Background: Traditional drug discovery approaches focus on a limited set of target
molecules for treatment against specific indications/diseases. However, drug absorption, dispersion,
metabolism, and excretion (ADME) involve interactions with multiple protein systems.
Drugs approved for particular indication(s) may be repurposed as novel therapeutics for others.
The severely declining rate of discovery and increasing costs of new drugs illustrate the limitations
of the traditional reductionist paradigm in drug discovery. Methods: We developed the
Computational Analysis of Novel Drug Opportunities (CANDO) platform based on a hypothesis
that drugs function by interacting with multiple protein targets to create a molecular interaction
signature that can be exploited for therapeutic repurposing and discovery. We compiled a library of compounds that are human ingestible
with minimal side effects, followed by an ‘all-compounds’ vs ‘all-proteins’ fragment-based multitarget docking with dynamics screen to
construct compound-proteome interaction matrices that were then analyzed to determine similarity of drug behavior. The proteomic signature
similarity of drugs is then ranked to make putative drug predictions for all indications in a shotgun manner. Results: We have previously
applied this platform with success in both retrospective benchmarking and prospective validation, and to understand the effect of
druggable protein classes on repurposing accuracy. Here we use the CANDO platform to analyze and determine the contribution of multitargeting
(polypharmacology) to drug repurposing benchmarking accuracy. Taken together with the previous work, our results indicate
that a large number of protein structures with diverse fold space and a specific polypharmacological interactome is necessary for accurate
drug predictions using our proteomic and evolutionary drug discovery and repurposing platform. Conclusion: These results have implications
for future drug development and repurposing in the context of polypharmacology.