ScaffoldGraph

ScaffoldGraph is an open-source cheminformatics library, built using RDKit and NetworkX for generating scaffold networks and scaffold trees.


Keywords
rdkit, networkx, cheminformatics, scaffolds, scaffold, tree, network, fragments, chemistry, computational-chemistry, molecules, murcko, scaffold-networks, scaffold-trees, hiers
License
MIT
Install
pip install ScaffoldGraph==1.1.2

Documentation

Conda Anaconda Release Build Status Contributing License: MIT DOI

⌬ ScaffoldGraph ⌬

ScaffoldGraph is an open-source cheminformatics library, built using RDKit and NetworkX, for the generation and analysis of scaffold networks and scaffold trees.

Features | Installation | Quick-start | Examples | Contributing | References | Citation

Features

  • Scaffold Network generation (Varin, 2011)
    • Explore scaffold-space through the iterative removal of available rings, generating all possible sub-scaffolds for a set of input molecules. The output is a directed acyclic graph of molecular scaffolds
  • HierS Network Generation (Wilkens, 2005)
    • Explore scaffold-space through the iterative removal of available rings, generating all possible sub-scaffolds without dissecting fused ring-systems
  • Scaffold Tree generation (Schuffenhauer, 2007)
    • Explore scaffold-space through the iterative removal of the least-characteristic ring from a molecular scaffold. The output is a tree of molecular scaffolds
  • Murcko Fragment generation (Bemis, 1996)
    • Generate a set of murcko fragments for a molecule through the iterative removal of available rings.
  • Compound Set Enrichment (Varin, 2010, 2011)
    • Identify active chemical series from primary screening data

Comparison to existing software

  • Scaffold Network Generator (SNG) (Matlock 2013)
  • Scaffold Hunter (SH) (Wetzel, 2009)
  • Scaffold Tree Generator (STG) (SH CLI predecessor)
SG SNG SH STG
Computes Scaffold Networks X X - -
Computes HierS Networks X - - -
Computes Scaffold Trees X X X X
Command Line Interface X X - X
Graphical Interface - * - X -
Accessible Library X - - -
Results can be computed in parallel X X - -
Benchmark for 150,000 molecules ** 15m 25s 27m 6s - -
Limit on input molecules N/A *** 10,000,000 200,000 **** 10,000,000

* While ScaffoldGraph has no explicit GUI, it contains functions for interactive scaffoldgraph visualization.

** Tests performed on an Intel Core i7-6700 @ 3.4 GHz with 32GB of RAM, without parallel processing. I could not find the code for STG and do not intend to search for it, SNG report that both itself and SH are both faster in the benchmark test.

*** Limited by available memory

**** Graphical interface has an upper limit of 2,000 scaffolds


Installation

  • ScaffoldGraph currently supports Python 3.6 and above.

Install with conda (recommended)

conda config --add channels conda-forge
conda install -c uclcheminformatics scaffoldgraph

Install with pip

# Basic installation.
pip install scaffoldgraph

# Install with ipycytoscape.
pip install scaffoldgraph[vis]

# Install with rdkit-pypi (Linux, MacOS).
pip install scaffoldgraph[rdkit]

# Install with all optional packages. 
pip install scaffoldgraph[rdkit, vis]

Warning: rdkit cannot be installed with pip, so must be installed through other means

Update (17/06/21): rdkit can now be installed through the rdkit-pypi wheels for Linux and MacOS, and can be installed alongside ScaffoldGraph optionally (see above instructions).


Quick Start

CLI usage

The ScaffoldGraph CLI is almost analogous to SNG consisting of a two step process (Generate --> Aggregate).

ScaffoldGraph can be invoked from the command-line using the following command:

$ scaffoldgraph <command> <input-file> <options>

Where "command" is one of: tree, network, hiers, aggregate or select.

  • Generating Scaffold Networks/Trees

    The first step of the process is to generate an intermediate scaffold graph. The generation commands are: network, hiers and tree

    For example, if a user would like to generate a network from two files:

    $ ls
    file_1.sdf  file_2.sdf

    They would first use the commands:

    $ scaffoldgraph network file_1.sdf file_1.tmp
    $ scaffoldgraph network file_2.sdf file_2.tmp

    Further options:

    --max-rings, -m : ignore molecules with # rings > N (default: 10)
    --flatten-isotopes -i : remove specific isotopes
    --keep-largest-fragment -f : only process the largest disconnected fragment
    --discharge-and-deradicalize -d : remove charges and radicals from scaffolds 
    
  • Aggregating Scaffold Graphs

    The second step of the process is aggregating the temporary files into a combined graph representation.

    $ scaffoldgraph aggregate file_1.tmp file_2.tmp file.tsv

    The final network is now available in 'file.tsv'. Output formats are explained below.

