In this study, we aimed to resolve the obstacles of low efficiency and accuracy in traditional enzyme-mining methods with a framework that combines data-driven prediction models with a rapid cell-free protein expression (CFPE) system . We showcased the feasibility and efficiency of the proposed framework with two case studies, identification of new enzymes for ZEA and OTA degradation. Using the framework, we successfully found 15 new enzymes with prominent degradation activity within 29 d and identified critical residues on enzymes based only on sequence-level information without any prior knowledge. Based on our findings, we anticipate that this framework will serve as an indispensable tool for the identification of new functional enzymes. First, potential biotransformation reactions and metabolic products of the target substrate (e.g., mycotoxins) were predicted using reaction rules in ToxinDB. The potential biotransformations were evaluated with regards to safety, economy, and feasibility. Next, optimal biotransformations and the corresponding categories of enzymes were selected based on enzyme commission (EC) numbers for downstream screening. The molecular structure of mycotoxins formatted in the simplified molecular input line entry system (SMILES) and the amino acid sequences of candidate enzymes were analyzed using the positive unlabeled (PU) learningbased enzyme promiscuity prediction (PU-EPP) model to screen enzymes that could specifically catalyze the biotransformation. Finally, selected enzymes were expressed using a CFPE system and assayed for their catalytic activity.