Sequence-based virtual screening using transformers
Abstract Protein-ligand interactions play central roles in myriad biological processes and are of key importance in drug design. Deep learning approaches are becoming cost-effective alternatives to high-throughput experimental methods for ligand identification. Here, to predict the binding affinity...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61833-8 |
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| author | Shengyu Zhang Donghui Huo Robert I. Horne Yumeng Qi Sebastian Pujalte Ojeda Aixia Yan Michele Vendruscolo |
| author_facet | Shengyu Zhang Donghui Huo Robert I. Horne Yumeng Qi Sebastian Pujalte Ojeda Aixia Yan Michele Vendruscolo |
| author_sort | Shengyu Zhang |
| collection | DOAJ |
| description | Abstract Protein-ligand interactions play central roles in myriad biological processes and are of key importance in drug design. Deep learning approaches are becoming cost-effective alternatives to high-throughput experimental methods for ligand identification. Here, to predict the binding affinity between proteins and small molecules, we introduce Ligand-Transformer, a deep learning method based on the transformer architecture. Ligand-Transformer implements a sequence-based approach, where the inputs are the amino acid sequence of the target protein and the topology of the small molecule to enable the prediction of the conformational space explored by the complex between the two. We apply Ligand-Transformer to screen and validate experimentally inhibitors targeting the mutant EGFRLTC kinase, identifying compounds with low nanomolar potency. We then use this approach to predict the conformational population shifts induced by known ABL kinase inhibitors, showing that sequence-based predictions enable the characterisation of the population shift upon binding. Overall, our results illustrate the potential of Ligand-Transformer to accurately predict the interactions of small molecules with proteins, including the binding affinity and the changes in the free energy landscapes upon binding, thus uncovering molecular mechanisms and facilitating the initial steps in drug design. |
| format | Article |
| id | doaj-art-eb02de2104b14254af2d596b5830c635 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-eb02de2104b14254af2d596b5830c6352025-08-20T04:02:54ZengNature PortfolioNature Communications2041-17232025-07-0116111210.1038/s41467-025-61833-8Sequence-based virtual screening using transformersShengyu Zhang0Donghui Huo1Robert I. Horne2Yumeng Qi3Sebastian Pujalte Ojeda4Aixia Yan5Michele Vendruscolo6Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCentre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCentre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCentre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCentre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeCollege of Life Science and Technology, Beijing University of Chemical TechnologyCentre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of CambridgeAbstract Protein-ligand interactions play central roles in myriad biological processes and are of key importance in drug design. Deep learning approaches are becoming cost-effective alternatives to high-throughput experimental methods for ligand identification. Here, to predict the binding affinity between proteins and small molecules, we introduce Ligand-Transformer, a deep learning method based on the transformer architecture. Ligand-Transformer implements a sequence-based approach, where the inputs are the amino acid sequence of the target protein and the topology of the small molecule to enable the prediction of the conformational space explored by the complex between the two. We apply Ligand-Transformer to screen and validate experimentally inhibitors targeting the mutant EGFRLTC kinase, identifying compounds with low nanomolar potency. We then use this approach to predict the conformational population shifts induced by known ABL kinase inhibitors, showing that sequence-based predictions enable the characterisation of the population shift upon binding. Overall, our results illustrate the potential of Ligand-Transformer to accurately predict the interactions of small molecules with proteins, including the binding affinity and the changes in the free energy landscapes upon binding, thus uncovering molecular mechanisms and facilitating the initial steps in drug design.https://doi.org/10.1038/s41467-025-61833-8 |
| spellingShingle | Shengyu Zhang Donghui Huo Robert I. Horne Yumeng Qi Sebastian Pujalte Ojeda Aixia Yan Michele Vendruscolo Sequence-based virtual screening using transformers Nature Communications |
| title | Sequence-based virtual screening using transformers |
| title_full | Sequence-based virtual screening using transformers |
| title_fullStr | Sequence-based virtual screening using transformers |
| title_full_unstemmed | Sequence-based virtual screening using transformers |
| title_short | Sequence-based virtual screening using transformers |
| title_sort | sequence based virtual screening using transformers |
| url | https://doi.org/10.1038/s41467-025-61833-8 |
| work_keys_str_mv | AT shengyuzhang sequencebasedvirtualscreeningusingtransformers AT donghuihuo sequencebasedvirtualscreeningusingtransformers AT robertihorne sequencebasedvirtualscreeningusingtransformers AT yumengqi sequencebasedvirtualscreeningusingtransformers AT sebastianpujalteojeda sequencebasedvirtualscreeningusingtransformers AT aixiayan sequencebasedvirtualscreeningusingtransformers AT michelevendruscolo sequencebasedvirtualscreeningusingtransformers |