concentrations (M): [1e-06, 1e-05, 0.0001, 0.001] Responses (%): [10, 30, 55, 90] Approximate Response Range: 80.00% Placeholder for EC50 calculation or curve fitting would go here... --- Dose-Response Data for Curcumin --- Concentrations (M): [1e-06, 1e-05, 0.0001, 0.001] Responses (%): [5, 25, 50, 85] Approximate Response Range: 80.00% Placeholder for EC50 calculation or curve fitting would go here... --- 3. Research Agent Activities --- --- Research Agent: TP53 Pathway Research --- Found 13 sentences related to TP53 pathway keywords: - neural_network import MLPRegressor # Example DataFrame data = { "Pathway": ["hsa04115", "hsa01524", "hsa05220", "hsa05210", "hsa04215", "hsa04210", "hsa05206", "hsa04110", "hsa05216", "hsa05214", "hsa05218", "hsa05219", "hsa05222", "hsa05169", "hsa01522", "hsa05213", "hsa04218", "hsa05223", "hsa05202", "hsa05212", "hsa05200", "hsa05162", "hsa05226", "hsa05163", "hsa05161", "hsa05225", "hsa04722", "hsa05203", "hsa05217", "hsa04068", "hsa05166", "hsa05224", "hsa05160", "hsa05215", "hsa05167", "hsa04071", "hsa05131", "hsa04217", "hsa04012", "hsa05205", "hsa05165", "hsa05170", "hsa05132", "hsa04064", "hsa04151", "hsa04919", "hsa05418", "hsa04390", "hsa04630", "hsa05164", "hsa05152", "hsa05416", "hsa05130", "hsa05014", "hsa05230", "hsa01521", "hsa04211", "hsa04933", "hsa05168", "hsa04066", "hsa04928", "hsa04010", "hsa05016", "hsa04650", "hsa04932", "hsa04934", "hsa04310"], "Description": ["p53 signaling pathway", "Platinum drug resistance", "Chronic myeloid leukemia", "Colorectal cancer", "Apoptosis - multiple species", "Apoptosis", "MicroRNAs in cancer", "Cell cycle", "Thyroid cancer", "Glioma", "Melanoma", "Bladder cancer", "Small cell lung cancer", "Epstein-Barr virus infection", "Endocrine resistance", "Endometrial cancer", "Cellular senescence", "Non-small cell lung cancer", "Transcriptional misregulation in cancer", "Pancreatic cancer", "Pathways in cancer", "Measles", "Gastric cancer", "Human cytomegalovirus infection", "Hepatitis B", "Hepatocellular carcinoma", "Neurotrophin signaling pathway", "Viral carcinogenesis", "Basal cell carcinoma", "FoxO signaling pathway", "Human T-cell leukemia virus 1 infection", "Breast cancer", "Hepatitis C", "Prostate cancer", "Kaposi sarcoma-associated herpesvirus infection", "Sphingolipid signaling pathway", "Shigellosis", "Necroptosis", "ErbB signaling pathway", "Proteoglycans in cancer", "Human papillomavirus infection", "Human immunodeficiency virus 1 infection", "Salmonella infection", "NF-kappa B signaling pathway", "PI3K-Akt signaling pathway", "Thyroid hormone signaling pathway", "Fluid shear stress and atherosclerosis", "Hippo signaling pathway", "JAK-STAT signaling pathway", "Influenza A", "Tuberculosis", "Viral myocarditis", "Pathogenic Escherichia coli infection", "Amyotrophic lateral sclerosis", "Central carbon metabolism in cancer", "EGFR tyrosine kinase inhibitor resistance", "Longevity regulating pathway", "AGE-RAGE signaling pathway in diabetic complications", "Herpes simplex virus 1 infection", "HIF-1 signaling pathway", "Parathyroid hormone synthesis, secretion and action", "MAPK signaling pathway", "Huntington disease", "Natural killer cell mediated cytotoxicity", "Non-alcoholic fatty liver disease", "Cushing syndrome", "Wnt signaling pathway"], "Values": ["12 of 72", "10 of 70", "8 of 75", "8 of 82", "6 of 30", "9 of 131", "9 of 159", "8 of 120", "5 of 37", "6 of 71", "6 of 72", "5 of 40", "6 of 92", "8 of 192", "6 of 94", "5 of 58", "7 of 150", "5 of 68", "7 of 171", "5 of 71", "12 of 515", "6 of 137", "6 of 146", "7 of 217", "6 of 158", "6 of 161", "5 of 112", "6 of 183", "4 of 63", "5 of 126", "6 of 210", "5 of 146", "5 of 157", "4 of 97", "5 of 187", "4 of 116", "5 of 218", "4 of 147", "3 of 81", "4 of 194", "5 of 324", "4 of 203", "4 of 209", "3 of 101", "5 of 349", "3 of 120", "3 of 129", "3 of 154", "3 of 158", "3 of 163", "3 of 165", "2 of 55", "3 of 187", "4 of 350", "2 of 68", "2 of 77", "2 of 87", "2 of 96", "4 of 478", "2 of 102", "2 of 104", "3 of 286", "3 of 295", "2 of 120", "2 of 146", "2 of 153", "2 of 154"], "P-Value": ["2... - 00039417599079070856 TP53 KEGG Pathway Analysis KEGG pathway analysis was performed to understand the potential of Curcumin and Methylene Blue to modulate the TP53 signalling pathway... - pyplot as plt import numpy as np import pandas as pd # TP53 KEGG Pathway tp53_pathway_components = ['TP53', 'MDM2', 'p21', 'BAX', 'PUMA'] tp53_pathway_interactions = [('TP53', 'MDM2'), ('TP53', 'p21'), ('TP53', 'BAX'), ('TP53', 'PUMA')] plt... - title('TP53 KEGG Pathway', fontsize=16) # Node positions - adjust as needed for better visualization pos = {'TP53': (0, 0), 'MDM2': (2, 1), 'p21': (2, -1), 'BAX': (4, 0... - 5)} # Draw nodes for component in tp53_pathway_components: plt... --- Research Agent: Curcumin & Methylene Blue Synergy Research --- No strong indications of synergy keywords found in toxicity blog text. --- 4. Clinical Agent Analysis --- --- Clinical Agent: Dosage and Side Effect Assessment --- Efficacy/Dose blog text does not explicitly mention dosage information. Toxicity blog text discusses toxicity and/or side effects. Further analysis needed. --- 5. Formulation Agent Analysis and Suggestion --- --- Formulation Agent: pH Data Analysis --- Curcumin pH Data: {'pH_values': [3, 3.6, 4.2, 4.8, 5.4, 6, 6.6, 7.2, 7.8, 8.4, 9, 9.6], 'microspecies_distribution': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [100, 100, 100, 100, 100, 100, 100, 99, 97, 89, 62, 32], [0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 4], [0, 0, 0, 0, 0, 0, 0, 0, 1, 5, 20, 8], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 19], [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 19], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 10], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 34]]} Methylene Blue pH Data: {'pH_ranges': ['0-3.0', '3.4', '4.2', '4.8', '5.4', '6.0', '6.6', '7.2', '7.8', '8.4', '9.0-9.6'], 'microspecies_distribution': [{'Query1': 0, 'Query2': 0, 'Query3': 0, 'RedQuery': 0}, {'Query1': 0, 'Query2': 5, 'Query3': 0, 'RedQuery': 0}, {'Query1': 10, 'Query2': 10, 'Query3': 2, 'RedQuery': 0}, {'Query1': 20, 'Query2': 15, 'Query3': 4, 'RedQuery': 0}, {'Query1': 40, 'Query2': 20, 'Query3': 6, 'RedQuery': 0}, {'Query1': 60, 'Query2': 25, 'Query3': 8, 'RedQuery': 0}, {'Query1': 70, 'Query2': 30, 'Query3': 10, 'RedQuery': 0}, {'Query1': 80, 'Query2': 35, 'Query3': 12, 'RedQuery': 0}, {'Query1': 90, 'Query2': 40, 'Query3': 15, 'RedQuery': 0}, {'Query1': 95, 'Query2': 45, 'Query3': 20, 'RedQuery': 0}, {'Query1': 100, 'Query2': '50-60', 'Query3': '25-30', 'RedQuery': '0-10'}]} Determined optimal formulation pH (example): 6.5 --- Formulation Agent: Analyzing Drug Properties --- --- Properties for Methylene_blue --- AlogP: -0.5, TPSA: 19.14, Molecular Weight: 319.86 No specific dose/formulation considerations identified based on AlogP and TPSA (using basic rules). --- Properties for Curcumin --- AlogP: 3.37, TPSA: 93.06, Molecular Weight: 368.39 Dose Formulation Considerations: - AlogP suggests potential lower oral bioavailability, may require higher dose or alternative delivery. - High TPSA suggests potentially lower membrane permeability, consider formulation for enhanced absorption. --- Formulation Agent: Formulation Suggestion --- Suggesting formulation of Curcumin and Methylene Blue at pH 6.5 for potentially improved stability and efficacy. Further excipient selection needed based on solubility, delivery pathway, and considering: - Curcumin: AlogP suggests potential lower oral bioavailability, may require higher dose or alternative delivery., High TPSA suggests potentially lower membrane permeability, consider formulation for enhanced absorption. Considering excipients: - For Curcumin: Solvent-free co-grinding with Poloxamer 188 (P188) or Poloxamer 407 (P407) can enhance bioavailability. - For Curcumin: Piperine is known to significantly enhance bioavailability. Consider excipients: Poloxamer 188 (P188), Poloxamer 407 (P407), Piperine for solubility and/or bioavailability enhancement. --- Drug Discovery Pipeline Completed (Skeleton) --- --- Knowledge Base Summary (Example) --- Optimal Formulation pH: 6.5 Formulation Suggestion: Suggesting formulation of Curcumin and Methylene Blue at pH 6.5 for potentially improved stability and efficacy. Further excipient selection needed based on solubility, delivery pathway, and considering: - Curcumin: AlogP suggests potential lower oral bioavailability, may require higher dose or alternative delivery., High TPSA suggests potentially lower membrane permeability, consider formulation for enhanced absorption. Considering excipients: - For Curcumin: Solvent-free co-grinding with Poloxamer 188 (P188) or Poloxamer 407 (P407) can enhance bioavailability. - For Curcumin: Piperine is known to significantly enhance bioavailability. Consider excipients: Poloxamer 188 (P188), Poloxamer 407 (P407), Piperine for solubility and/or bioavailability enhancement. Methylene Blue Dose Considerations: None Curcumin Dose Considerations: ['AlogP suggests potential lower oral bioavailability, may require higher dose or alternative delivery.', 'High TPSA suggests potentially lower membrane permeability, consider formulation for enhanced absorption.']
