Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs
The accumulation of senescent cells is a major contributor to aging and various age-related diseases, making developing senolytic compounds that are capable of clearing these cells an important area of research. However, progress has been hampered by the limited number of known senolytics and the in...
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MDPI AG
2025-06-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/12/2653 |
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| author | Jinjun Li Kai Zhao Guotai Yang Haohao Lv Renxin Zhang Shuhan Li Zhiyuan Chen Min Xu Naixue Yang Shaoxing Dai |
| author_facet | Jinjun Li Kai Zhao Guotai Yang Haohao Lv Renxin Zhang Shuhan Li Zhiyuan Chen Min Xu Naixue Yang Shaoxing Dai |
| author_sort | Jinjun Li |
| collection | DOAJ |
| description | The accumulation of senescent cells is a major contributor to aging and various age-related diseases, making developing senolytic compounds that are capable of clearing these cells an important area of research. However, progress has been hampered by the limited number of known senolytics and the incomplete understanding of their mechanisms. This study presents a powerful senolytic predictor built using phenotypic data and machine learning techniques to identify compounds with potential senolytic activity. A comprehensive training dataset consisting of 111 positive and 3951 negative compounds was curated from the literature. The dataset was used to train machine learning models, incorporating traditional molecular fingerprints, molecular descriptors, and MoLFormer molecular embeddings. By applying MoLFormer-based oversampling and testing different algorithms, it was found that the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models with MoLFormer embeddings exhibited the best performance, achieving Area Under the Curve (AUC) scores of 0.998 and 0.997, and F1 scores of 0.948 and 0.941, respectively. This senolytic predictor was then used to perform virtual screening of compounds from the DrugBank and TCMbank databases. In the DrugBank database, 98 structurally novel candidate compounds with potential senolytic activity were identified. For TCMbank, 714 potential senolytic compounds were predicted and 81 medicinal herbs with possible senolytic properties were identified. Moreover, pathway enrichment analysis revealed key targets and potential mechanisms underlying senolytic activity. In an experimental screening of predicted compounds, panaxatriol was found to exhibit senolytic activity on the etoposide-induced senescence of the IMR-90 cell line. Additionally, voclosporin was found to extend the lifespan of <i>C. elegans</i> more effectively than metformin, demonstrating the value of our model for drug repurposing. This study not only provides an efficient framework for discovering novel senolytic agents, but also highlights the predicted novel senolytic compounds and herbs as valuable starting points for future research into senolytic drug development. |
| format | Article |
| id | doaj-art-3c74fa0e4cb147919b7f3cb4bc4a896b |
| institution | Kabale University |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-3c74fa0e4cb147919b7f3cb4bc4a896b2025-08-20T03:29:39ZengMDPI AGMolecules1420-30492025-06-013012265310.3390/molecules30122653Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and HerbsJinjun Li0Kai Zhao1Guotai Yang2Haohao Lv3Renxin Zhang4Shuhan Li5Zhiyuan Chen6Min Xu7Naixue Yang8Shaoxing Dai9State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, ChinaCenter for Pharmaceutical Sciences, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, ChinaCenter for Pharmaceutical Sciences, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, ChinaState Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, ChinaState Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, ChinaState Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, ChinaState Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, ChinaCenter for Pharmaceutical Sciences, Faculty of Life Science and Technology, Kunming University of Science and Technology, Kunming 650500, ChinaState Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, ChinaState Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, ChinaThe accumulation of senescent cells is a major contributor to aging and various age-related diseases, making developing senolytic compounds that are capable of clearing these cells an important area of research. However, progress has been hampered by the limited number of known senolytics and the incomplete understanding of their mechanisms. This study presents a powerful senolytic predictor built using phenotypic data and machine learning techniques to identify compounds with potential senolytic activity. A comprehensive training dataset consisting of 111 positive and 3951 negative compounds was curated from the literature. The dataset was used to train machine learning models, incorporating traditional molecular fingerprints, molecular descriptors, and MoLFormer molecular embeddings. By applying MoLFormer-based oversampling and testing different algorithms, it was found that the Support Vector Machine (SVM) and Multilayer Perceptron (MLP) models with MoLFormer embeddings exhibited the best performance, achieving Area Under the Curve (AUC) scores of 0.998 and 0.997, and F1 scores of 0.948 and 0.941, respectively. This senolytic predictor was then used to perform virtual screening of compounds from the DrugBank and TCMbank databases. In the DrugBank database, 98 structurally novel candidate compounds with potential senolytic activity were identified. For TCMbank, 714 potential senolytic compounds were predicted and 81 medicinal herbs with possible senolytic properties were identified. Moreover, pathway enrichment analysis revealed key targets and potential mechanisms underlying senolytic activity. In an experimental screening of predicted compounds, panaxatriol was found to exhibit senolytic activity on the etoposide-induced senescence of the IMR-90 cell line. Additionally, voclosporin was found to extend the lifespan of <i>C. elegans</i> more effectively than metformin, demonstrating the value of our model for drug repurposing. This study not only provides an efficient framework for discovering novel senolytic agents, but also highlights the predicted novel senolytic compounds and herbs as valuable starting points for future research into senolytic drug development.https://www.mdpi.com/1420-3049/30/12/2653senolytic compoundsmachine learningvirtual screeningMoLFormerDrugBankTCMbank |
| spellingShingle | Jinjun Li Kai Zhao Guotai Yang Haohao Lv Renxin Zhang Shuhan Li Zhiyuan Chen Min Xu Naixue Yang Shaoxing Dai Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs Molecules senolytic compounds machine learning virtual screening MoLFormer DrugBank TCMbank |
| title | Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs |
| title_full | Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs |
| title_fullStr | Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs |
| title_full_unstemmed | Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs |
| title_short | Development and Application of a Senolytic Predictor for Discovery of Novel Senolytic Compounds and Herbs |
| title_sort | development and application of a senolytic predictor for discovery of novel senolytic compounds and herbs |
| topic | senolytic compounds machine learning virtual screening MoLFormer DrugBank TCMbank |
| url | https://www.mdpi.com/1420-3049/30/12/2653 |
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