    Further options:

    --map-mols, -m  <file>   : generate a file mapping molecule IDs to scaffold IDs 
    --map-annotations <file> : generate a file mapping scaffold IDs to annotations
    --sdf                    : write the output as an SDF file
    
  • Selecting Subsets

    ScaffoldGraph allows a user to select a subset of a scaffold network or tree using a molecule-based query, i.e. selecting only scaffolds for molecules of interest.

    This command can only be performed on an aggregated graph (Not SDF).

    $ scaffoldgraph select <graph input-file> <input molecules> <output-file> <options>

    Options:

    <graph input-file>   : A TSV graph constructed using the aggregate command
    <input molecules>    : Input query file (SDF, SMILES)
    <output-file>        : Write results to specified file
    --sdf                : Write the output as an SDF file
    
  • Input Formats

    ScaffoldGraphs CLI utility supports input files in the SMILES and SDF formats. Other file formats can be converted using OpenBabel.

    • Smiles Format:

    ScaffoldGraph expects a delimited file where the first column defines a SMILES string, followed by a molecule identifier. If an identifier is not specified the program will use a hash of the molecule as an identifier.

    Example SMILES file:

    CCN1CCc2c(C1)sc(NC(=O)Nc3ccc(Cl)cc3)c2C#N   CHEMBL4116520
    CC(N1CC(C1)Oc2ccc(Cl)cc2)C3=Nc4c(cnn4C5CCOCC5)C(=O)N3   CHEMBL3990718
    CN(C\C=C\c1ccc(cc1)C(F)(F)F)Cc2coc3ccccc23  CHEMBL4116665
    N=C1N(C(=Nc2ccccc12)c3ccccc3)c4ccc5OCOc5c4  CHEMBL4116261
    ...
    
    • SDF Format:

    ScaffoldGraph expects an SDF file, where the molecule identifier is specified in the title line. If the title line is blank, then a hash of the molecule will be used as an identifier.

    Note: selecting subsets of a graph will not be possible if a name is not supplied

  • Output Formats

    • TSV Format (default)

    The generate commands (network, hiers, tree) produce an intermediate tsv containing 4 columns:

    1. Number of rings (hierarchy)
    2. Scaffold SMILES
    3. Sub-scaffold SMILES
    4. Molecule ID(s) (top-level scaffolds (Murcko))

    The aggregate command produces a tsv containing 4 columns

    1. Scaffold ID
    2. Number of rings (hierarchy)
    3. Scaffold SMILES
    4. Sub-scaffold IDs
    • SDF Format

    An SDF file can be produced by the aggregate and select commands. This SDF is formatted according to the SDF specification with added property fields:

    1. TITLE field = scaffold ID
    2. SUBSCAFFOLDS field = list of sub-scaffold IDs
    3. HIERARCHY field = number of rings
    4. SMILES field = scaffold canonical SMILES

Library usage

ScaffoldGraph makes it simple to construct a graph using the library API. The resultant graphs follow the same API as a NetworkX DiGraph.

Some example notebooks can be found in the 'examples' directory.

import scaffoldgraph as sg

# construct a scaffold network from an SDF file
network = sg.ScaffoldNetwork.from_sdf('my_sdf_file.sdf')

# construct a scaffold tree from a SMILES file
tree = sg.ScaffoldTree.from_smiles('my_smiles_file.smi')

# construct a scaffold tree from a pandas dataframe
import pandas as pd
df = pd.read_csv('activity_data.csv')
network = sg.ScaffoldTree.from_dataframe(
    df, smiles_column='Smiles', name_column='MolID',
    data_columns=['pIC50', 'MolWt'], progress=True,
)

Advanced Usage

  • Multi-processing

    It is simple to construct a graph from multiple input source in parallel, using the concurrent.futures module and the sg.utils.aggregate function.

    from concurrent.futures import ProcessPoolExecutor
    from functools import partial
    import scaffoldgraph as sg
    import os
        
    directory = './data'
    sdf_files = [f for f in os.listdir(directory) if f.endswith('.sdf')]
        
    func = partial(sg.ScaffoldNetwork.from_sdf, ring_cutoff=10)
          
    graphs = []
    with ProcessPoolExecutor(max_workers=4) as executor:
        futures = executor.map(func, sdf_files)
        for future in futures:
            graphs.append(future)
          
    network = sg.utils.aggregate(graphs)
  • Creating custom scaffold prioritisation rules

    If required a user can define their own rules for prioritizing scaffolds during scaffold tree construction. Rules can be defined by subclassing one of four rule classes:

    BaseScaffoldFilterRule, ScaffoldFilterRule, ScaffoldMinFilterRule or ScaffoldMaxFilterRule

