Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles

Respiratory viruses are infectious agents, which can
cause pandemics.
Although nowadays the danger associated with respiratory viruses continues
to be evidenced by the severe acute respiratory syndrome coronavirus
2 (SARS-CoV-2) as the virus responsible for the current COVID-19 pandemic,
other viruses such as SARS-CoV-1, the influenza A and B viruses (IAV
and IBV, respectively), and the respiratory syncytial virus (RSV)
can lead to globally spread viral diseases. Also, from a biological
point of view, most of these viruses can cause an organ-damaging hyperinflammatory
response known as the cytokine storm (CS). Computational approaches
constitute an essential component of modern drug development campaigns,
and therefore, they have the potential to accelerate the discovery
of chemicals able to simultaneously inhibit multiple molecular and
nonmolecular targets. We report here the first multicondition model
based on quantitative structure–activity relationships and
an artificial neural network (mtc-QSAR-ANN) for the virtual design
and prediction of molecules with dual pan-antiviral and anti-CS profiles.
Our mtc-QSAR-ANN model exhibited an accuracy higher than 80%. By interpreting
the different descriptors present in the mtc-QSAR-ANN model, we could
retrieve several molecular fragments whose assembly led to new molecules
with drug-like properties and predicted pan-antiviral and anti-CS
activities.