Year: 2026
Type: article
Abstract: Artificial intelligence (AI) has emerged as a powerful tool in biomedical research, offering new opportunities for molecular target identification and the development of precision phytotherapy.AI enables the systematic identification of bioactive plant compounds by integrating machine learning (ML) and deep learning (DL) approaches with phytochemical and multi-omics datasets, including genomic, transcriptomic, and metabolomic data.It also facilitates the prediction of their pharmacological activities and the elucidation of their molecular mechanisms of action.Advanced computational models such as neural networks, support vector machines, and generative algorithms are increasingly applied in virtual screening, QSAR modeling, and ADMET prediction, accelerating the discovery and optimization of plant-derived therapeutic candidates.Furthermore, AI-driven platforms facilitate the design of novel phytochemical analogues and support drug repurposing strategies, thereby enhancing the efficiency and scalability of natural product-based drug discovery.Despite existing challenges such as data heterogeneity, model interpretability, and the need for clinical validation, the integration of AI with phytochemistry and precision medicine provides a promising framework for translating complex biological data into personalized therapeutic strategies.
Author: Sevgi Gezici
Institution: Gaziantep University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7169847023