    When subclassing a name property must be defined and either a condition, get_property or filter function. Examples are shown below:

    import scaffoldgraph as sg
    from scaffoldgraph.prioritization import *
      
    """
    Scaffold filter rule (must implement name and condition)
    The filter will retain all scaffolds which return a True condition
    """
    
    class CustomRule01(ScaffoldFilterRule):
        """Do not remove rings with >= 12 atoms if there are smaller rings to remove"""
    
        def condition(self, child, parent):
            removed_ring = child.rings[parent.removed_ring_idx]
            return removed_ring.size < 12
              
        @property
        def name(self):
            return 'custom rule 01'
            
    """
    Scaffold min/max filter rule (must implement name and get_property)
    The filter will retain all scaffolds with the min/max property value
    """
      
    class CustomRule02(ScaffoldMinFilterRule):
        """Smaller rings are removed first"""
      
        def get_property(self, child, parent):
            return child.rings[parent.removed_ring_idx].size
              
        @property
        def name(self):
            return 'custom rule 02'
          
        
    """
    Scaffold base filter rule (must implement name and filter)
    The filter method must return a list of filtered parent scaffolds
    This rule is used when a more complex rule is required, this example
    defines a tiebreaker rule. Only one scaffold must be left at the end
    of all filter rules in a rule set
    """
      
    class CustomRule03(BaseScaffoldFilterRule):
        """Tie-breaker rule (alphabetical)"""
      
        def filter(self, child, parents):
            return [sorted(parents, key=lambda p: p.smiles)[0]]
      
        @property
        def name(self):
            return 'custom rule 03'  

    Custom rules can subsequently be added to a rule set and supplied to the scaffold tree constructor:

    ruleset = ScaffoldRuleSet(name='custom rules')
    ruleset.add_rule(CustomRule01())
    ruleset.add_rule(CustomRule02())
    ruleset.add_rule(CustomRule03())
     
    graph = sg.ScaffoldTree.from_sdf('my_sdf_file.sdf', prioritization_rules=ruleset)

Contributing

Contributions to ScaffoldGraph will most likely fall into the following categories:

  1. Implementing a new Feature:
    • New Features that fit into the scope of this package will be accepted. If you are unsure about the idea/design/implementation, feel free to post an issue.
  2. Fixing a Bug:
    • Bug fixes are welcomed, please send a Pull Request each time a bug is encountered. When sending a Pull Request please provide a clear description of the encountered bug. If unsure feel free to post an issue

Please send Pull Requests to: http://github.com/UCLCheminformatics/ScaffoldGraph

Testing

ScaffoldGraphs testing is located under test/. Run all tests using:

$ python setup.py test

or run an individual test: pytest --no-cov tests/core

When contributing new features please include appropriate test files

Continuous Integration

ScaffoldGraph uses Travis CI for continuous integration


References

  • Bemis, G. W. and Murcko, M. A. (1996). The properties of known drugs. 1. molecular frameworks. Journal of Medicinal Chemistry, 39(15), 2887–2893.
  • Matlock, M., Zaretzki, J., Swamidass, J. S. (2013). Scaffold network generator: a tool for mining molecular structures. Bioinformatics, 29(20), 2655-2656
  • Schuffenhauer, A., Ertl, P., Roggo, S., Wetzel, S., Koch, M. A., and Waldmann, H. (2007). The scaffold tree visualization of the scaffold universe by hierarchical scaffold classification. Journal of Chemical Information and Modeling, 47(1), 47–58. PMID: 17238248.
  • Varin, T., Schuffenhauer, A., Ertl, P., and Renner, S. (2011). Mining for bioactive scaffolds with scaffold networks: Improved compound set enrichment from primary screening data. Journal of Chemical Information and Modeling, 51(7), 1528–1538.
  • Varin, T., Gubler, H., Parker, C., Zhang, J., Raman, P., Ertl, P. and Schuffenhauer, A. (2010) Compound Set Enrichment: A Novel Approach to Analysis of Primary HTS Data. Journal of Chemical Information and Modeling, 50(12), 2067-2078.
  • Wetzel, S., Klein, K., Renner, S., Rennerauh, D., Oprea, T. I., Mutzel, P., and Waldmann, H. (2009). Interactive exploration of chemical space with scaffold hunter. Nat Chem Biol, 1875(8), 581–583.
  • Wilkens, J., Janes, J. and Su, A. (2005). HierS:  Hierarchical Scaffold Clustering Using Topological Chemical Graphs. Journal of Medicinal Chemistry, 48(9), 3182-3193.

Citation

If you use this software in your own work please cite our paper, and the respective papers of the methods used.

@article{10.1093/bioinformatics/btaa219,
    author = {Scott, Oliver B and Chan, A W Edith},
    title = "{ScaffoldGraph: an open-source library for the generation and analysis of molecular scaffold networks and scaffold trees}",
    journal = {Bioinformatics},
    year = {2020},
    month = {03},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa219},
    url = {https://doi.org/10.1093/bioinformatics/btaa219},
    note = {btaa219}
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa219/32984904/btaa219.pdf},
}