In the big data era, voluminous datasets are routinely acquired, stored and analyzed with the aim to inform biomedical discoveries and validate hypotheses. No doubt, data volume and diversity have dramatically increased by the advent of new technologies and open data initiatives. Big data are used across the whole drug discovery pipeline from target identification and mechanism of action to identification of novel leads and drug candidates. Such methods are depicted and discussed, with the aim to provide a general view of computational tools and databases available. We feel that big data leveraging needs to be cost-effective and focus on personalized medicine. For this, we propose the interplay of information technologies and (chemo)informatic tools on the basis of their synergy.
Data integration; Information technologies; Target identification; Computer-aided drug discovery
Citation: Theodora Katsila, Georgios A. Spyroulias, George P. Patrinos, Minos-Timotheos Matsoukas Computational Approaches In Target Identification And Drug Discovery doi:10.1016/j.csbj.2016.04.004
Received: 31 January 2016, Revised: 21 April 2016, Accepted: 25 April 2016, Available online: 7 May 2016
Copyright: © 2016 Katsila et al.. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
This work was supported by a grant to M.-T.M. from the Greek State Scholarships Foundation.
The authors have no conflict of interests to disclose.