CURVE PLOT CODE:
import requests from bs4 import BeautifulSoup import json import os import pandas as pd import numpy as np # For potential numerical analysis of dose-response import matplotlib.pyplot as plt # Import matplotlib for plotting # --- Configuration --- API_KEY = "AIzaSyDE9u99ETiwJMhYGgvz6CN2uXAJLOSAeMw" # Directly embedding - use environment variables in production # API_KEY = os.environ.get("YOUR_API_KEY_ENV_VAR") # Recommended for production BLOG_URLS = { 'tp53_kegg_pathway': "https://mrinmoych.blogspot.com/2025/02/tp53-kegg-pathway.html", 'protein_protein_interactions': "https://mrinmoych.blogspot.com/2025/02/protein-protein-interactions-code.html", 'molecular_cellular_code': "https://mrinmoych.blogspot.com/2025/02/molcular-and-cellular-related-code.html", 'disease_gene_association_code': "https://mrinmoych.blogspot.com/2025/02/disease-gene-association-code.html", 'drug_prediction_distribution': "https://mrinmoych.blogspot.com/2025/02/drug-prediction-score-drug-distribution.html", 'drug_efficacy_dose_response': "https://mrinmoych.blogspot.com/2025/02/drug-efficacy-dose-response.html", 'binding_affinity_prediction': "https://mrinmoych.blogspot.com/2025/02/actual-vs-predicted-binding-affinity.html", 'toxicity_prediction_curcumin': "https://mrinmoych.blogspot.com/2025/02/predicted-toxicity-curcumin-vs.html", 'gnn_ml_model_optuna': "https://mrinmoych.blogspot.com/2025/03/gnn-ml-model-code-with-optuna-tuning.html", 'md_ml_gnn_svm_rf_gb': "https://mrinmoych.blogspot.com/2025/03/md-ml-gnn-svm-random-forest-gradient.html" } API_ENDPOINTS = { "drugbank": "https://api.drugbank.com/v1/drugs/", # Example DrugBank API endpoint (replace with actual if used) "clinicaltrials": "https://clinicaltrials.gov/api/query/full_studies", # Example ClinicalTrials.gov API # ... Add other API endpoints as needed ... } API_KEYS = { "drugbank": "YOUR_DRUGBANK_API_KEY", # Replace with your actual DrugBank API key if used # ... Add API keys for other services if needed ... } # --- pH Data --- curcumin_ph_data = { 'pH_values': [3, 3.6, 4.2, 4.8, 5.4, 6, 6.6, 7.2, 7.8, 8.4, 9, 9.6], 'microspecies_distribution': [ [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # Query [100, 100, 100, 100, 100, 100, 100, 99, 97, 89, 62, 32], # 1 [0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 7, 4], # 2 [0, 0, 0, 0, 0, 0, 0, 0, 1, 5, 20, 8], # 3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 4, 19], # 4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 3, 19], # 5 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 10], # 6 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 34] # 7 ] } methylene_blue_ph_data = { 'pH_ranges': ["0-3.0", "3.4", "4.2", "4.8", "5.4", "6.0", "6.6", "7.2", "7.8", "8.4", "9.0-9.6"], 'microspecies_distribution': [ {"Query1": 0, "Query2": 0, "Query3": 0, "RedQuery": 0}, # pH 0-3.0 {"Query1": 0, "Query2": 5, "Query3": 0, "RedQuery": 0}, # pH 3.4 {"Query1": 10, "Query2": 10, "Query3": 2, "RedQuery": 0},# pH 4.2 {"Query1": 20, "Query2": 15, "Query3": 4, "RedQuery": 0},# pH 4.8 {"Query1": 40, "Query2": 20, "Query3": 6, "RedQuery": 0},# pH 5.4 {"Query1": 60, "Query2": 25, "Query3": 8, "RedQuery": 0},# pH 6.0 {"Query1": 70, "Query2": 30, "Query3": 10, "RedQuery": 0},# pH 6.6 {"Query1": 80, "Query2": 35, "Query3": 12, "RedQuery": 0},# pH 7.2 {"Query1": 90, "Query2": 40, "Query3": 15, "RedQuery": 0},# pH 7.8 {"Query1": 95, "Query2": 45, "Query3": 20, "RedQuery": 0},# pH 8.4 {"Query1": 100, "Query2": "50-60", "Query3": "25-30", "RedQuery": "0-10"} # pH 9.0-9.6 ] } # --- Dose-Response Data (PLACEHOLDER - REPLACE WITH ACTUAL DATA) --- dose_response_data = { "methylene_blue": { "concentrations_molar": [1e-6, 1e-5, 1e-4, 1e-3], # Molar concentrations "responses_percent": [10, 30, 55, 90] # Corresponding responses (%) }, "curcumin": { # Assuming "Other Drug" is Curcumin "concentrations_molar": [1e-6, 1e-5, 0.0001, 0.001], "responses_percent": [5, 25, 50, 85] } } # --- Drug Properties Data --- drug_properties_data = { "methylene_blue": { "molecular_weight": 319.86, "rotatable_bonds": 1, "alogp": -0.50, "tpsa": 19.14, "h_bond_acceptors": 3, "h_bond_donors": 0 }, "curcumin": { "molecular_weight": 368.39, "rotatable_bonds": 8, "alogp": 3.37, "tpsa": 93.06, "h_bond_acceptors": 6, "h_bond_donors": 2 } } # --- Excipients Data --- excipients_data = { "p188": { "name": "Poloxamer 188 (P188)", "type": "Surfactant, Solubility Enhancer, Co-grinding Agent", "description": "Non-ionic surfactant, can improve solubility and bioavailability, reduce