Computational approaches in target identification and drug discovery

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 a...

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Bibliographic Details
Published in:Computational and structural biotechnology journal Vol. 14; no. C; pp. 177 - 184
Main Authors: Katsila, Theodora, Spyroulias, Georgios A., Patrinos, George P., Matsoukas, Minos-Timotheos
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.01.2016
Research Network of Computational and Structural Biotechnology
Elsevier
Subjects:
biotechnology
Computer-aided drug discovery
cost effectiveness
data collection
Data integration
drugs
Information technologies
mechanism of action
medicine
Mini Review
Target identification
Computer-aided drug discovery
Information technologies
Data integration
Target identification
ISSN:2001-0370, 2001-0370
Online Access:Get full text
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Summary: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.
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These authors contributed equally to this work.
ISSN:2001-0370
2001-0370
DOI:10.1016/j.csbj.2016.04.004