New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids.

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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition (2018, positive): “Baseline PCM vectors were concatenated Morgan FPs (2048 bits) and z-scales 22 which are sequence-based physicochemical protein descriptors.”
GPCRdb in 2021: integrating GPCR sequence, structure and function (2020, methodology): “The amino acids conservation, shown under a GPCRdb sequence alignment, have herein been complemented with conservation and consensus of residue properties and mean values of numeric amino acid descriptors, ‘z-scales’ (20) (Materials and Methods).”
Benchmarking of protein descriptor sets in proteochemometric modeling (part 1): comparative study of 13 amino acid descriptor sets (2013, methodology): “Sandberg et al. [14] improved on the original Z-scales published by Hellberg et al. [15] by introducing two more Z-scales, bringing the total to five scales rather than three and using 26 properties derived for 87 AAs.”
Cell-penetrating peptides: design, synthesis, and applications. (2014, methodology): “A computational approach can be applied by incorporating z-descriptors.(57) Using Sandberg's expanded”
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches (2019, background): “Z-Scale ZSCALE (44,45) TPC Tri-Peptide Composition (37) GTPC Grouped Tri-Peptide Composition (37) CKSAAP Composition of k-spaced Amino Acid Pairs (46–49)”