app.py

"""
Docker Container: https://hub.docker.com/r/continuumio/anaconda3
RDKit Installation: https://www.rdkit.org/docs/Install.html
"""
import mols2grid
import pandas as pd
import streamlit as st
import streamlit.components.v1 as components
from rdkit import Chem
from rdkit.Chem.Descriptors import ExactMolWt, MolLogP, NumHDonors, NumHAcceptors

st.title("Filtro para Drogas Aprobadas por FDA por la Regla de los 5 de Lipinski con Streamlit")

st.markdown("""
- App modificada por [Chanin Nantasenamat](http://medium.dataprofessor.org) (aka [Data Professor](http://youtube.com/dataprofessor))
- App original de [Justin Chavez](https://blog.reverielabs.com/building-web-applications-from-python-scripts-with-streamlit/)
- Traducción Enrique Carbó
""")

@st.cache(allow_output_mutation=True)
def download_dataset():
    """Loads once then cached for subsequent runs"""
    df = pd.read_csv(
        "https://www.cureffi.org/wp-content/uploads/2013/10/drugs.txt", sep="\t"
    ).dropna()
    return df

# Calculate descriptors
def calc_mw(smiles_string):
    """Given a smiles string (ex. C1CCCCC1), calculate and return the molecular weight"""
    mol = Chem.MolFromSmiles(smiles_string)
    return ExactMolWt(mol)

def calc_logp(smiles_string):
    """Given a smiles string (ex. C1CCCCC1), calculate and return the LogP"""
    mol = Chem.MolFromSmiles(smiles_string)
    return MolLogP(mol)

def calc_NumHDonors(smiles_string):
    """Given a smiles string (ex. C1CCCCC1), calculate and return the NumHDonors"""
    mol = Chem.MolFromSmiles(smiles_string)
    return NumHDonors(mol)

def calc_NumHAcceptors(smiles_string):
    """Given a smiles string (ex. C1CCCCC1), calculate and return the NumHAcceptors"""
    mol = Chem.MolFromSmiles(smiles_string)
    return NumHAcceptors(mol)


# Copy the dataset so any changes are not applied to the original cached version
df = download_dataset().copy()
df["PM"] = df.apply(lambda x: calc_mw(x["smiles"]), axis=1)
df["LogP"] = df.apply(lambda x: calc_logp(x["smiles"]), axis=1)
df["NumHDonors"] = df.apply(lambda x: calc_NumHDonors(x["smiles"]), axis=1)
df["NumHAcceptors"] = df.apply(lambda x: calc_NumHAcceptors(x["smiles"]), axis=1)


# Sidebar panel
st.sidebar.header('Establecer Parámetros')
st.sidebar.write('*Nota: Muestra los componentes que poseen un valor menor que el siguiente umbral*')
weight_cutoff = st.sidebar.slider(
    label="Peso Molecular",
    min_value=0,
    max_value=1000,
    value=500,
    step=10,
)
logp_cutoff = st.sidebar.slider(
    label="LogP",
    min_value=-10,
    max_value=10,
    value=5,
    step=1,
)
NumHDonors_cutoff = st.sidebar.slider(
    label="NumHDonors",
    min_value=0,
    max_value=15,
    value=5,
    step=1,
)
NumHAcceptors_cutoff = st.sidebar.slider(
    label="NumHAcceptors",
    min_value=0,
    max_value=20,
    value=10,
    step=1,
)

df_result = df[df["PM"] < weight_cutoff]
df_result2 = df_result[df_result["LogP"] < logp_cutoff]
df_result3 = df_result2[df_result2["NumHDonors"] < NumHDonors_cutoff]
df_result4 = df_result3[df_result3["NumHAcceptors"] < NumHAcceptors_cutoff]

st.write(df_result4.shape)
st.write(df_result4)


raw_html = mols2grid.display(df_result4,
                            #subset=["Name", "img"],
                            subset=["img", "Name", "PM"],
                            mapping={"smiles": "SMILES", "generic_name": "Name"})._repr_html_()
components.html(raw_html, width=900, height=1100, scrolling=False)