Metadata about: The ongoing outbreak of the recently emerged 2019 novel coronavirus (nCoV), which has seriously threatened global health security, is caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) with high morbidity and mortality. Despite the burden of the disease worldwide, still, no licensed vaccine or any specific drug against 2019‐nCoV is available. Data from several countries show that few repurposed drugs using existing antiviral drugs have not (so far) been satisfactory and more recently were proven to be even highly toxic. These findings underline an urgent need for preventative and therapeutic interventions designed to target specific aspects of 2019‐nCoV. Again the major factor in this urgency is that the process of data acquisition by physical experiment is time‐consuming and expensive to obtain. Scientific simulations and more in‐depth data analysis permit to validate or refute drug repurposing opportunities predicted via target similarity profiling to speed up the development of a new more effective anti‐2019‐nCoV therapy especially where in vitro and/or in vivo data are not yet available. In addition, several research programs are being developed, aiming at the exploration of vaccines to prevent and treat the 2019‐nCoV. Computational‐based technology has given us the tools to explore and identify potentially effective drug and/or vaccine candidates which can effectively shorten the time and reduce the operating cost. The aim of the present review is to address the available information on molecular determinants in disease pathobiology modules and define the computational approaches employed in systematic drug repositioning and vaccine development settings for SARS‐CoV‐2.

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