A quantitative structure-activity (affinity) relationship (QSAR) study is carried out to model the proton, sodium, copper, and silver cation affinities of R-amino acids (AA). Stepping multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN) approaches are applied to elucidate the multiple factors affecting these affinities. The MLR and PLS models reveal that the variation in proton affinity is attributed to the highest electrophilic superdelocalizability of nitrogen ( major) and the number of rotatable bonds ( minor) in AA. The noncovalent interactions, especially ion-dipole interactions, are responsible for the changes in Na+ affinity. The ionization potential, dipole moment of the side chain, and degree of linearity are the properties of AA that give the best correlation with the Cu+ and Ag+ affinities. The ANN models are developed to study the relationships (linear or nonlinear) between the molecular descriptors and binding affinities. The ANN models show higher predictive power. The QSAR models are used to study the binding forms of AA (neutral vs zwitterionic) upon protonation/cationization. To our knowledge, this is the first attempt to carry out a QSAR study on protonated/cationized to elucidate their binding properties. In virtue of the Na+ affinity ANN model, the Na+ affinities of dihydroxyphenylalanine (DOPA) were predicted. This work may pave the way for the success of applying similar approaches to peptides or proteins (with AA as the building blocks) in the future.