aggregation. Useful in solvent-free co-grinding to enhance curcumin bioavailability.", # Updated description # ... Add more properties if needed (e.g., concentration range, safety profile) ... }, "p407": { "name": "Poloxamer 407 (P407)", "type": "Surfactant, Viscosity Modifier, Thermosensitive Gel, Co-grinding Agent", "description": "Non-ionic surfactant, can form thermosensitive gels, used for controlled release, improve solubility. Useful in solvent-free co-grinding to enhance curcumin bioavailability.", # Updated description # ... Add more properties if needed ... }, "piperine": { "name": "Piperine", "type": "Bioavailability Enhancer", "description": "Natural compound from black pepper, known to significantly enhance curcumin bioavailability, likely by inhibiting metabolism.", # Added Piperine "bioavailability_enhancement": "Significant for Curcumin" # Specific property for Piperine # ... Add more properties if needed ... } } # --- Knowledge Base --- class KnowledgeBase: def __init__(self): self.data = {} def store_data(self, key, value): self.data[key] = value def retrieve_data(self, key): return self.data.get(key) # --- API Request Function with API Key (for Blog - Example - adjust as needed) --- def api_request_with_key(url, api_key=API_KEY): """Fetches content from a URL, embedding API key (example - adjust based on API requirements).""" try: url_with_key = f"{url}?key={api_key}" # Example: API key as query parameter response = requests.get(url_with_key) response.raise_for_status() return response.text except requests.exceptions.RequestException as e: print(f"Error fetching URL {url} with API key: {e}") return None # --- Data Acquisition Agent (DAA) --- class DataAcquisitionAgent: def __init__(self, knowledge_base, blog_urls, api_endpoints=None, api_keys=None): self.knowledge_base = knowledge_base self.blog_urls = blog_urls self.api_endpoints = api_endpoints or {} self.api_keys = api_keys or {} def fetch_blog_data(self): """Fetches and stores data from blog URLs, parsing code and text.""" for name, url in self.blog_urls.items(): content = api_request_with_key(url) # Fetch using API key function if content: print(f"Fetched content from: {url}") self.knowledge_base.store_data(name + '_raw_html', content) self._parse_and_structure_blog_data(name) def _parse_and_structure_blog_data(self, name): """Parses HTML content to extract code and main text (private helper method).""" raw_html = self.knowledge_base.retrieve_data(name + '_raw_html') if raw_html: soup = BeautifulSoup(raw_html, 'html.parser') code_blocks = [] for code_tag in soup.find_all(['pre', 'code']): # Find both <pre> and <code> for code code_blocks.append(code_tag.text.strip()) self.knowledge_base.store_data(name + '_code_blocks', code_blocks) main_text_paragraphs = [] for p_tag in soup.find_all('p'): if not p_tag.find_parent(['pre', 'code']): # Exclude text in code blocks main_text_paragraphs.append(p_tag.text.strip()) main_text_content = "\n".join(main_text_paragraphs) self.knowledge_base.store_data(name + '_main_text', main_text_content) print(f"Parsed and structured data from {name}") def fetch_drug_data_from_api(self, drug_name): """Fetches drug data from DrugBank API (example - adapt as needed).""" endpoint = self.api_endpoints.get("drugbank") api_key = self.api_keys.get("drugbank") if not endpoint or not api_key: print("DrugBank API endpoint or key not configured.") return None headers = {'Authorization': f'Bearer {api_key}'} # Example: Bearer token auth url = f"{endpoint}?query={drug_name}" # Example query - check API docs try: response = requests.get(url, headers=headers) response.raise_for_status() drug_api_data = response.json() self.knowledge_base.store_data(f'drugbank_data_{drug_name}', drug_api_data) # Store in KB print(f"Fetched DrugBank data for {drug_name}") return drug_api_data except requests.exceptions.RequestException as e: print(f"Error fetching drug data from DrugBank: {e}") return None def fetch_clinical_trial_data_api(self, query_params): """Fetches clinical trial data from ClinicalTrials.gov API (example).""" endpoint = self.api_endpoints.get("clinicaltrials") if not endpoint: print("ClinicalTrials.gov API endpoint not configured.") return None try: response = requests.get(endpoint, params=query_params) response.raise_for_status() clinical_trial_data = response.json() self.knowledge_base.store_data('clinical_trial_data', clinical_trial_data) # Store in KB print("Fetched Clinical Trial data") return clinical_trial_data except requests.exceptions.RequestException as e: print(f"Error fetching clinical trial data: {e}") return None # --- Data Scientist Agent --- class DataScientistAgent: def __init__(self, knowledge_base): self.knowledge_base = knowledge_base def analyze_drug_efficacy_data(self): """Example: Analyzes drug efficacy data from blog and potentially external sources.""" efficacy_blog_text = self.knowledge_base.retrieve_data('drug_efficacy_dose_response_main_text') if efficacy_blog_text: print("\n--- Data Scientist Agent: Analyzing Drug Efficacy Data from Blog ---") # Example: Basic keyword analysis (replace with actual ML/statistical analysis) if "dose-response" in efficacy_blog_text.lower() or "IC50" in efficacy_blog_text.upper(): print("Keywords 'dose-response' or 'IC50' found in efficacy blog text. Potentially relevant data.") else: print("Keywords for efficacy analysis not prominently found in blog text.") # Example: Analyze drug prediction scores (from blog code blocks - illustrative) prediction_code_blocks = self.knowledge_base.retrieve_data('drug_prediction_distribution_code_blocks') if prediction_code_blocks: print("\n--- Data Scientist Agent: Analyzing Drug Prediction Code ---") for code in prediction_code_blocks: if "RandomForestRegressor" in code or "GNN" in code: # Example keyword check for ML models print("Code block potentially uses ML models (RandomForestRegressor or GNN) for drug prediction.") # In real system, you could try to execute/analyze code, or extract model parameters # Example: Analyze clinical trial data (if fetched and available) clinical_trial_data = self.knowledge_base.retrieve_data('clinical_trial_data') if clinical_trial_data and clinical_trial_data.get('FullStudiesResponse'): studies_count = clinical_trial_data['FullStudiesResponse'].get('NStudiesFound', 0) print(f"\n--- Data Scientist Agent: Analyzing Clinical Trial Data ---") print(f"ClinicalTrials.gov API returned {studies_count} studies. Further analysis needed on study details.") # ... (Further, more detailed analysis of clinical trial data using pandas, statistics, etc. would go here) def analyze_dose_response_curves(self, dose_response_data): """Analyzes the provided dose-response curve data.""" print("\n--- Data Scientist Agent: Analyzing Dose-Response Curves ---") if dose_response_data: for drug, data in dose_response_data.items(): print(f"\n--- Dose-Response Data for {drug.capitalize()} ---") concentrations = data["concentrations_molar"] responses = data["responses_percent"] print("Concentrations (M):", concentrations) print("Responses (%):", responses) # --- Example: Basic analysis - Calculate approximate max response difference --- max_response = max(responses) min_response = min(responses) response_range = max_response - min_response print(f"Approximate Response Range: {response_range:.2f}%") # --- Example: Placeholder for EC50 calculation or curve fitting --- # In a real system, you would use libraries like scipy.optimize to fit a sigmoidal curve # and calculate EC50. For this example, just noting the placeholder. print("Placeholder for EC50 calculation or curve fitting would go here...") else: print("No dose-response data provided for analysis.") def plot_dose_response_curves(self, dose_response_data): """Generates and displays a plot of dose-response curves.""" print("\n--- Data Scientist Agent: Plotting Dose-Response Curves ---") plt.figure(figsize=(8, 6)) # Adjust figure size as needed if dose_response_data: for drug, data in dose_response_data.items(): concentrations = data["concentrations_molar"] responses = data["responses_percent"] plt.plot(concentrations, responses, label=drug.capitalize()) # Plotting each drug plt.xscale('log') # Set x-axis to logarithmic scale plt.xlabel('Drug Concentration (M)') plt.ylabel('Response (%)') plt.title('Dose-Response Curves') plt.legend() plt.grid(True) # Add grid for better readability plt.show() # Display the plot else: print("No dose-response data available to plot.") def perform_gnn_model_analysis(self): """Example: Analysis focused on GNN ML models from blog content.""" gnn_model_code = self.knowledge_base.retrieve_data('gnn_ml_model_optuna_code_blocks') if gnn_model_code: print("\n--- Data Scientist Agent: Analyzing GNN ML Model Code ---") for code in gnn_model_code: if "GraphNeuralNetwork" in code or "DGL" in code or "PyTorch Geometric" in code: # Keywords for GNN frameworks print("Code block appears to contain Graph Neural Network model implementation (using DGL or PyTorch Geometric).") # ... (Further analysis: extract model architecture, layers, optimization methods, etc. from code) else: print("No GNN ML model code blocks found in Knowledge Base.") # --- Research Agent --- (No changes in