Fine-tuning biosynthetic pathways is crucial for the development of economic feasible microbial cell factories. Therefore, the use of computational models able to predictably design regulatory sequences for pathway engineering proves to be a valuable tool, especially for modifying genes at the translational level. In this study we developed a computational approach for the de novo design of 5′-untranslated regions (5′UTRs) in Saccharomyces cerevisiae with a predictive outcome on translation initiation rate. On the basis of existing data, a partial least-squares (PLS) regression model was trained and showed good performance on predicting protein abundances of an independent test set. This model was further used for the construction of a “yUTR calculator” that can design 5′UTR sequences with a diverse range of desired translation efficiencies. The predictive power of our yUTR calculator was confirmed in vivo by different representative case studies. As such, these results show the great potential of data driven approaches for reliable pathway engineering in S. cerevisiae.