Jain, A.; Pandey, N.; Roy, A.

Enzymes have emerged as an important alternative to traditional catalysts in chemical industries over the past few decades owing to their sustainable nature. The application of enzymes in chemical synthesis relies on their ability to catalyse promiscuous reactions. Promiscuous activity of enzymes is an abundant phenomenon in nature; approximately 37% of Escherichia coli K12 enzymes show promiscuous activity. This highlights the vast expanse of chemical reactions that can be made biochemically feasible through selection of the correct candidate enzymes. Here, we present EnzFinder, a promiscuous enzyme prediction tool that filters candidate enzymes based on similarity in chemical transformation patterns and subsequently ranks them using substrate product similarity, enabling enzyme prioritization up to the fourth level of EC classification without requiring sequence information. On a blind benchmarking set of 2,309 biochemical reactions, the method achieves substantially higher prediction accuracy than existing rule based and deep learning approaches, with improvements exceeding 20% at the sub subclass level and significantly higher coverage at the fourth level. Application to industrially relevant reactions demonstrates EnzFinder ability to identify alternative enzymes with higher substrate similarity and improved kinetic potential. Furthermore, integration of EnzFinder with in silico retrosynthesis tools enables effective prioritization of enzymatic steps within hybrid chemical biological pathways. Together, these results establish EnzFinder as a practical and interpretable tool for accelerating enzyme discovery and promoting greener, enzyme driven synthesis routes.