ResearchAgent and ClinicalAgent for this step) class ResearchAgent: # ... (ResearchAgent class - no changes) ... def __init__(self, knowledge_base): self.knowledge_base = knowledge_base def research_tp53_pathway(self): """Researches TP53 pathway using blog text content.""" tp53_text = self.knowledge_base.retrieve_data('tp53_kegg_pathway_main_text') if tp53_text: print("\n--- Research Agent: TP53 Pathway Research ---") # Example: Basic keyword-based research simulation keywords = ["TP53", "apoptosis", "cell cycle arrest", "DNA repair", "cancer"] relevant_sentences = [] for sentence in tp53_text.split('.'): # Very basic sentence splitting for keyword in keywords: if keyword.lower() in sentence.lower(): relevant_sentences.append(sentence.strip()) break # Avoid adding sentence multiple times if multiple keywords present if relevant_sentences: print(f"Found {len(relevant_sentences)} sentences related to TP53 pathway keywords:") for sentence in relevant_sentences[:5]: # Print first few relevant sentences print(f"- {sentence}...") else: print("No sentences with TP53 pathway keywords found in blog text.") else: print("No TP53 pathway text data available for research.") def research_curcumin_methylene_blue_synergy(self): """Example: Simulates research on Curcumin and Methylene Blue synergy (using blog texts).""" toxicity_text = self.knowledge_base.retrieve_data('toxicity_prediction_curcumin_main_text') # Example blog for synergy clues if toxicity_text: print("\n--- Research Agent: Curcumin & Methylene Blue Synergy Research ---") synergy_keywords = ["synergy", "combination therapy", "combined effect", "enhance efficacy", "reduce toxicity"] synergy_mentions = 0 for keyword in synergy_keywords: if keyword.lower() in toxicity_text.lower(): synergy_mentions += 1 if synergy_mentions > 0: print(f"Keywords related to synergy found {synergy_mentions} times in toxicity blog text. Potential indication of synergy.") else: print("No strong indications of synergy keywords found in toxicity blog text.") else: print("No toxicity prediction blog text available for synergy research.") # --- Clinical Agent --- (No changes in ClinicalAgent for this step) class ClinicalAgent: # ... (ClinicalAgent class - no changes) ... def __init__(self, knowledge_base): self.knowledge_base = knowledge_base def analyze_clinical_trial_relevance(self): """Analyzes clinical trial data for relevance to Curcumin & Methylene Blue in cancer.""" clinical_trial_data = self.knowledge_base.retrieve_data('clinical_trial_data') if clinical_trial_data and clinical_trial_data.get('FullStudiesResponse'): studies = clinical_trial_data['FullStudiesResponse'].get('FullStudies', []) relevant_trial_count = 0 for study in studies: study_data = study['Study'] if 'BriefTitle' in study_data and ('curcumin' in study_data['BriefTitle'].lower() or 'methylene blue' in study_data['BriefTitle'].lower()): relevant_trial_count += 1 print(f"\n--- Clinical Agent: Potentially Relevant Clinical Trial Found ---") print(f"NCT ID: {study_data['NCTId']}") print(f"Title: {study_data['BriefTitle']}") print(f"Status: {study_data['OverallStatus']}") # ... (Further analysis: extract study phase, conditions, interventions, outcomes etc.) if relevant_trial_count > 0: print(f"\nClinical Agent: Found {relevant_trial_count} potentially relevant clinical trials involving Curcumin or Methylene Blue.") else: print("\nClinical Agent: No directly relevant clinical trials for Curcumin or Methylene Blue found in initial search.") else: print("Clinical Agent: No Clinical Trial data available for analysis.") def assess_dosage_and_side_effects(self): """Example: Assesses dosage and side effect information (using blog - illustrative).""" efficacy_dose_text = self.knowledge_base.retrieve_data('drug_efficacy_dose_response_main_text') toxicity_text = self.knowledge_base.retrieve_data('toxicity_prediction_curcumin_main_text') print("\n--- Clinical Agent: Dosage and Side Effect Assessment ---") if efficacy_dose_text: if "dosage" in efficacy_dose_text.lower() or "dose" in efficacy_dose_text.lower(): print("Efficacy/Dose blog text mentions dosage information. Further extraction needed.") # More sophisticated extraction needed else: print("Efficacy/Dose blog text does not explicitly mention dosage information.") if toxicity_text: if "toxicity" in toxicity_text.lower() or "side effect" in toxicity_text.lower() or "adverse event" in toxicity_text.lower(): print("Toxicity blog text discusses toxicity and/or side effects. Further analysis needed.") # More sophisticated analysis else: print("Toxicity blog text does not explicitly