Despite
tremendous
efforts in the past two years, our understanding
of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus–host
interactions, immune response, virulence, transmission, and evolution
is still very limited. This limitation calls for further in-depth
investigation. Computational studies have become an indispensable
component in combating coronavirus disease 2019 (COVID-19) due to
their low cost, their efficiency, and the fact that they are free
from safety and ethical constraints. Additionally, the mechanism that
governs the global evolution and transmission of SARS-CoV-2 cannot
be revealed from individual experiments and was discovered by integrating
genotyping of massive viral sequences, biophysical modeling of protein–protein
interactions, deep mutational data, deep learning, and advanced mathematics.
There exists a tsunami of literature on the molecular modeling, simulations,
and predictions of SARS-CoV-2 and related developments of drugs, vaccines,
antibodies, and diagnostics. To provide readers with a quick update
about this literature, we present a comprehensive and systematic methodology-centered
review. Aspects such as molecular biophysics, bioinformatics, cheminformatics,
machine learning, and mathematics are discussed. This review will
be beneficial to researchers who are looking for ways to contribute
to SARS-CoV-2 studies and those who are interested in the status of
the field.