The quantitative structure-property relationship (QSPR) methodology is applied to estimate the binding affinity of lithium, sodium, potassium, copper, and silver cations to the 20 common amino acids. The proposed model, nonlinearly derived from computational neural networks (CNN), contains seven descriptors and was validated by an external prediction set. Good results are obtained with correlation coefficients, R-2, and root-mean-square errors (rms) (kJ/mol) of 0.998 (3.89), 0.999 (2.86), and 0.997 (3.90) for the training, prediction, and validation sets, respectively. Five of the descriptors of the model correspond to the amino acids and the other two to the cations; they encode information clearly related to the cation-amino acid interactions responsible for the binding affinity values analyzed. A detailed analysis of results shows that, despite the different nature of the bonding between the metal cations and the amino acids, the neural networks used are capable of predicting accurately the property studied.