Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However,
to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex
interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient
modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both
through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease
network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable
to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal
interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly
unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining
the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing
the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches
to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric
modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.
Keywords: Network pharmacology, computational models, experimental design, anticancer therapies.
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