Network analysis has diverse roles in drug discovery

Computational biologists use network analysis to uncover relationships between various data types of interest for drug discovery. For example, signalling and metabolic pathways are commonly used to understand disease states and drug mechanisms. However, several other flavours of network analysis techniques are also applicable in a drug discovery context. Recent advances include networks that encompass relationships between diseases, molecular mechanisms and gene targets. Even social networks that mirror interactions within the scientific community are helping to foster collaborations and novel research. We review how these different types of network analysis approaches facilitate drug discovery and their associated challenges.

► Biological networks aid disease/drug mechanism understanding, target identification and biomarker discovery. ► Studying how biological networks respond to different conditions is of paramount importance. ► Integrative mining of drugs, diseases and drug target data help to find polypharmacology and drug-repositioning opportunities. ► Studying social ‘co-author’ networks can generate business opportunities.