Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products
Abstract Natural products (NPs) are invaluable resources for drug discovery, characterized by their intricate scaffolds and diverse bioactivities. AI drug discovery & design (AIDD) has emerged as a transformative approach for the rational structural modification of NPs. This review examines a va...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-06-01
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| Series: | Natural Products and Bioprospecting |
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| Online Access: | https://doi.org/10.1007/s13659-025-00521-y |
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| author | Chuan-Su Liu Bing-Chao Yan Han-Dong Sun Jin-Cai Lu Pema-Tenzin Puno |
| author_facet | Chuan-Su Liu Bing-Chao Yan Han-Dong Sun Jin-Cai Lu Pema-Tenzin Puno |
| author_sort | Chuan-Su Liu |
| collection | DOAJ |
| description | Abstract Natural products (NPs) are invaluable resources for drug discovery, characterized by their intricate scaffolds and diverse bioactivities. AI drug discovery & design (AIDD) has emerged as a transformative approach for the rational structural modification of NPs. This review examines a variety of molecular generation models since 2020, focusing on their potential applications in two primary scenarios of NPs structure modification: modifications when the target is identified and when it remains unidentified. Most of the molecular generative models discussed herein are open-source, and their applicability across different domains and technical feasibility have been evaluated. This evaluation was accomplished by integrating a limited number of research cases and successful practices observed in the molecular optimization of synthetic compounds. Furthermore, the challenges and prospects of employing molecular generation modeling for the structural modification of NPs are discussed. Graphical Abstract |
| format | Article |
| id | doaj-art-e1493532bb6a45e392089ea99fb74a43 |
| institution | OA Journals |
| issn | 2192-2195 2192-2209 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Natural Products and Bioprospecting |
| spelling | doaj-art-e1493532bb6a45e392089ea99fb74a432025-08-20T02:31:09ZengSpringerOpenNatural Products and Bioprospecting2192-21952192-22092025-06-0115111710.1007/s13659-025-00521-yBridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural productsChuan-Su Liu0Bing-Chao Yan1Han-Dong Sun2Jin-Cai Lu3Pema-Tenzin Puno4School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical UniversityState Key Laboratory of Phytochemistry and Natural Medicines, Kunming Institute of Botany, Chinese Academy of SciencesState Key Laboratory of Phytochemistry and Natural Medicines, Kunming Institute of Botany, Chinese Academy of SciencesSchool of Traditional Chinese Materia Medica, Shenyang Pharmaceutical UniversityState Key Laboratory of Phytochemistry and Natural Medicines, Kunming Institute of Botany, Chinese Academy of SciencesAbstract Natural products (NPs) are invaluable resources for drug discovery, characterized by their intricate scaffolds and diverse bioactivities. AI drug discovery & design (AIDD) has emerged as a transformative approach for the rational structural modification of NPs. This review examines a variety of molecular generation models since 2020, focusing on their potential applications in two primary scenarios of NPs structure modification: modifications when the target is identified and when it remains unidentified. Most of the molecular generative models discussed herein are open-source, and their applicability across different domains and technical feasibility have been evaluated. This evaluation was accomplished by integrating a limited number of research cases and successful practices observed in the molecular optimization of synthetic compounds. Furthermore, the challenges and prospects of employing molecular generation modeling for the structural modification of NPs are discussed. Graphical Abstracthttps://doi.org/10.1007/s13659-025-00521-yNatural productsArtificial intelligenceMolecular generative modelsStructural modification |
| spellingShingle | Chuan-Su Liu Bing-Chao Yan Han-Dong Sun Jin-Cai Lu Pema-Tenzin Puno Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products Natural Products and Bioprospecting Natural products Artificial intelligence Molecular generative models Structural modification |
| title | Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products |
| title_full | Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products |
| title_fullStr | Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products |
| title_full_unstemmed | Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products |
| title_short | Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products |
| title_sort | bridging chemical space and biological efficacy advances and challenges in applying generative models in structural modification of natural products |
| topic | Natural products Artificial intelligence Molecular generative models Structural modification |
| url | https://doi.org/10.1007/s13659-025-00521-y |
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