cando.py: Open source software for predictive bioanalytics of large scale drug-protein-disease data

Abstract

Elucidating drug-protein interactions is essential for understanding the beneficial effects of small molecule therapeutics in human disease states. Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets. Analyzing drug interactions with a library of proteins provides further 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 [1-7]. The CANDO package allows for rapid drug similarity assessment, most notably via the bioinformatic docking 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 bench-marking protocols to determine how well drugs are related to each other in terms 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/.