Therapeutic activity of a significant majority of drugs is determined by their interactions
with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein
targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this
knowledge gap is to employ computational methods to predict protein targets for a given drug molecule,
or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods
that were published in high-impact venues and that predict DPIs based on similarity between drugs
and similarity between protein targets. We analyze the internal databases of known PDIs that these
methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available
source databases. We discuss contents, impact and relationships between these internal and
source databases, and well as the timeline of their releases and publications. The 35 predictors exploit
and often combine three types of similarities that consider drug structures, drug profiles, and
target sequences. We review the predictive architectures of these methods, their impact, and we
explain how their internal DPIs databases are linked to the source databases. We also include a detailed
timeline of the development of these predictors and discuss the underlying limitations of the
current resources and predictive tools. Finally, we provide several recommendations concerning the
future development of the related databases and methods.
Critical review of prostate cancer predictive tools
