ScaffoldGraph is an open-source cheminformatics library, built using RDKit and NetworkX for generating scaffold networks and scaffold trees.
⌬ ScaffoldGraph ⌬
- 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)
|Computes Scaffold Networks||X||X||-||-|
|Computes HierS Networks||X||-||-||-|
|Computes Scaffold Trees||X||X||X||X|
|Command Line Interface||X||X||-||X|
|Results can be computed in parallel||X||X||-||-|
|Benchmark for 150,000 molecules
||15m 25s||27m 6s||-||-|
|Limit on input molecules||N/A
* 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
** Limited by available memory
*** Graphical interface has an upper limit of 2,000 scaffolds
- ScaffoldGraph currently only supports Python 3
Install with conda (recommended)
conda config --add channels conda-forge conda install -c uclcheminformatics scaffoldgraph
Install with pip
pip install scaffoldgraph
Warning: rdkit cannot be installed with pip, so must be installed through other means
The ScaffoldGraph CLI is almost analagous 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
--max-rings, -m : ignore molecules with # rings > N (default: 10)
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.
--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
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>
<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
ScaffoldGraphs CLI utility supports input files in the SMILES and SDF formats. Other file formats can be converted using OpenBabel.
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 ...
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
TSV Format (default)
The generate commands (network, hiers, tree) produce an intermediate tsv containing 4 columns:
- Number of rings (hierarchy)
- Scaffold SMILES
- Sub-scaffold SMILES
- Molecule ID(s) (top-level scaffolds (Murcko))
The aggregate command produces a tsv containing 4 columns
- Scaffold ID
- Number of rings (hierarchy)
- Scaffold SMILES
- Sub-scaffold IDs
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:
- TITLE field = scaffold ID
- SUBSCAFFOLDS field = list of sub-scaffold IDs
- HIERARCHY field = number of rings
- SMILES field = scaffold canonical SMILES
ScaffoldGraph makes it simple to construct a graph using the library API. The resultant graphs follow the same API as a NetworkX DiGraph.
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')
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)] @property def name(self): return 'cutstom 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)
Contributions to ScaffoldGraph will most likely fall into the following categories:
- 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.
- 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
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
ScaffoldGraph uses Travis CI for continuous integration
- 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.
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