  Despite considerable progress in genome- and proteome-based high-throughput
screening methods and in rational drug design, the increase in approved drugs
in the past decade did not match the increase of drug development costs.
Network description and analysis not only give a systems-level understanding of
drug action and disease complexity, but can also help to improve the efficiency
of drug design. We give a comprehensive assessment of the analytical tools of
network topology and dynamics. The state-of-the-art use of chemical similarity,
protein structure, protein-protein interaction, signaling, genetic interaction
and metabolic networks in the discovery of drug targets is summarized. We
propose that network targeting follows two basic strategies. The central hit
strategy selectively targets central nodes/edges of the flexible networks of
infectious agents or cancer cells to kill them. The network influence strategy
works against other diseases, where an efficient reconfiguration of rigid
networks needs to be achieved by targeting the neighbors of central nodes or
edges. It is shown how network techniques can help in the identification of
single-target, edgetic, multi-target and allo-network drug target candidates.
We review the recent boom in network methods helping hit identification, lead
selection optimizing drug efficacy, as well as minimizing side-effects and drug
toxicity. Successful network-based drug development strategies are shown
through the examples of infections, cancer, metabolic diseases,
neurodegenerative diseases and aging. Summarizing more than 1200 references we
suggest an optimized protocol of network-aided drug development, and provide a
list of systems-level hallmarks of drug quality. Finally, we highlight
network-related drug development trends helping to achieve these hallmarks by a
cohesive, global approach.
