Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions
Artificial intelligence (AI) has emerged as a powerful tool in medical sciences that is revolutionizing various fields of drug research. AI algorithms can analyze large-scale biological data and identify molecular targets and pathways advancing pharmacological knowledge. An especially promising area...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Pharmaceuticals |
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| Online Access: | https://www.mdpi.com/1424-8247/18/3/282 |
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| author | Marios Spanakis Eleftheria Tzamali Georgios Tzedakis Chryssalenia Koumpouzi Matthew Pediaditis Aristides Tsatsakis Vangelis Sakkalis |
| author_facet | Marios Spanakis Eleftheria Tzamali Georgios Tzedakis Chryssalenia Koumpouzi Matthew Pediaditis Aristides Tsatsakis Vangelis Sakkalis |
| author_sort | Marios Spanakis |
| collection | DOAJ |
| description | Artificial intelligence (AI) has emerged as a powerful tool in medical sciences that is revolutionizing various fields of drug research. AI algorithms can analyze large-scale biological data and identify molecular targets and pathways advancing pharmacological knowledge. An especially promising area is the assessment of drug interactions. The AI analysis of large datasets, such as drugs’ chemical structure, pharmacological properties, molecular pathways, and known interaction patterns, can provide mechanistic insights and identify potential associations by integrating all this complex information and returning potential risks associated with these interactions. In this context, an area where AI may prove valuable is in the assessment of the underlying mechanisms of drug interactions with natural products (i.e., herbs) that are used as dietary supplements. These products pose a challenging problem since they are complex mixtures of constituents with diverse and limited information regarding their pharmacological properties, especially their pharmacokinetic data. As the use of herbal products and supplements continues to grow, it becomes increasingly important to understand the potential interactions between them and conventional drugs and the associated adverse drug reactions. This review will discuss AI approaches and how they can be exploited in providing valuable mechanistic insights regarding the prediction of interactions between drugs and herbs, and their potential exploitation in experimental validation or clinical utilization. |
| format | Article |
| id | doaj-art-b29d5555ae314074adacdc49eadfeb40 |
| institution | OA Journals |
| issn | 1424-8247 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj-art-b29d5555ae314074adacdc49eadfeb402025-08-20T01:48:45ZengMDPI AGPharmaceuticals1424-82472025-02-0118328210.3390/ph18030282Artificial Intelligence Models and Tools for the Assessment of Drug–Herb InteractionsMarios Spanakis0Eleftheria Tzamali1Georgios Tzedakis2Chryssalenia Koumpouzi3Matthew Pediaditis4Aristides Tsatsakis5Vangelis Sakkalis6Department of Toxicology and Forensic Sciences, School of Medicine, University of Crete, 71003 Heraklion, GreeceComputational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, GreeceComputational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, GreeceComputational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, GreeceComputational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, GreeceDepartment of Toxicology and Forensic Sciences, School of Medicine, University of Crete, 71003 Heraklion, GreeceComputational Bio-Medicine Laboratory, Institute of Computer Science, Foundation for Research and Technology—Hellas, 70013 Heraklion, GreeceArtificial intelligence (AI) has emerged as a powerful tool in medical sciences that is revolutionizing various fields of drug research. AI algorithms can analyze large-scale biological data and identify molecular targets and pathways advancing pharmacological knowledge. An especially promising area is the assessment of drug interactions. The AI analysis of large datasets, such as drugs’ chemical structure, pharmacological properties, molecular pathways, and known interaction patterns, can provide mechanistic insights and identify potential associations by integrating all this complex information and returning potential risks associated with these interactions. In this context, an area where AI may prove valuable is in the assessment of the underlying mechanisms of drug interactions with natural products (i.e., herbs) that are used as dietary supplements. These products pose a challenging problem since they are complex mixtures of constituents with diverse and limited information regarding their pharmacological properties, especially their pharmacokinetic data. As the use of herbal products and supplements continues to grow, it becomes increasingly important to understand the potential interactions between them and conventional drugs and the associated adverse drug reactions. This review will discuss AI approaches and how they can be exploited in providing valuable mechanistic insights regarding the prediction of interactions between drugs and herbs, and their potential exploitation in experimental validation or clinical utilization.https://www.mdpi.com/1424-8247/18/3/282drug–herb interactionsherbal medicinesartificial intelligenceinteractionsmachine learningdeep learning |
| spellingShingle | Marios Spanakis Eleftheria Tzamali Georgios Tzedakis Chryssalenia Koumpouzi Matthew Pediaditis Aristides Tsatsakis Vangelis Sakkalis Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions Pharmaceuticals drug–herb interactions herbal medicines artificial intelligence interactions machine learning deep learning |
| title | Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions |
| title_full | Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions |
| title_fullStr | Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions |
| title_full_unstemmed | Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions |
| title_short | Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions |
| title_sort | artificial intelligence models and tools for the assessment of drug herb interactions |
| topic | drug–herb interactions herbal medicines artificial intelligence interactions machine learning deep learning |
| url | https://www.mdpi.com/1424-8247/18/3/282 |
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