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TeachOpenCADD | 003 化合物的过滤

化学信息学

TeachOpenCADD

化学信息学TeachOpenCADD

YangHe

发布于 2023-06-15

数据集

AI4SCUP-CNS-BBB(v1)

003 分子过滤：去除不想要的子结构

🏃🏻 快速开始
您可以直接在 Bohrium Notebook 上执行此文档。首先，请点击位于界面顶部的开始连接按钮，然后选择 bohrium-notebook:05-31 镜像并选择合适的的机器配置，稍等片刻即可开始运行。

📖 来源
本 Notebook 来自 https://github.com/volkamerlab/teachopencadd，由杨合 📨 修改搬运至 Bohrium Notebook。

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Talktorial T003: This talktorial is part of the TeachOpenCADD pipeline described in the first TeachOpenCADD publication (J. Cheminform. (2019), 11, 1-7), comprising of talktorials T001-T010.

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Aim of this talktorial

There are some substructures we prefer not to include into our screening library. In this talktorial, we learn about different types of such unwanted substructures and how to find, highlight and remove them with RDKit.

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Contents in Theory

Unwanted substructures
Pan Assay Interference Compounds (PAINS)

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Contents in Practical

Load and visualize data
Filter for PAINS
Filter for unwanted substructures
Highlight substructures
Substructure statistics

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References

Pan Assay Interference compounds (wikipedia, J. Med. Chem. (2010), 53, 2719-2740)
Unwanted substructures according to Brenk et al. (Chem. Med. Chem. (2008), 3, 435-44)
Inspired by a Teach-Discover-Treat tutorial (repository)
RDKit (repository, documentation)

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Theory

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Unwanted substructures

Substructures can be unfavorable, e.g., because they are toxic or reactive, due to unfavorable pharmacokinetic properties, or because they likely interfere with certain assays. Nowadays, drug discovery campaigns often involve high throughput screening. Filtering unwanted substructures can support assembling more efficient screening libraries, which can save time and resources.

Brenk et al. (Chem. Med. Chem. (2008), 3, 435-44) have assembled a list of unfavorable substructures to filter their libraries used to screen for compounds to treat neglected diseases. Examples of such unwanted features are nitro groups (mutagenic), sulfates and phosphates (likely resulting in unfavorable pharmacokinetic properties), 2-halopyridines and thiols (reactive). This list of undesired substructures was published in the above mentioned paper and will be used in the practical part of this talktorial.

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Pan Assay Interference Compounds (PAINS)

PAINS are compounds that often occur as hits in HTS even though they actually are false positives. PAINS show activity at numerous targets rather than one specific target. Such behavior results from unspecific binding or interaction with assay components. Baell et al. (J. Med. Chem. (2010), 53, 2719-2740) focused on substructures interfering in assay signaling. They described substructures which can help to identify such PAINS and provided a list which can be used for substructure filtering.

PAINS

Figure 1: Specific and unspecific binding in the context of PAINS. Figure taken from Wikipedia.

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Practical

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Load and visualize data

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First, we import the required libraries, load our filtered dataset from Talktorial T002 and draw the first molecules.

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[1]

from pathlib import Path

import pandas as pd

from tqdm.auto import tqdm

from rdkit import Chem

from rdkit.Chem import PandasTools

from rdkit.Chem.FilterCatalog import FilterCatalog, FilterCatalogParams

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[ ]

#download data

!git clone https://github.com/UR-Free/data.git

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[2]

# define paths

HERE = Path(_dh[-1])

DATA = HERE / "data"

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[3]

# load data from Talktorial T2

egfr_data = pd.read_csv(

HERE / "../T002_compound_adme/data/EGFR_compounds_lipinski.csv",

index_col=0,

)

# Drop unnecessary information

print("Dataframe shape:", egfr_data.shape)

egfr_data.drop(columns=["molecular_weight", "n_hbd", "n_hba", "logp"], inplace=True)

egfr_data.head()

Dataframe shape: (4635, 10)

	molecule_chembl_id	IC50	units	smiles	pIC50	ro5_fulfilled
0	CHEMBL63786	0.003	nM	Brc1cccc(Nc2ncnc3cc4ccccc4cc23)c1	11.522879	True
1	CHEMBL35820	0.006	nM	CCOc1cc2ncnc(Nc3cccc(Br)c3)c2cc1OCC	11.221849	True
2	CHEMBL53711	0.006	nM	CN(C)c1cc2c(Nc3cccc(Br)c3)ncnc2cn1	11.221849	True
3	CHEMBL66031	0.008	nM	Brc1cccc(Nc2ncnc3cc4[nH]cnc4cc23)c1	11.096910	True
4	CHEMBL53753	0.008	nM	CNc1cc2c(Nc3cccc(Br)c3)ncnc2cn1	11.096910	True

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[4]

# Add molecule column

PandasTools.AddMoleculeColumnToFrame(egfr_data, smilesCol="smiles")

# Draw first 3 molecules

Chem.Draw.MolsToGridImage(

list(egfr_data.head(3).ROMol),

legends=list(egfr_data.head(3).molecule_chembl_id),

)

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Filter for PAINS

The PAINS filter is already implemented in RDKit (documentation). Such pre-defined filters can be applied via the FilterCatalog class. Let's learn how it can be used.

