PyDPI:
Freely Available Python Package for Chemoinformatics, Bioinformatics,
and Chemogenomics Studies
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Abstract
The
rapidly increasing amount of publicly available data in biology and
chemistry enables researchers to revisit interaction problems by systematic
integration and analysis of heterogeneous data. Herein, we developed
a comprehensive python package to emphasize the integration of chemoinformatics
and bioinformatics into a molecular informatics platform for drug
discovery. PyDPI (drug–protein interaction with Python) is
a powerful python toolkit for computing commonly used structural and
physicochemical features of proteins and peptides from amino acid
sequences, molecular descriptors of drug molecules from their topology,
and protein–protein interaction and protein–ligand interaction
descriptors. It computes 6 protein feature groups composed of 14 features
that include 52 descriptor types and 9890 descriptors, 9 drug feature
groups composed of 13 descriptor types that include 615 descriptors.
In addition, it provides seven types of molecular fingerprint systems
for drug molecules, including topological fingerprints, electro-topological
state (E-state) fingerprints, MACCS keys, FP4 keys, atom pair fingerprints,
topological torsion fingerprints, and Morgan/circular fingerprints.
By combining different types of descriptors from drugs and proteins
in different ways, interaction descriptors representing protein–protein
or drug–protein interactions could be conveniently generated.
These computed descriptors can be widely used in various fields relevant
to chemoinformatics, bioinformatics, and chemogenomics. PyDPI is freely
available via https://sourceforge.net/projects/pydpicao/