Biological network inference for drug discovery

A better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying a disease. Biological network inference uses computational methods for deducing from high-throughput experimental data, the topology and the causal structure of the interactions among the drugs and their targets. Therefore, biological network inference can support and contribute to the experimental identification of both gene and protein networks causing a disease as well as the biochemical networks of drugs metabolism and mechanisms of action. The resulting high-level networks serve as a foundational basis for more detailed mechanistic models and are increasingly used in drug discovery by pharmaceutical and biotechnology companies. We review and compare recent computational technologies for network inference applied to drug discovery.

► We review biological network inference methods/tools relevant to drug discovery. ► We classify the methods into classifier-based and reverse engineering algorithms. ► We further categorize them on the basis of the input data and inferred networks. ► We discuss challenges, limitations and advantages of the methods and tools. ► Recent promising approaches integrating static and dynamic data are indicated.