Greetings, we are the octagon group. We are a darknet entity that has provided chat-gpt leveraged HIV cure research solutions and donated them to the international AIDS foundation. Here is both portions of the code thus far. We are making this open source. It leverages AI, machine learning and open source code to test for potential HIV cures.

v1
import networkx as nx
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
import pandas as pd
import requests
import io
def get_network_data(network_type):
"""
This function queries public databases to obtain network data for the specified network type (e.g. protein-protein
interaction network or gene co-expression network).
"""
if network_type == "protein-protein interaction":

Query the STRING database to obtain protein-protein interaction data

url = "https://string-db.org/cgi/download?sessionId=%24input-%3ES%7B%27search%27%7D%24-%3EK7V42bdvGdOw&species_text=Human"
s = requests.get(url).content
network_data = pd.read_csv(io.StringIO(s.decode('utf-8')))

elif network_type == "gene co-expression":

Query the Gene Expression Omnibus database to obtain gene co-expression data

url = "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE163248"
s = requests.get(url).content
network_data = pd.read_csv(io.StringIO(s.decode('utf-8')))

else:
raise ValueError("Invalid network type")

return network_data
def process_network_data(network_data):
"""
This function processes the network data and returns a networkx graph object.
"""
graph = nx.Graph()

for index, row in network_data.iterrows():
node1 = row['Node1']
node2 = row['Node2']
weight = row['combined_score']
graph.add_edge(node1, node2, weight=weight)

return graph
def identify_key_nodes(graph):
"""
This function identifies the key nodes in the network using centrality measures such as degree centrality and
betweenness centrality.
"""
degree_centrality = nx.degree_centrality(graph)
sorted_degree = sorted(degree_centrality.items(), key=lambda x: x[1], reverse=True)
top_degree = [node[0] for node in sorted_degree[:10]]

betweenness_centrality = nx.betweenness_centrality(graph)
sorted_betweenness = sorted(betweenness_centrality.items(), key=lambda x: x[1], reverse=True)
top_betweenness = [node[0] for node in sorted_betweenness[:10]]

key_nodes = list(set(top_degree + top_betweenness))

return key_nodes
def predict_drug_efficacy(X_train, y_train, X_test, model_type):
"""
This function uses a machine learning algorithm to predict the efficacy of potential drug candidates in targeting the
key nodes identified in the network analysis.
"""
if model_type == "random_forest":
model = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=42)
elif model_type == "neural_network":
model = MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=1000, random_state=42)
else:
raise ValueError("Invalid model type")

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

return y_pred
def get_clinical_data():
"""
This function queries public databases to obtain clinical data related to HIV and treatment outcomes.
"""
url = "https://clinicaltrials.gov/api/query/full_studies?expr=HIV&min_rnk=1&max_rnk=100"

v2

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
import csv
import json
import flask
import plotly.graph_objs as go
from plotly.subplots import make_subplots
import sqlite3
import rdkit
from rdkit import Chem
from rdkit.Chem import Descriptors, AllChem
from deap import base, creator, tools
import networkx as nx
from sklearn.ensemble import RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score
import requests
import io
# Define the Flask app and the database connection inside a function
def create_app():
app = flask.Flask(__name__)
conn = sqlite3.connect('simulation_results.db')
return app, conn
# Define the virus growth model with a docstring and comments
def simulate_virus_growth(virus_population, drug_concentration, crispr_concentration, time, k1, k2, k3, k4, k5):
"""
Simulate virus growth and CRISPR gene editing over time.
Args:
virus_population (float): Initial virus population.
drug_concentration (float): Initial drug concentration.
crispr_concentration (float): Initial CRISPR concentration.
time (ndarray): Time vector for simulation.
k1 (float): Virus growth rate.
k2 (float): Virus decay rate.
k3 (float): Drug effectiveness.
k4 (float): Drug decay rate.
k5 (float): CRISPR gene editing rate.
Returns:
ndarray: Array of virus population values over time.
"""

Set initial conditions

virus_pop = np.zeros(time.shape)
virus_pop[0] = virus_population
drug_conc = np.zeros(time.shape)
drug_conc[0] = drug_concentration
crispr_conc = np.zeros(time.shape)
crispr_conc[0] = crispr_concentration

Simulate virus growth and CRISPR gene editing over time

for i in range(1, len(time)):

Calculate virus growth rate

virus_growth_rate = k1 * virus_pop[i-1] - k2 * virus_pop[i-1]

Calculate drug effectiveness

drug_effectiveness = k3 * drug_conc[i-1] * virus_pop[i-1] - k4 * drug_conc[i-1]

Calculate CRISPR gene editing rate

crispr_editing_rate = -k5 * crispr_conc[i-1] * virus_pop[i-1]

Update virus population

virus_pop[i] = virus_pop[i-1] + virus_growth_rate + crispr_editing_rate

return virus_pop
# Define a function to read in the HIV-1 protease dataset
def load_hiv_protease_data(filename):
"""
Load the HIV-1 protease dataset from a CSV file.
Args:
filename (str): Name of the CSV file containing the dataset.
Returns:
pandas.DataFrame: Dataframe containing the HIV-1 protease dataset.
"""
data = pd.read_csv(filename)
return data
# Define a function to calculate molecular descriptors for compounds in the dataset
def calculate_molecular_descriptors(dataset):
"""
Calculate molecular descriptors for compounds in the HIV-1 protease dataset.
Args:
dataset (pandas.DataFrame): Dataframe containing the HIV-1 protease dataset.
Returns:
pandas.DataFrame: Dataframe containing the calculated molecular