Functional Approaches to Enzymatic Synthesis of New Compounds

The field of biotechnology highly values natural compounds isolated from medicinal plants, but their discovery, purification, biofunctional characterization, and biochemical validation have historically been time-consuming and laborious processes. To more efficiently discover new bioactive substances, two innovative approaches have emerged: the predicted data mining approach (PDMA) and biotransformation-guided purification (BGP). PDMA is a computational method that predicts biotransformation potential, identifying potential substrates for specific enzymes from numerous candidate compounds to generate new compounds. BGP combines enzymatic biotransformation with traditional purification techniques to directly identify and isolate biotransformed products from crude extract fractions. This review examines recent research employing BGP or PDMA for novel compound discovery. These studies demonstrate that both approaches effectively discover novel bioactive molecules from natural sources, enhance bioactivity and solubility of existing compounds, and provide alternatives to traditional methods. The findings highlight the potential of integrating traditional medicinal knowledge with modern enzymatic and computational tools to advance drug discovery and development.