A Multi‐Task Self‐Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors
Abstract Studying the molecular properties of drugs and their interactions with human targets aids in better understanding the clinical performance of drugs and guides drug development. In computer‐aided drug discovery, it is crucial to utilize effective molecular feature representations for predict...
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Wiley
2025-04-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202412987 |
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| author | Xin Yang Yang Wang Ye Lin Mingxuan Zhang Ou Liu Jianwei Shuai Qi Zhao |
| author_facet | Xin Yang Yang Wang Ye Lin Mingxuan Zhang Ou Liu Jianwei Shuai Qi Zhao |
| author_sort | Xin Yang |
| collection | DOAJ |
| description | Abstract Studying the molecular properties of drugs and their interactions with human targets aids in better understanding the clinical performance of drugs and guides drug development. In computer‐aided drug discovery, it is crucial to utilize effective molecular feature representations for predicting molecular properties and designing ligands with high binding affinity to targets. However, designing an effective multi‐task and self‐supervised strategy remains a significant challenge for the pretraining framework. In this study, a multi‐task self‐supervised deep learning framework is proposed, MTSSMol, which utilizes ≈10 million unlabeled drug‐like molecules for pretraining to identify potential inhibitors of fibroblast growth factor receptor 1 (FGFR1). During the pretraining of MTSSMol, molecular representations are learned through a graph neural networks (GNNs) encoder. A multi‐task self‐supervised pretraining strategy is proposed to fully capture the structural and chemical knowledge of molecules. Extensive computational tests on 27 datasets demonstrate that MTSSMol exhibits exceptional performance in predicting molecular properties across different domains. Moreover, MTSSMol's capability is validated to identify potential inhibitors of FGFR1 through molecular docking using RoseTTAFold All‐Atom (RFAA) and molecular dynamics simulations. Overall, MTSSMol provides an effective algorithmic framework for enhancing molecular representation learning and identifying potential drug candidates, offering a valuable tool to accelerate drug discovery processes. All of the codes are freely available online at https:// github.com/zhaoqi106/MTSSMol. |
| format | Article |
| id | doaj-art-df9d9137e32d439ebc25f431357c8ff8 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Wiley |
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| series | Advanced Science |
| spelling | doaj-art-df9d9137e32d439ebc25f431357c8ff82025-08-20T01:51:39ZengWileyAdvanced Science2198-38442025-04-011213n/an/a10.1002/advs.202412987A Multi‐Task Self‐Supervised Strategy for Predicting Molecular Properties and FGFR1 InhibitorsXin Yang0Yang Wang1Ye Lin2Mingxuan Zhang3Ou Liu4Jianwei Shuai5Qi Zhao6School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan Liaoning 114051 P. R. ChinaWenzhou Key Laboratory of Biomedical Imaging Center of Biomedical Physics Wenzhou Institute University of Chinese Academy of Sciences Wenzhou Zhejiang 325001 P. R. ChinaCollege of Computer Science and Technology Jilin University Changchun Jilin 130012 P. R. ChinaSchool of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan Liaoning 114051 P. R. ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou Zhejiang 325001 P. R. ChinaOujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision, and Brain Health) Wenzhou Institute University of Chinese Academy of Sciences Wenzhou Zhejiang 325001 P. R. ChinaSchool of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan Liaoning 114051 P. R. ChinaAbstract Studying the molecular properties of drugs and their interactions with human targets aids in better understanding the clinical performance of drugs and guides drug development. In computer‐aided drug discovery, it is crucial to utilize effective molecular feature representations for predicting molecular properties and designing ligands with high binding affinity to targets. However, designing an effective multi‐task and self‐supervised strategy remains a significant challenge for the pretraining framework. In this study, a multi‐task self‐supervised deep learning framework is proposed, MTSSMol, which utilizes ≈10 million unlabeled drug‐like molecules for pretraining to identify potential inhibitors of fibroblast growth factor receptor 1 (FGFR1). During the pretraining of MTSSMol, molecular representations are learned through a graph neural networks (GNNs) encoder. A multi‐task self‐supervised pretraining strategy is proposed to fully capture the structural and chemical knowledge of molecules. Extensive computational tests on 27 datasets demonstrate that MTSSMol exhibits exceptional performance in predicting molecular properties across different domains. Moreover, MTSSMol's capability is validated to identify potential inhibitors of FGFR1 through molecular docking using RoseTTAFold All‐Atom (RFAA) and molecular dynamics simulations. Overall, MTSSMol provides an effective algorithmic framework for enhancing molecular representation learning and identifying potential drug candidates, offering a valuable tool to accelerate drug discovery processes. All of the codes are freely available online at https:// github.com/zhaoqi106/MTSSMol.https://doi.org/10.1002/advs.202412987FGFR1graph neural networksmolecular propertiesmulti‐task strategypretraining framework |
| spellingShingle | Xin Yang Yang Wang Ye Lin Mingxuan Zhang Ou Liu Jianwei Shuai Qi Zhao A Multi‐Task Self‐Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors Advanced Science FGFR1 graph neural networks molecular properties multi‐task strategy pretraining framework |
| title | A Multi‐Task Self‐Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors |
| title_full | A Multi‐Task Self‐Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors |
| title_fullStr | A Multi‐Task Self‐Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors |
| title_full_unstemmed | A Multi‐Task Self‐Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors |
| title_short | A Multi‐Task Self‐Supervised Strategy for Predicting Molecular Properties and FGFR1 Inhibitors |
| title_sort | multi task self supervised strategy for predicting molecular properties and fgfr1 inhibitors |
| topic | FGFR1 graph neural networks molecular properties multi‐task strategy pretraining framework |
| url | https://doi.org/10.1002/advs.202412987 |
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