Determining the values of various properties for new bioinks for 3D printing is a very important task in the design of new materials. For this purpose, a large number of experimental works have been consulted and a database with >1200 bioprinting tests has been created. These tests cover different combinations of conditions in terms of print pressure, temperature, and needle values, for example. These data are difficult to deal with in terms of determining combinations of conditions to optimize the tests and to analyze new options. The best model presented has values of specificity = Sp (%) = 88.4, sensitivity = Sn (%) = 86.2 in training series and Sp (%) = 85.9, Sn (%) = 80.3 in external validation series. This model uses operators based on perturbation theory in order to analyze the complexity of the data. The performance of the model has been compared with neural networks with very similar results. This tool could be easily applied to predict the properties of in silico bioprinting assays.