Chang Te-Sheng
Department of Biological Sciences and Technology, National University of Tainan, Tainan 70005, Taiwan.
Molecules. 2025 May 20;30(10):2228. doi: 10.3390/molecules30102228.
In the field of biotechnology, natural compounds isolated from medicinal plants are highly valued; however, their discovery, purification, biofunctional characterization, and biochemical validation have historically involved time-consuming and laborious processes. Two innovative approaches have emerged to more efficiently discover new bioactive substances: the predicted data mining approach (PDMA) and biotransformation-guided purification (BGP). The 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 the PDMA for novel compound discovery. This research demonstrates that both approaches effectively allow for the discovery of novel bioactive molecules from natural sources, the enhancement of the bioactivity and solubility of existing compounds, and the development of alternatives to traditional methods. These findings highlight the potential of integrating traditional medicinal knowledge with modern enzymatic and computational tools to advance drug discovery and development.
在生物技术领域，从药用植物中分离出的天然化合物备受重视；然而，其发现、纯化、生物功能表征及生化验证在历史上一直涉及耗时费力的过程。已出现两种更高效发现新生物活性物质的创新方法：预测数据挖掘方法（PDMA）和生物转化导向纯化（BGP）。PDMA是一种计算方法，可预测生物转化潜力，从众多候选化合物中识别特定酶的潜在底物以生成新化合物。BGP将酶促生物转化与传统纯化技术相结合，直接从粗提物馏分中识别和分离生物转化产物。本综述考察了近期采用BGP或PDMA进行新型化合物发现的研究。该研究表明，这两种方法均能有效实现从天然来源发现新型生物活性分子、提高现有化合物的生物活性和溶解性，以及开发传统方法的替代方法。这些发现凸显了将传统医学知识与现代酶学和计算工具相结合以推进药物发现与开发的潜力。
