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Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products

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Published on: March 12, 2020

Artificial intelligence in natural products research.

Xiao Yuan¹, Xiaobo Yang², Qiyuan Pan¹

¹Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.

Chinese Journal of Natural Medicines

|November 19, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) accelerates natural medicine drug discovery by analyzing complex data. AI enhances screening, optimization, and pathway design, improving bioavailability and safety, despite current data and ethical challenges.

Keywords:

Artificial intelligence Deep learning Drug discovery Model interpretability Natural products

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Area of Science:

Pharmacology
Computational Biology
Natural Products Chemistry

Background:

Natural medicines offer diverse therapeutic applications but face R&D hurdles due to complex compositions and mechanisms.
Challenges include component complexity, extraction difficulties, and efficacy validation in natural product research.

Purpose of the Study:

To explore the transformative role of Artificial Intelligence (AI) in accelerating drug discovery and development within natural medicines research.
To highlight AI's potential in overcoming R&D challenges for natural products.

Main Methods:

Application of deep learning (DL) and machine learning (ML) approaches for analyzing extensive datasets.
Utilizing AI for efficient drug screening, component analysis, and pharmacological mechanism elucidation.
Employing AI in virtual screening, compound optimization, and synthetic pathway design.

Main Results:

AI facilitates efficient analysis of complex natural medicine data, aiding in drug screening and mechanism elucidation.
AI implementation shows potential in enhancing natural medicines' bioavailability and safety profiles through optimization and pathway design.
Current AI applications in this field face limitations concerning data quality, model interpretability, and ethical considerations.

Conclusions:

AI technology, including DL and ML, significantly enhances efficiency and precision in natural medicines research and development.
AI integration promises to advance personalized medicine and contemporary drug development strategies.
Continued evolution of AI will further refine natural product drug discovery, addressing existing limitations.