With the rapid development of high-throughput genomic technologies and the accumulation
of genome-wide datasets for human disease, it has been shown that using only reductionistic principles
has been difficult to capture the complex biological networks and design rational medication.
However, the emerging paradigm of “network based methodology” proposes to harness the power of
networks to uncover relationships between various data types of interest for drug discovery. Recent
advances include networks that encompass relationships between drugs, disease-related genes,
therapeutic targets and diseases. It is shown how network techniques can help in the investigation of
the mechanism of action of existing drugs, new molecules, or to identify novel disease genes and targets. We review how
these different types of network analysis approaches facilitate drug discovery and their associated challenges. Some
representative examples are reviewed to show that network analysis is a powerful, integrated, computational and
experimental approach to improve the drug discovery process.