ChEMBL is a manually curated chemical database of bioactive molecules with drug-like properties. It is maintained by the European Bioinformatics Institute (EBI), of the European Molecular Biology Laboratory (EMBL) based in Hinxton, UK.
insilico helps drug researchers find promising compounds for drug discovery. It preprocesses ChEMBL molecular data and outputs Lapinski's descriptors and chemical fingerprints using popular bioinformatic libraries. Additionally, this package can be used to make a decision tree model that predicts drug efficacy.
The term in silico is a neologism used to mean pharmacology hypothesis development & testing performed via computer (silicon), and is related to the more commonly known biological terms in vivo ("within the living") and in vitro ("within the glass".)
Installation via pip:
$ pip install insilico
Installation via cloned repository:
$ git clone https://github.com/konstanzer/insilico
$ cd insilico
$ python setup.py install
For preprocessing, rdkit-pypi, padelpy, and chembl_webresource_client and for modeling, sklearn and seaborn
insilico offers two primary functions: one to search the ChEMBL database and a second to output preprocessed ChEMBL data based on the molecular ID, which saves the chemical fingerprint in the data folder.
Using the chemical fingerprint, the ModelChembl class creates a decision tree and outputs residual plots and metrics. When declaring the modeling class, you may specify a test set size and a variance threshold, which sets the minimum variance allowed for each column. This optional step can eliminate hundreds of features unhelpful for modeling.
When calling the tree function, you may specify max tree depth and cost-complexity alpha, hyperparameters to control overfitting.
from insilico import target_search, process_target_data, ModelChembl
# return search results for 'P. falciparum D6'
result = target_search('P. falciparum D6')
# return molecular data for CHEMBL2367107 (P. falciparum D6)
df = process_target_data('CHEMBL2367107')
# display molecular descriptor plots
plot_descriptors(df)
model = ModelChembl(df, test_size=0.2, var_threshold=0.15)
# return a fitted decision tree & test set predictions
tree, predictions = model.tree(max_depth=50, ccp_alpha=0.)
# return metrics (R^2 and MAE) & display plots for test set
metrics = model.evaluate(predictions)
# return split data for other modeling
X_train, X_test, y_train, y_test = model.get_data()Valid fingerprinters are "PubchemFingerprinter" (default), "ExtendedFingerprinter", "EStateFingerprinter", "GraphOnlyFingerprinter", "MACCSFingerprinter", "SubstructureFingerprinter", "SubstructureFingerprintCount", "KlekotaRothFingerprinter", "KlekotaRothFingerprintCount", "AtomPairs2DFingerprinter", and "AtomPairs2DFingerprintCount".
df = process_target_data('CHEMBL2367107', fp='SubstructureFingerprinter')Bioinformatics Project from Scratch: Drug Discovery by Chanin Nantasenamat