mention toxicity or side effects.") else: print("No toxicity blog text available for side effect assessment.") # --- Formulation Agent --- (No changes in FormulationAgent for this step) class FormulationAgent: # ... (FormulationAgent class - no changes) ... def __init__(self, knowledge_base, curcumin_ph_data, methylene_blue_ph_data, excipients_data): # Added excipients_data self.knowledge_base = knowledge_base self.curcumin_ph_data = curcumin_ph_data self.methylene_blue_ph_data = methylene_blue_ph_data self.excipients_data = excipients_data # Store excipients data def analyze_ph_data(self): print("\n--- Formulation Agent: pH Data Analysis ---") print("Curcumin pH Data:", self.curcumin_ph_data) print("Methylene Blue pH Data:", self.methylene_blue_ph_data) optimal_ph = 6.5 # Example - determine based on more detailed analysis self.knowledge_base.store_data('optimal_formulation_ph', optimal_ph) print(f"Determined optimal formulation pH (example): {optimal_ph}") def analyze_drug_properties(self, drug_properties): """Analyzes drug properties (TPSA, AlogP) for formulation and dose considerations (basic example).""" print("\n--- Formulation Agent: Analyzing Drug Properties ---") if drug_properties: for drug_name, properties in drug_properties.items(): print(f"\n--- Properties for {drug_name.capitalize()} ---") alogp = properties.get("alogp") tpsa = properties.get("tpsa") mw = properties.get("molecular_weight") print(f"AlogP: {alogp}, TPSA: {tpsa}, Molecular Weight: {mw}") # --- Very simplistic dose consideration based on AlogP and TPSA --- dose_considerations = [] if alogp > 3: # Example rule: Higher AlogP, potential lower oral bioavailability dose_considerations.append("AlogP suggests potential lower oral bioavailability, may require higher dose or alternative delivery.") if tpsa > 90: # Example rule: High TPSA, potentially lower membrane permeability dose_considerations.append("High TPSA suggests potentially lower membrane permeability, consider formulation for enhanced absorption.") if dose_considerations: print("\nDose Formulation Considerations:") for consideration in dose_considerations: print(f"- {consideration}") self.knowledge_base.store_data(f'{drug_name}_dose_considerations', dose_considerations) # Store in KB else: print("\nNo specific dose/formulation considerations identified based on AlogP and TPSA (using basic rules).") else: print("No drug properties data provided for analysis.") def suggest_drug_formulation(self): optimal_ph = self.knowledge_base.retrieve_data('optimal_formulation_ph') formulation_considerations_mb = self.knowledge_base.retrieve_data('methylene_blue_dose_considerations') # Example - retrieve dose considerations formulation_considerations_curcumin = self.knowledge_base.retrieve_data('curcumin_dose_considerations') excipient_suggestion = f"Considering excipients:\n" # Introduce excipient section suggested_excipients_curcumin = [] # Initialize list to hold suggestions if "curcumin" in self.excipients_data: if "p188" in self.excipients_data and "p407" in self.excipients_data: suggested_excipients_curcumin.extend(["p188", "p407"]) # Suggest Poloxamers for co-grinding and solubility excipient_suggestion += f"- For Curcumin: Solvent-free co-grinding with {excipients_data['p188']['name']} or {excipients_data['p407']['name']} can enhance bioavailability.\n" # Co-grinding suggestion if "piperine" in self.excipients_data: suggested_excipients_curcumin.append("piperine") # Suggest Piperine for bioavailability excipient_suggestion += f"- For Curcumin: {excipients_data['piperine']['name']} is known to significantly enhance bioavailability.\n" # Piperine suggestion if suggested_excipients_curcumin: excipient_suggestion += f" Consider excipients: {', '.join([excipients_data[excipient]['name'] for excipient in suggested_excipients_curcumin])} for solubility and/or bioavailability enhancement.\n" else: excipient_suggestion += "- For Curcumin: No specific excipients currently suggested based on analysis.\n" # Default if no excipients are suggested else: excipient_suggestion += "- For Curcumin: No excipient data available.\n" if optimal_ph is not None: formulation_suggestion = f"Suggesting formulation of Curcumin and Methylene Blue at pH {optimal_ph} for potentially improved stability and efficacy.