Computational Biology and Drug Discovery: From Single-Target to Network Drugs

Title: Computational Biology and Drug Discovery: From Single-Target to Network Drugs

Volume: 1 Issue: 1

Author(s): Alberto Ambesi-Impiombato and Diego d Bernardo

Affiliation:

Keywords: lead identification, Supervised-learning methods, Leukemia, titration-invariant similarity score (TISS), bioinformatics, reverse engineering

Abstract: The drug discovery process is complex, time consuming and expensive, and includes preclinical and clinical phases. The pharmaceutical industry is moving from a symptomatic relief focus towards a more pathology-based approach where a better understanding of the pathophysiology should help deliver drugs whose targets are involved in the causative processes underlying the disease. Computational biology and bioinformatics have the potential not only to speed up the drug discovery process, thus reducing the costs, but also to change the way drugs are designed. In this review we focus on the different computational and bioinformatics approaches that have been proposed and applied to the different steps involved in the drug development process. The development of network-reconstruction methods is now making it possible to infer a detailed map of the regulatory circuit among genes, proteins and metabolites. It is likely that the development of these technologies will radically change, in the next decades, the drug discovery process, as we know it today.

Cite this article as:

Ambesi-Impiombato Alberto and Bernardo d Diego, Computational Biology and Drug Discovery: From Single-Target to Network Drugs, Current Bioinformatics 2006; 1 (1) . https://dx.doi.org/10.2174/157489306775330598