Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors
Abstract The role of LOXL2 in cancer has been widely demonstrated, but current therapies targeting LOXL2 are not yet fully developed. We believe that selective nature-derived inhibition of LOXL2 may provide a better therapeutic approach for the treatment of cancer. Therefore, we adopted a comprehens...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-95530-9 |
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| author | Xiaowei Jia Meng Liu Yushi Tang Jingyan Meng Ruolin Fang Xiting Wang Cheng Li |
| author_facet | Xiaowei Jia Meng Liu Yushi Tang Jingyan Meng Ruolin Fang Xiting Wang Cheng Li |
| author_sort | Xiaowei Jia |
| collection | DOAJ |
| description | Abstract The role of LOXL2 in cancer has been widely demonstrated, but current therapies targeting LOXL2 are not yet fully developed. We believe that selective nature-derived inhibition of LOXL2 may provide a better therapeutic approach for the treatment of cancer. Therefore, we adopted a comprehensive approach combining deep learning and traditional computer-aided drug design methods to screen LOXL2 selective inhibitors. Bioactivity and affinity of the potential LOXL2 inhibitors were determined by molecular docking and virtual screening. At the same time, we experimentally tested the effect of potential LOXL2 inhibitors on cancer cells. Validation showed that it could inhibit proliferation and migration, promote apoptosis of CT26 cells, and reduce the expression level of LOXL2 protein. As a result, we identified a potent LOXL2 inhibitor: the natural product Forsythoside A, and demonstrated that Forsythoside A has an inhibitory effect on tumors. |
| format | Article |
| id | doaj-art-68c3dbde83fb4f8b8c49c0b5b005ed45 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-68c3dbde83fb4f8b8c49c0b5b005ed452025-08-20T02:10:23ZengNature PortfolioScientific Reports2045-23222025-03-0115111510.1038/s41598-025-95530-9Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitorsXiaowei Jia0Meng Liu1Yushi Tang2Jingyan Meng3Ruolin Fang4Xiting Wang5Cheng Li6School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese MedicineSijiqing HospitalSchool of Traditional Chinese Medicine, Tianjin University of Traditional Chinese MedicineSchool of Traditional Chinese Medicine, Tianjin University of Traditional Chinese MedicineSchool of Traditional Chinese Medicine, Tianjin University of Traditional Chinese MedicineSchool of Traditional Chinese Medicine, Beijing University of Chinese MedicineSchool of Traditional Chinese Medicine, Tianjin University of Traditional Chinese MedicineAbstract The role of LOXL2 in cancer has been widely demonstrated, but current therapies targeting LOXL2 are not yet fully developed. We believe that selective nature-derived inhibition of LOXL2 may provide a better therapeutic approach for the treatment of cancer. Therefore, we adopted a comprehensive approach combining deep learning and traditional computer-aided drug design methods to screen LOXL2 selective inhibitors. Bioactivity and affinity of the potential LOXL2 inhibitors were determined by molecular docking and virtual screening. At the same time, we experimentally tested the effect of potential LOXL2 inhibitors on cancer cells. Validation showed that it could inhibit proliferation and migration, promote apoptosis of CT26 cells, and reduce the expression level of LOXL2 protein. As a result, we identified a potent LOXL2 inhibitor: the natural product Forsythoside A, and demonstrated that Forsythoside A has an inhibitory effect on tumors.https://doi.org/10.1038/s41598-025-95530-9LOXL2CancerDeep learningDrug discovery |
| spellingShingle | Xiaowei Jia Meng Liu Yushi Tang Jingyan Meng Ruolin Fang Xiting Wang Cheng Li Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors Scientific Reports LOXL2 Cancer Deep learning Drug discovery |
| title | Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors |
| title_full | Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors |
| title_fullStr | Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors |
| title_full_unstemmed | Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors |
| title_short | Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors |
| title_sort | artificial intelligence accelerates the identification of nature derived potent loxl2 inhibitors |
| topic | LOXL2 Cancer Deep learning Drug discovery |
| url | https://doi.org/10.1038/s41598-025-95530-9 |
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