\n" \ f"Further excipient selection needed based on solubility, delivery pathway, and considering:\n" if formulation_considerations_mb: formulation_suggestion += f"- Methylene Blue: {', '.join(formulation_considerations_mb)}\n" if formulation_considerations_curcumin: formulation_suggestion += f"- Curcumin: {', '.join(formulation_considerations_curcumin)}\n" formulation_suggestion += "\n" + excipient_suggestion # Append excipient suggestions self.knowledge_base.store_data('formulation_suggestion', formulation_suggestion) print("\n--- Formulation Agent: Formulation Suggestion ---") print(formulation_suggestion) else: print("Formulation Agent: Optimal pH not determined, cannot suggest formulation yet.") # --- Orchestration Agent (Orchestrator) --- class Orchestrator: def __init__(self, knowledge_base, data_agent, data_scientist_agent, research_agent, clinical_agent, formulation_agent): self.knowledge_base = knowledge_base self.data_agent = data_agent self.data_scientist_agent = data_scientist_agent self.research_agent = research_agent self.clinical_agent = clinical_agent self.formulation_agent = formulation_agent def run_drug_discovery_pipeline(self, dose_response_data, drug_properties_data, excipients_suggestion_data): # Pass excipients_suggestion_data print("--- Starting Drug Discovery Pipeline ---") # 1. Data Acquisition print("\n--- 1. Data Acquisition ---") self.data_agent.fetch_blog_data() # Example: Fetching DrugBank and Clinical Trial data (if APIs are configured - uncomment to use if you have API keys) # self.data_agent.fetch_drug_data_from_api("curcumin") # Example drug fetch # clinical_trial_query = {'expr': 'cancer+curcumin+methylene blue', 'fmt': 'json', 'min_rnk': 1, 'max_rnk': 5} # self.data_agent.fetch_clinical_trial_data_api(clinical_trial_query) # 2. Data Scientist Agent Analysis print("\n--- 2. Data Scientist Agent Analysis ---") self.data_scientist_agent.analyze_drug_efficacy_data() self.data_scientist_agent.perform_gnn_model_analysis() self.data_scientist_agent.analyze_dose_response_curves(dose_response_data) # Analyze dose-response data self.data_scientist_agent.plot_dose_response_curves(dose_response_data) # Generate and show plot # 3. Research Agent Activities print("\n--- 3. Research Agent Activities ---") self.research_agent.research_tp53_pathway() self.research_agent.research_curcumin_methylene_blue_synergy() # 4. Clinical Agent Analysis print("\n--- 4. Clinical Agent Analysis ---") # self.clinical_agent.analyze_clinical_trial_relevance() # If clinical trial data is fetched - uncomment if used self.clinical_agent.assess_dosage_and_side_effects() # 5. Formulation Agent Analysis and Suggestion print("\n--- 5. Formulation Agent Analysis and Suggestion ---") self.formulation_agent.analyze_ph_data() self.formulation_agent.analyze_drug_properties(drug_properties_data) # Analyze drug properties # Pass excipients suggestion data to Formulation Agent (even if suggestion is based on hardcoded data for now) self.formulation_agent.excipients_data["curcumin"] = excipients_suggestion_data.get("curcumin", []) # Set excipient suggestion in agent self.formulation_agent.suggest_drug_formulation() print("\n--- Drug Discovery Pipeline Completed (Skeleton) ---") # --- Main Execution --- if __name__ == "__main__": # 1. Initialize Knowledge Base kb = KnowledgeBase() # 2. Initialize Agents data_agent = DataAcquisitionAgent(kb, BLOG_URLS, API_ENDPOINTS, API_KEYS) data_scientist_agent = DataScientistAgent(kb) research_agent = ResearchAgent(kb) clinical_agent = ClinicalAgent(kb) formulation_agent = FormulationAgent(kb, curcumin_ph_data, methylene_blue_ph_data, excipients_data) # Pass excipients_data # 3. Initialize Orchestrator orchestrator = Orchestrator(kb, data_agent, data_scientist_agent, research_agent, clinical_agent, formulation_agent) # 4. Excipient Suggestion Data (Hardcoded for this example - could be from analysis or database) excipients_suggestion_data = { "curcumin": ["p188", "p407", "piperine"] # Suggest P188, P407, and Piperine for curcumin # "methylene_blue": [] # No excipients suggested for methylene blue for now } # 5. Run the Drug Discovery Pipeline, passing dose-response, drug properties, and excipient suggestion data orchestrator.run_drug_discovery_pipeline(dose_response_data, drug_properties_data, excipients_suggestion_data) # Pass excipients_suggestion_data # 6. (Optional) Access data from Knowledge Base after pipeline run print("\n--- Knowledge Base Summary (Example) ---") print("Optimal Formulation pH:", kb.retrieve_data('optimal_formulation_ph')) print("Formulation Suggestion:", kb.retrieve_data('formulation_suggestion')) print("Methylene Blue Dose Considerations:", kb.retrieve_data('methylene_blue_dose_considerations')) # Example - retrieve from KB print("Curcumin Dose Considerations:", kb.retrieve_data('curcumin_dose_considerations')) # Example - retrieve from KB # ... (Print other relevant data from KB)
