  The deadly coronavirus disease 2019 (COVID-19) pandemic caused by severe
acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has gone out of control
globally. Despite much effort by scientists, medical experts, and society in
general, the slow progress on drug discovery and antibody therapeutic
development, the unknown possible side effects of the existing vaccines, and
the high transmission rate of the SARS-CoV-2, remind us of the sad reality that
our current understanding of the transmission, infectivity, and evolution of
SARS-CoV-2 is unfortunately very limited. The major limitation is the lack of
mechanistic understanding of viral-host cell interactions, the viral
regulation, protein-protein interactions, including antibody-antigen binding,
protein-drug binding, host immune response, etc. This limitation will likely
haunt the scientific community for a long time and have a devastating
consequence in combating COVID-19 and other pathogens. Notably, compared to the
long-cycle, highly cost, and safety-demanding molecular-level experiments, the
theoretical and computational studies are economical, speedy, and easy to
perform. There exists a tsunami of the literature on molecular modeling,
simulation, and prediction of SARS-CoV-2 that has become impossible to fully be
covered in a review. To provide the reader a quick update about the status of
molecular modeling, simulation, and prediction of SARS-CoV-2, we present a
comprehensive and systematic methodology-centered narrative in the nick of
time. Aspects such as molecular modeling, Monte Carlo (MC) methods, structural
bioinformatics, machine learning, deep learning, and mathematical approaches
are included in this review. This review will be beneficial to researchers who
are looking for ways to contribute to SARS-CoV-2 studies and those who are
assessing the current status in the field.
