Papyrus Scaffold Visualizer

Extraction and interactive visualization of data extracted from Papyrus. This package allows you to quickly filter out relevant data from the Papyrus database and make an interactive visualization of the compounds in the data set. By default, the compounds are grouped and colored by scaffold, but you can also customize the grouping and coloring by other means. If you want to quickly analyze what kind of compounds you have in the data set, this can help you to do so effortlessly. The assembled data set is also a good starting point for machine learning.

Getting started

You can install the package from PyPI:

   pip install git+https://github.com/martin-sicho/papyrus-scaffold-visualizer.git@main

You can then use the package to extract data from the Papyrus database and visualize it (examples/example_depiction_papyrus.py):

from qsprpred.data.utils.descriptorcalculator import MoleculeDescriptorsCalculator
from qsprpred.data.utils.descriptorsets import FingerprintSet
from scaffviz.clustering.manifold import TSNE
from qsprpred.data.utils.scaffolds import BemisMurcko
from qsprpred.data.sources.papyrus import Papyrus
from scaffviz.depiction.plot import Plot

acc_keys = ["P51681"] # replace with your own accession key
name = "P51681_LIGANDS_nostereo" # replace with your own name for the output data set file
quality = "low" # choose minimum quality from {"high", "medium", "low"}

# fetches the latest version of Papyrus if not already available and filters out the relevant data
papyrus = Papyrus(data_dir="./data", stereo=False)
dataset = papyrus.getData(
    acc_keys,
    quality,
    name=name,
    use_existing=True # use existing data set if it was already compiled before
)

# add Murcko scaffolds to the data set -> will be used to group compounds inside the plot
dataset.addScaffolds([BemisMurcko(convert_hetero=False, force_single_bonds=False)])

# add Morgan fingerprints to the data set -> these will be used to calculate the t-SNE embedding in 2D
desc_calculator = MoleculeDescriptorsCalculator(descsets=[FingerprintSet(fingerprint_type="MorganFP", radius=3, nBits=2048)])
dataset.addDescriptors(desc_calculator, recalculate=False)

# make an interactive plot that will use t-SNE to embed the data set in 2D (all available descriptors in the data set will be used)
plt = Plot(TSNE(perplexity=150))
plt.plot(dataset, recalculate=True, mols_per_scaffold_group=5, card_data=["all_doc_ids"], title_data='InChIKey')

You can find more example scripts under examples.

License

MIT License.

Credits

This work is based on the code developed by Nadieh van Marrewijk and Yorick van Aalst during their internships at the Computational Drug Discovery Group, LACDR, Leiden University. We would also like to thank the authors of the molplotly package for its continued development and support.