ChemSchematicResolver is a toolkit for the automatic resolution of chemical schematic diagrams and their labels. You can find out how it works on the website , and try out the online demo here
- Extraction of generic R-Group structures
- Automatic detection and download of schematic chemical diagrams from scientific articles
- HTML and XML document format support from RSC and Elsevier
- High-throughput capabilities
- Direct extraction from image files
- PNG, GIF, JPEG, TIFF image format support
Installation of ChemSchematicResolver is achieved using conda.
First, install Miniconda, which contains a complete Python distribution alongside the conda package manager.
Next, go to the command line terminal and create a working environment by typing
conda create --name <my_env> python=3.6
Once this is created, enter this environment with the command
conda activate <my_env>
There are two ways to continue the installation - via the anaconda cloud, and from source.
Please note that the following option will not work until release.
We recommend the installation of ChemSchematicResolver through the anaconda cloud.
Simply type:
conda install -c edbeard -c mcs07 -c conda-forge chemschematicresolver
This command installs ChemSchematicResolver and all it's dependencies from the author's channel. This includes pyosra, the Python wrapper for the OSRA toolkit, and ChemDataExtractor-CSR, the bespoke version of ChemDataExtractor containing diagram parsers.
This method of installation is currently supported on linux machines only
Please note that all following links will not work until release.
We strongly recommend installation via the conda cloud whenever possible, as all the dependencies are automatically handled.
If this cannot be done, users are invited to compile the code from source. This is easiest to do through conda build, by building and installing using the recipes here.
The following packages will need to be built from a recipe, in the order below:
-
Pyosra: [recipe, source code]
-
ChemDataExtracor-CSR: [recipe, source code]
-
ChemSchematicResolver: [recipe, source code]
For each, enter the directory and run:
conda build .
to create a compressed tarball file, which contains the instructions for installing the code (Please note that this can take up to 30 minutes to build).
Move all compressed tarballs to a single directory, enter the directory and run:
conda index .
This changes the directory to a format emulating a conda channel. To install all code and dependencies, then simply run
conda install -c <path/to/tarballs> chemschematicresolver
And you should have everything installed!
This section gives a introduction on how to get started with ChemSchematicResolver. This assumes you already have ChemSchematicResolver and all dependencies installed.
It's simplest to run ChemSchematicResolver on an image file.
Open a python terminal and import the library with:
>>> import chemschematicresolver as csr
Then run:
>>> result = csr.extract_image('<path/to/image/file>')
to perform the extraction.
This runs ChemSchematicResolver on the image and returns a list of tuples to output. Each tuple consists of a SMILES string and a list of label candidates, where each tuple identifies a unique structure. For example:
>>> print(result)
[(['1a'], 'C1CCCCC1'), (['1b'], 'CC1CCCCC1')]
To automatically extract the structures and labels of diagrams from a HTML or XML article, use the extract_document method instead:
>>> result = csr.extract_document('<path/to/document/file>')
If the user has permissions to access the full article, this function will download all relevant images locally to a directory called csr, and extract from them automatically. The csr directory with then be deleted.
The tool currently supports HTML documents from the Royal Society of Chemistry and Springer, as well as XML files obtained using the Elsevier Developers Portal .
ChemSchematicResolver will return the complete chemical records from the document extracted with ChemDataExtractor, enriched with extracted structure and raw label. For example:
>>> print(result)
{'labels': ['1a'], 'roles': ['compound'], 'melting_points': [{'value': '5', 'units': '°C'}], 'diagram': { 'smiles': 'C1CCCCC1', 'label': '1a' } }
Alternatively, if you just want the structures and labels extracted from the images without the ChemDataExtractor output, run:
>>> result = csr.extract_document('<path/to/document/file>', extract_all=False)
which, for the above example, will return:
>>> print(output)
[(['1a'], 'C1CCCCC1')]