In recent years, the traditional view that enzymes have strict substrate specificity has been replaced by the promiscuity theory 16 . Enzyme promiscuity is defined as the capability of enzymes to catalyze other substrates besides their natural substrate 17 . More than 37% of enzymes in E. coli are promiscuous, and these enzymes catalyze >65% of the known metabolic reactions in E. coli 18 . This finding inspired us to establish a prediction model for enzyme promiscuity and use it to screen enzymes that can specifically degrade mycotoxins. A few methods have recently been proposed to predict enzyme promiscuity based on the similarity theory, assuming the substrates catalyzed by an enzyme generally possess similar structural characteristics 19 . However, being limited by poor generalization abilities, these methods cannot yield reliable results for complex and lessstudied molecules, such as mycotoxin ochratoxin A (OTA) and zearalenone (ZEA) . Another strategy used to identify functional enzymes for the catalysis of a specific biochemical reaction is predicting enzyme commission (EC) numbers based on sequence characteristics 20 ; however, this strategy only applies to common reactions involved in the EC system. For complex molecules and less-studied reactions, no effective computational method exists to identify their catalytic enzymes. The feasibility of enzymatic reactions is determined by complex interactions between enzymes and substrates. Hence, an enzyme's substrate promiscuity is more suitably predicted based on the characteristics of both the enzyme and substrate 21 . Many active enzymesubstrate pairs have been annotated in bioinformatics databases 22-27 . However, owing to the lack of negative samples (e.g., a certain enzyme that cannot catalyze a certain substrate), the feasibility of predicting enzyme promiscuity based on both enzyme and substrate characteristics remains tenuous. Moreover, although data-driven models enable the rapid prediction of potential enzymes, enzyme screening is a slow process owing to the long experimental period of traditional verification systems.