Advancement of AI in Natural Product Chemistry
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Natural Products Chemistry".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 200
Editors
Interests: medicinal plants; medicinal mushroom; mushroom; plants; antioxidant; antimicrobial; anticancer
Interests: isolation and structural elucidation of bioactive natural products from plants and microorganisms; structure–activity relationships of bioactive natural products; analytical methods; development and standardization of herbal drug material; herbal drug repurposing—new pharmacological activities of traditional herbs; development of natural product nanoformulations for pharmaceutical applications
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) into natural product research presents an innovative solution for accelerating the isolation, characterization, and application of natural products in drug development. Leveraging AI algorithms has the potential to enhance the precision and efficiency of discovering novel bioactive compounds and molecular scaffolds. This approach helps to overcome traditional hurdles related to complex data interpretation and advances methodologies for structural elucidation and diverse applications of natural products.
In this Special Issue, we welcome research articles and reviews on the following:
- The application of AI techniques, such as machine learning (ML) models which include supervised learning (SL), unsupervised learning (UL), and deep learning (DL), in enhancing NMR and MS data interpretation and analysis and resolving spectral complexity to improve the structural characterization of natural products.
- The application of AI in spectral prediction, pattern recognition, and data-driven model development within the context of natural products.
- The use of Large Language Model (LLM) agents to automate diverse aspects of natural product research, including experimental design, natural product synthesis planning, and data analysis related to natural products.
- Using machine learning models to predict the biological activity of natural compounds and to screen vast databases of natural products for potential drug candidates.
- AI-driven quality assurance in natural products, including the identification of adulterants, assurance of product consistency, and blockchain integration for traceability.
- Perspectives on implementing AI in natural product research, including handling of data privacy, challenges and future trends.
Dr. Mustafa Sevindik
Guest Editor
Dr. Njogu M. Kimani
Guest Editor Assistant
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI
- natural products
- machine learning
- spectral analysis
- data-driven models
- quality assurance
- LLM agents
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