In recent years, the
world has faced several outbreaks caused by viral diseases, resulting in deaths
and comorbidities, harming the health of the population. Due to the “constant”
discovery of new antivirals, vaccines, hygiene habits, and basic sanitation,
society had the false impression of being free from these diseases. However, since
the 1980s, various outbreaks have occurred, such as HIV (Human immunodeficiency
virus) and recently, ZIKV (Zika virus), CHIKV (Chikungunya virus), and EBOV
(Ebola virus) have increased the concern about such pathogens, resulting in
advances in drug discovery. In addition, the SARS-CoV-2 outbreak responsible
for 27,417,497 cases, and 894,241 deaths (to date, September 9th, 2020), showed
how scientists should advance to end this disease so damaging to the global health
and economy. In this context, researches focused on drug development have been
improved in recent years. Thus, it is essential to use computational approaches
to accelerate drug discovery in laboratories. Based on this, structure-based
drug design (SBDD) techniques constitute the most used computer-aided
approaches for discovering and developing new drugs. Among these techniques,
molecular dynamics (MD) simulations have been essential steps and their use in
virtual screening studies is considered indispensable. The MD considers the
macromolecule flexibility using Newtonian principles applied to proteins,
enzymes, membranes, nucleic acids, and other systems. Thus, it is possible to
analyze protein-ligand interactions, and also the affinity energy that a
determined ligand exhibits towards its target. Such information is indispensable
for designing and optimizing new active agents. This chapter will be addressed
to concepts and applications of MD simulations, as well as their applications in
the discovery of drugs against Coronaviruses (SARS-, MERS-CoV, and SARSCoV-2);
Influenza (INFV); Chikungunya (CHIKV); Zika (ZIKV); Dengue (DENV); Ebola
(EBOV); and human immunodeficiency virus (HIV), constituting a great source of
helpful information that could be utilized for designing new compounds against these
diseases.