Weiduo Liu^1,2

1 Zhejiang zhongxin Medical Plant Seed Co., Ltd, Hangzhou, 311402, Zhejiang, China
2 Zhejiang Agronomist College, Hangzhou, 310021, Zhejiang, China
Author    Correspondence author
Genomics and Applied Biology, 2026, Vol. 17, No. 5   doi: 10.5376/gab.2026.17.0024
Received: 22 Aug., 2026    Accepted: 26 Sep., 2026    Published: 11 Oct., 2026

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Liu W.D., 2026, Computational analysis of growth characteristics and active compound accumulation in Zhejiang medicinal plants, Genomics and Applied Biology, 17(5): 312-325 (doi: 10.5376/gab.2026.17.0024)

Zhejiang Province is recognized as one of the important regions for medicinal plant diversity in China, with abundant resources characterized by unique ecological adaptability and rich accumulation of bioactive compounds. However, the growth performance and medicinal quality of these plants are strongly influenced by complex interactions among genetic background, environmental conditions, cultivation practices, and metabolic regulation processes. Recent advances in computational biology, artificial intelligence, and multi-omics technologies provide new opportunities for systematically investigating plant growth dynamics and active compound accumulation. This review summarizes the biological characteristics and developmental patterns of Zhejiang medicinal plants and discusses computational frameworks integrating phenotypic data, environmental parameters, metabolomics, transcriptomics, and machine learning approaches. Particular emphasis is placed on computational modeling strategies for predicting growth traits, identifying key regulatory factors, and elucidating metabolic pathways associated with bioactive compound biosynthesis.Furthermore, advanced technologies, including remote sensing-based digital phenotyping, network biology, and deep learning models, are evaluated for their potential applications in precision cultivation and medicinal quality optimization. A case study framework is presented to demonstrate how machine learning models can integrate environmental variables, growth indicators, and metabolite profiles to predict plant productivity and medicinal compound accumulation.Despite significant progress, challenges remain in data standardization, model interpretability, and the integration of computational predictions with experimental validation.Future development of artificial intelligence-driven platforms, digital twins, and multi-scale biological models will facilitate sustainable cultivation, resource conservation, and quality improvement of Zhejiang medicinal plants.This computational perspective provides a theoretical and technological foundation for advancing intelligent medicinal plant research and precision herbal medicine production.

Zhejiang medicinal plants; Computational biology; Machine learning prediction; Active compound accumulation; Multi-omics integration