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leelasd / approved_drugs.csv
Created Nov 16, 2021
CSV generated by the Python script
View approved_drugs.csv
We can't make this file beautiful and searchable because it's too large.
compound_chembl_id,canonical_smiles,molregno,structure_type,first_in_class,indication_class,first_approval
CHEMBL1000,O=C(O)COCCN1CCN(C(c2ccccc2)c2ccc(Cl)cc2)CC1,111185,MOL,0,Antihistaminic,1995.0
CHEMBL100116,CC(C)=CCN1CCC2(C)c3cc(O)ccc3CC1C2C,165474,MOL,0,,1967.0
CHEMBL1002,CC(C)(C)NC[C@H](O)c1ccc(O)c(CO)c1,111482,MOL,0,Bronchodilator; Asthma Prophylactic,1999.0
CHEMBL100259,O=c1ccn([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c(=O)[nH]1,161181,MOL,0,,
CHEMBL1003,O=C([O-])[C@H]1/C(=C/CO)O[C@@H]2CC(=O)N21.[K+],111491,MOL,0,Inhibitor (beta-lactamase),1984.0
CHEMBL1004,CN(C)CCOC(C)(c1ccccc1)c1ccccn1,111498,MOL,0,Antihistaminic,1948.0
CHEMBL1005,CCC(=O)N(c1ccccc1)C1(C(=O)OC)CCN(CCC(=O)OC)CC1,111871,MOL,0,Analgesic,1996.0
CHEMBL1006,NCCCNCCSP(=O)(O)O,112480,MOL,0,Protectant (topical); Radioprotector,1995.0
CHEMBL1007,CC(C)C[C@H](NC(=O)CNC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CO)NC(=O)[C@H](Cc1c[nH]c2ccccc12)NC(=O)[C@H](Cc1cnc[nH]1)NC(=O)[C@@H]1CCC(=O)N1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N1CCC[C@H]1C(=O)NCC(N)=O,112560,BOTH,0,Gonad
@leelasd
leelasd / extract_approved_drugs.py
Created Nov 16, 2021
Extract approved Drugs from Chembl 29 local database
View extract_approved_drugs.py
import psycopg2
import pandas as pd
import pandas.io.sql as sqlio
con = psycopg2.connect(database="chembl_29", user="leelasd", password="", host="127.0.0.1", port="5432")
print("Database opened successfully")
sql="""SELECT DISTINCT m.chembl_id AS compound_chembl_id,s.canonical_smiles,
m.molregno,
@leelasd
leelasd / PermeabilityDataFromChembl.py
Created Nov 16, 2021
Extract Permeability Data from Chembl SQL database
View PermeabilityDataFromChembl.py
import psycopg2
import pandas as pd
import pandas.io.sql as sqlio
con = psycopg2.connect(database="chembl_29", user="leelasd", password="", host="127.0.0.1", port="5432")
print("Database opened successfully")
sql="""SELECT m.chembl_id AS compound_chembl_id,
s.canonical_smiles,
r.compound_key,
@leelasd
leelasd / retrieve_bioactivity_info_from_chembl.py
Created Nov 15, 2021 — forked from avrilcoghlan/retrieve_bioactivity_info_from_chembl.py
Python script to query the ChEMBL database to retrieve a list of compounds with bioactivities for certain target proteins, and then retrieve information on the molecular properties of those compounds
View retrieve_bioactivity_info_from_chembl.py
import pandas as pd # uses pandas python module to view and analyse data
import requests # this is used to access json files
#====================================================================#
# using a list of known targets, find compounds that are active on these targets:
def find_bioactivities_for_targets(targets):
targets = ",".join(targets) # join the targets into a suitable string to fulfil the search conditions of the ChEMBL api
View combination.ipynb
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View MD_using_ANI1cxx.py
from openmmtools.constants import kB
from simtk import unit
import numpy as np
from tqdm import tqdm
from IPython.core.display import display, HTML
from IPython.display import SVG
from rdkit.Chem.Draw import IPythonConsole
import mdtraj as md
#from utils import *
import nglview
View schnet_pred_edge.py
import torch
import torch.nn as nn
from torch.nn import Softplus
from torch.utils.data import Dataset, DataLoader
import dgl
import dgl.function as fn
from dgl.batched_graph import sum_nodes
from dgl.data.chem.utils import mol_to_complete_graph
import numpy as np
from collections import defaultdict
View Clustering Methods.ipynb
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View MCS Pharmacophore.ipynb
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View Show_Ph4_Features_in_PyMOL.ipynb
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