Industrial biotechnology provides an efficient, sustainable solution for chemical production. However, designing biochemical pathways based solely on known reactions does not exploit its full potential. Enzymes are known to accept non-native substrates, which may allow novel, advantageous reactions. We have previously developed a computational program named Biological Network Integrated Computational Explorer (BNICE) to predict promiscuous enzyme activities and design synthetic pathways, using generalized reaction rules curated from biochemical reaction databases. Here, we use BNICE to design pathways synthesizing propionic acid from pyruvate. The currently known natural pathways produce undesirable by-products lactic acid and succinic acid, reducing their economic viability. BNICE predicted seven pathways containing four reaction steps or less, five of which avoid these by-products. Among the 16 biochemical reactions comprising these pathways, 44% were validated by literature references. More than 28% of these known reactions were not in the BNICE training dataset, showing that BNICE was able to predict novel enzyme substrates. Most of the pathways included the intermediate acrylic acid. As acrylic acid bioproduction has been well advanced, we focused on the critical step of reducing acrylic acid to propionic acid. We experimentally validated that Oye2p from Saccharomyces cerevisiae can catalyze this reaction at a slow turnover rate (10−3 s−1), which was unknown to occur with this enzyme, and is an important finding for further propionic acid metabolic engineering. These results validate BNICE as a pathway-searching tool that can predict previously unknown promiscuous enzyme activities and show that computational methods can elucidate novel biochemical pathways for industrial applications.