Multivariate statistical analyses were used to: (i) comprehend the large data sets obtained from the CaverDock calculations, (ii) establish structure-activity relationships, and (iii) select the best potential drug candidates. Two statistically significant models were generated by PCA using the CaverDock results obtained using the set of 4358 ligands and six protein states. The data used in the PCA were the minimum binding energies for each drug along the trajectory and the proportions of the trajectory during which the docked ligand was in contact with one, two, and all three individual subunits of the s-glycoprotein trimer, expressed as percentages. The first PCA model (PCA-1) used 24 variables: 3 related to the minimum binding energies for each protein state, and 3 quantifying the percentages of the trajectory during which the drug was in contact with 1, 2, and 3 units of the trimeric s-glycoprotein. Ten statistically significant principal components were obtained, collectively explaining 98% of the variation in the data. The second model, PCA-2, was generated using 12 variables representing the energy minima and the percentages of each trajectory during which the drug was in contact with all three monomeric units of the s-glycoprotein trimer for each of the six studied protein states. This model yielded only two principal components that explained 85% and 8% of the variation in the data, respectively. Because it had only two principal components, this model was easier to interpret than the first. The top hits predicted by the two models were very similar, so only the results obtained with the simpler model 2 will be discussed further. By inspecting the distribution of the docked compounds in the 2D space spanned by the first two principal components (Figure ), the compounds interacting most strongly with all three subunits of the spike protein were identified (ESI).