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[5]

# initialize filter

params = FilterCatalogParams()

params.AddCatalog(FilterCatalogParams.FilterCatalogs.PAINS)

catalog = FilterCatalog(params)

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[6]

# search for PAINS

matches = []

clean = []

for index, row in tqdm(egfr_data.iterrows(), total=egfr_data.shape[0]):

molecule = Chem.MolFromSmiles(row.smiles)

entry = catalog.GetFirstMatch(molecule) # Get the first matching PAINS

if entry is not None:

# store PAINS information

matches.append(

{

"chembl_id": row.molecule_chembl_id,

"rdkit_molecule": molecule,

"pains": entry.GetDescription().capitalize(),

}

)

else:

# collect indices of molecules without PAINS

clean.append(index)

matches = pd.DataFrame(matches)

egfr_data = egfr_data.loc[clean] # keep molecules without PAINS

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[7]

# NBVAL_CHECK_OUTPUT

print(f"Number of compounds with PAINS: {len(matches)}")

print(f"Number of compounds without PAINS: {len(egfr_data)}")

Number of compounds with PAINS: 408
Number of compounds without PAINS: 4227

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Let's have a look at the first 3 identified PAINS.

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[8]

Chem.Draw.MolsToGridImage(

list(matches.head(3).rdkit_molecule),

legends=list(matches.head(3)["pains"]),

)

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Filter and highlight unwanted substructures

Some lists of unwanted substructures, like PAINS, are already implemented in RDKit. However, it is also possible to use an external list and get the substructure matches manually. Here, we use the list provided in the supporting information from Brenk et al. (Chem. Med. Chem. (2008), 3, 535-44).

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[9]

substructures = pd.read_csv(DATA / "unwanted_substructures.csv", sep="\s+")

substructures["rdkit_molecule"] = substructures.smarts.apply(Chem.MolFromSmarts)

print("Number of unwanted substructures in collection:", len(substructures))

# NBVAL_CHECK_OUTPUT

Number of unwanted substructures in collection: 104

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Let's have a look at a few substructures.

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[10]

Chem.Draw.MolsToGridImage(

mols=substructures.rdkit_molecule.tolist()[2:5],

legends=substructures.name.tolist()[2:5],

)

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Search our filtered dataframe for matches with these unwanted substructures.

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[11]

# search for unwanted substructure

matches = []

clean = []

for index, row in tqdm(egfr_data.iterrows(), total=egfr_data.shape[0]):

molecule = Chem.MolFromSmiles(row.smiles)

match = False

for _, substructure in substructures.iterrows():

if molecule.HasSubstructMatch(substructure.rdkit_molecule):

matches.append(

{

"chembl_id": row.molecule_chembl_id,

"rdkit_molecule": molecule,

"substructure": substructure.rdkit_molecule,

"substructure_name": substructure["name"],

}

)

match = True

if not match:

clean.append(index)

matches = pd.DataFrame(matches)

egfr_data = egfr_data.loc[clean]

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[12]

# NBVAL_CHECK_OUTPUT

print(f"Number of found unwanted substructure: {len(matches)}")

print(f"Number of compounds without unwanted substructure: {len(egfr_data)}")

Number of found unwanted substructure: 3232
Number of compounds without unwanted substructure: 2089

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Highlight substructures

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Let's have a look at the first 3 identified unwanted substructures. Since we have access to the underlying SMARTS patterns we can highlight the substructures within the RDKit molecules.

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[13]

to_highlight = [

row.rdkit_molecule.GetSubstructMatch(row.substructure) for _, row in matches.head(3).iterrows()

]

Chem.Draw.MolsToGridImage(

list(matches.head(3).rdkit_molecule),

highlightAtomLists=to_highlight,

legends=list(matches.head(3).substructure_name),

)

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Substructure statistics

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Finally, we want to find the most frequent substructure found in our data set. The Pandas DataFrame provides convenient methods to group containing data and to retrieve group sizes.

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[14]

# NBVAL_CHECK_OUTPUT

groups = matches.groupby("substructure_name")

group_frequencies = groups.size()

group_frequencies.sort_values(ascending=False, inplace=True)

group_frequencies.head(10)

substructure_name
Michael-acceptor               1113
Aliphatic-long-chain            489
Oxygen-nitrogen-single-bond     367
triple-bond                     252
nitro-group                     177
imine                           150
Thiocarbonyl-group              114
aniline                          64
halogenated-ring                 62
conjugated-nitrile-group         59
dtype: int64

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Discussion

In this talktorial we learned two possibilities to perform a search for unwanted substructures with RDKit:

The FilterCatalog class can be used to search for predefined collections of substructures, e.g., PAINS.
The HasSubstructMatch() function to perform manual substructure searches.

Actually, PAINS filtering could also be implemented via manual substructure searches with HasSubstructMatch(). Furthermore, the substructures defined by Brenk et al. (Chem. Med. Chem. (2008), 3, 535-44) are already implemented as a FilterCatalog. Additional pre-defined collections can be found in the RDKit documentation.

So far, we have been using the HasSubstructMatch() function, which only yields one match per compound. With the GetSubstructMatches() function (documentation) we have the opportunity to identify all occurrences of a particular substructure in a compound. In case of PAINS, we have only looked at the first match per molecule (GetFirstMatch()). If we simply want to filter out all PAINS this is enough. However, we could also use GetMatches() in order to see all critical substructures of a molecule.

Detected substructures can be handled in two different fashions:

Either, the substructure search is applied as a filter and the compounds are excluded from further testing to save time and money.
Or, they can be used as warnings, since ~5 % of FDA-approved drugs were found to contain PAINS (ACS. Chem. Biol. (2018), 13, 36-44). In this case experts can judge manually, if an identified substructure is critical or not.

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化学信息学

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