Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity
Abstract Background The binding between proteins and ligands plays a crucial role in the field of drug discovery. However, this area currently faces numerous challenges. On one hand, existing methods are constrained by the limited availability of labeled data, often performing inadequately when addr...
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2025-02-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06064-w |
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| author | Jiashan Li Xinqi Gong |
| author_facet | Jiashan Li Xinqi Gong |
| author_sort | Jiashan Li |
| collection | DOAJ |
| description | Abstract Background The binding between proteins and ligands plays a crucial role in the field of drug discovery. However, this area currently faces numerous challenges. On one hand, existing methods are constrained by the limited availability of labeled data, often performing inadequately when addressing complex protein-ligand interactions. On the other hand, many models struggle to effectively capture the flexible variations and relative spatial relationships between proteins and ligands. These issues not only significantly hinder the advancement of protein-ligand binding research but also adversely affect the accuracy and efficiency of drug discovery. Therefore, in response to these challenges, our study aims to enhance predictive capabilities through innovative approaches, providing more reliable support for drug discovery efforts. Methods This study leverages a pre-trained model with spatial awareness to enhance the prediction of protein-ligand binding affinity. By perturbing the structures of small molecules in a manner consistent with physical constraints and employing self-supervised tasks, we improve the representation of small molecule structures, allowing for better adaptation to affinity predictions. Meanwhile, our approach enables the identification of potential binding sites on proteins. Results Our model demonstrates a significantly higher correlation coefficient in binding affinity predictions. Extensive evaluation on the PDBBind v2019 refined set, CASF, and Merck FEP benchmarks confirms the model’s robustness and strong generalization across diverse datasets. Additionally, the model achieves over 95% in classification ROC for binding site identification, underscoring its high accuracy in pinpointing protein-ligand interaction regions. Conclusion This research presents a novel approach that not only enhances the accuracy of binding affinity predictions but also facilitates the identification of binding sites, showcasing the potential of pre-trained models in computational drug design. Data and code are available at https://github.com/MIALAB-RUC/SableBind . |
| format | Article |
| id | doaj-art-49e291bd804e4bab8af84b81eb51f33f |
| institution | DOAJ |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-49e291bd804e4bab8af84b81eb51f33f2025-08-20T03:10:50ZengBMCBMC Bioinformatics1471-21052025-02-0126112110.1186/s12859-025-06064-wHarnessing pre-trained models for accurate prediction of protein-ligand binding affinityJiashan Li0Xinqi Gong1Institute for Mathematical Sciences, School of Mathematics, Renmin University of ChinaInstitute for Mathematical Sciences, School of Mathematics, Renmin University of ChinaAbstract Background The binding between proteins and ligands plays a crucial role in the field of drug discovery. However, this area currently faces numerous challenges. On one hand, existing methods are constrained by the limited availability of labeled data, often performing inadequately when addressing complex protein-ligand interactions. On the other hand, many models struggle to effectively capture the flexible variations and relative spatial relationships between proteins and ligands. These issues not only significantly hinder the advancement of protein-ligand binding research but also adversely affect the accuracy and efficiency of drug discovery. Therefore, in response to these challenges, our study aims to enhance predictive capabilities through innovative approaches, providing more reliable support for drug discovery efforts. Methods This study leverages a pre-trained model with spatial awareness to enhance the prediction of protein-ligand binding affinity. By perturbing the structures of small molecules in a manner consistent with physical constraints and employing self-supervised tasks, we improve the representation of small molecule structures, allowing for better adaptation to affinity predictions. Meanwhile, our approach enables the identification of potential binding sites on proteins. Results Our model demonstrates a significantly higher correlation coefficient in binding affinity predictions. Extensive evaluation on the PDBBind v2019 refined set, CASF, and Merck FEP benchmarks confirms the model’s robustness and strong generalization across diverse datasets. Additionally, the model achieves over 95% in classification ROC for binding site identification, underscoring its high accuracy in pinpointing protein-ligand interaction regions. Conclusion This research presents a novel approach that not only enhances the accuracy of binding affinity predictions but also facilitates the identification of binding sites, showcasing the potential of pre-trained models in computational drug design. Data and code are available at https://github.com/MIALAB-RUC/SableBind .https://doi.org/10.1186/s12859-025-06064-wBinding affinityBinding site predictionMolecular representationMolecular pre-training |
| spellingShingle | Jiashan Li Xinqi Gong Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity BMC Bioinformatics Binding affinity Binding site prediction Molecular representation Molecular pre-training |
| title | Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity |
| title_full | Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity |
| title_fullStr | Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity |
| title_full_unstemmed | Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity |
| title_short | Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity |
| title_sort | harnessing pre trained models for accurate prediction of protein ligand binding affinity |
| topic | Binding affinity Binding site prediction Molecular representation Molecular pre-training |
| url | https://doi.org/10.1186/s12859-025-06064-w |
| work_keys_str_mv | AT jiashanli harnessingpretrainedmodelsforaccuratepredictionofproteinligandbindingaffinity AT xinqigong harnessingpretrainedmodelsforaccuratepredictionofproteinligandbindingaffinity |