Chemical protein synthesis (CPS) is a powerful technique to create proteins for biochemical studies or drug discovery. Proteins that are difficult or impossible to obtain by recombinant means, such as those containing non-canonical aa, post-translational modifications, site-specific labels, or even fully mirror-image D-proteins, can be produced by CPS with high homogeneity and precise atomic control. The general principle involves producing peptides of up to ~50 aa by solid-phase peptide synthesis (SPPS), joining peptide segments together through chemoselective ligation reactions (most commonly native chemical ligation, NCL), and folding into a functional protein. Since the inception of NCL three decades ago, CPS has grown from a specialty technique into a field of its own, with dedicated international conferences as well as a publicly available Protein Chemical Synthesis Database (PCS-db) with information on hundreds of synthesized proteins reported in literature. As the community grows and aims for larger and more diverse protein targets, the field needs data science tools to manage the complexity of information in a typical CPS project.