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Comput Struct Biotechnol J. 2016 May 7;14:177-84. doi: 10.1016/j.csbj.2016.04.004. eCollection 2016.

Computational approaches in target identification and drug discovery.

Author information

1
University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece.
2
University of Patras, School of Health Sciences, Department of Pharmacy, University Campus, Rion, Patras, Greece; Department of Pathology, College of Medicine and Health Sciences, United Arab Emirates University, Al-Ain, United Arab Emirates.

Abstract

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.

KEYWORDS:

Computer-aided drug discovery; Data integration; Information technologies; Target identification

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