cando.py: Open Source Software for Predictive Bioanalytics of Large Scale Drug–Protein–Disease Data

Traditional
drug discovery methods focus on optimizing the efficacy
of a drug against a single biological target of interest for a specific
disease. However, evidence supports the multitarget theory, i.e.,
drugs work by exerting their therapeutic effects via interaction with
multiple biological targets, which have multiple phenotypic effects.
Analytics of drug–protein interactions on a large proteomic
scale provides insight into disease systems while also allowing for
prediction of putative therapeutics against specific indications.
We present a Python package for analysis of drug–proteome and
drug–disease relationships implementing the Computational Analysis
of Novel Drug Opportunities (CANDO) platform. The CANDO package allows
for rapid drug similarity assessment, most notably via an in-house
interaction scoring protocol where billions of drug–protein
interactions are rapidly scored and the similarity of drug-proteome
interaction signatures is calculated. The package also implements
a variety of benchmarking protocols for shotgun drug discovery and
repurposing, i.e., to determine how every known drug is related to
every other in the context of the indications/diseases for which they
are approved. Drug predictions are generated through consensus scoring
of the most similar compounds to drugs known to treat a particular
indication. Support for comparing and ranking novel chemical entities,
as well as machine learning modules for both benchmarking and putative
drug candidate prediction is also available. The CANDO Python package
is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.