Identifying functional enzymes for a specific substrate and determining their interaction has been a major bottleneck in the fields of synthetic biology, metabolic engineering, and pollutant biodegradation. Here, we constructed a comprehensive enzyme promiscuity dataset containing over 600,000 known enzyme-substrate pairs and developed PU-EPP for robust enzyme promiscuity prediction based on weighted random sampling and PU learning strategies. PU-EPP achieved good performance on test sets and presented significantly better robustness and antiinterference ability than that of baseline models in cases of highly biased data. PU-EPP was combined with a rapid CFPE system to establish a rapid screening framework, which shortened the time needed for new enzyme discovery from several years to 29 d. Using the framework, we successfully identified nine carboxylic-ester hydrolases that can specifically open the macrocyclic structure of ZEA and six amidohydrolases that can efficiently degrade OTA into OTA α, by causing fundamental alterations to their conformation and eliminating most of their toxicity. The attention mechanism of PU-EPP enabled us to identify important enzyme residues, which can assist in downstream enzyme engineering and modification to optimize catalytic efficiency and stability, and ultimately promote the practical application of enzymes.