ScaffoldGraphDG 1.1.8
ScaffoldGraph is an open-source cheminformatics library, built using RDKit and NetworkX for generating scaffold networks and scaffold trees.
License: MIT
Requires Python: >=3.6
Keywords: rdkit,networkx,cheminformatics,scaffolds,scaffold tree,scaffold network

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# &#9004; ScaffoldGraph  &#9004;

**ScaffoldGraph** is an open-source cheminformatics library, built using [RDKit](https://www.rdkit.org/) and
[NetworkX](https://networkx.github.io/), for the generation and analysis of scaffold networks and scaffold trees.

<p align="center">
    <img width="80%", src="https://raw.githubusercontent.com/UCLCheminformatics/ScaffoldGraph/main/img/scaffoldgraph.jpg" />
</p>

[Features](https://github.com/UCLCheminformatics/ScaffoldGraph#features) | 
[Installation](https://github.com/UCLCheminformatics/ScaffoldGraph#installation) |
[Quick-start](https://github.com/UCLCheminformatics/ScaffoldGraph#quick-start) |
[Examples](https://github.com/UCLCheminformatics/ScaffoldGraph/tree/master/examples) |
[Contributing](https://github.com/UCLCheminformatics/ScaffoldGraph#contributing) |
[References](https://github.com/UCLCheminformatics/ScaffoldGraph#references) |
[Citation](https://github.com/UCLCheminformatics/ScaffoldGraph#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]('https://www.rdkit.org/docs/Install.html')

__Update (17/06/21)__: rdkit can now be installed through the [rdkit-pypi](https://pypi.org/project/rdkit-pypi/) wheels for
Linux and MacOS, and can be installed alongside ScaffoldGraph optionally (see above instructions).  

__Update (16/11/21)__: Jupyter lab users may also need to follow the extra installation instructions 
[here](https://github.com/cytoscape/ipycytoscape#for-jupyterlab-1x-or-2x) / [here](https://ipycytoscape.readthedocs.io/en/latest/installing.html) 
when using the ipycytoscape visualisation utility.


--------------------------------------------------------------------------------

## 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:

```console
$ 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:
    
    ```console
    $ ls
    file_1.sdf  file_2.sdf
    ```
    
    They would first use the commands:
    
    ```console
    $ 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.
    
    ```console
    $ 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).
    
    ```console
    $ 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](http://openbabel.org/wiki/Main_Page).

    - ##### 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:
        
    ```csv
    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](https://en.wikipedia.org/wiki/Chemical_table_file) 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](https://github.com/UCLCheminformatics/ScaffoldGraph/tree/master/examples) 
notebooks can be found in the 'examples' directory.

```python
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.
    
  ```python
  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:
    
  ```python
  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:
    
   ```python
  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](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaa219/5814205),
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},
}
```