Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning
Abstract The stability and therapeutic efficacy of antibody-drug conjugates (ADCs) are critically determined by the chemical linkers that connect the antibody to the cytotoxic payload, which is a key factor influencing drug release, plasma stability, and off-target toxicity. However, the current lin...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-05555-3 |
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| author | An Su Yanlin Luo Chengwei Zhang Hongliang Duan |
| author_facet | An Su Yanlin Luo Chengwei Zhang Hongliang Duan |
| author_sort | An Su |
| collection | DOAJ |
| description | Abstract The stability and therapeutic efficacy of antibody-drug conjugates (ADCs) are critically determined by the chemical linkers that connect the antibody to the cytotoxic payload, which is a key factor influencing drug release, plasma stability, and off-target toxicity. However, the current linker design space remains highly constrained, with most approved ADCs relying on a narrow set of established motifs. This limitation highlights an urgent need for computational tools capable of generating structurally diverse and synthetically accessible linkers. In this study, we introduce Linker-GPT, a Transformer-based deep learning framework leveraging self-attention mechanisms to generate novel ADC linkers with high structural diversity and synthetic feasibility. The model integrates transfer learning from large-scale molecular datasets and reinforcement learning (RL) to iteratively refine molecular properties such as drug-likeness and synthetic accessibility. During transfer learning, a pre-trained model was fine-tuned on a curated linker dataset, yielding molecules with high validity (0.894), novelty (0.997), and uniqueness (0.814 at 1k generation). RL further optimized the model to prioritize synthesizability and drug-like properties, resulting in 98.7% of generated molecules meeting target thresholds for QED (> 0.6), LogP (< 5), and synthetic accessibility score (SAS < 4). Linker-GPT demonstrates strong potential as a computational platform for accelerating the discovery and optimization of novel ADC linkers, offering a scalable solution for early-stage linker design. While these results are currently computational, they provide a foundation for future experimental validation and optimization. |
| format | Article |
| id | doaj-art-00a62bc36d6644cf831363a1f4e7d022 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-00a62bc36d6644cf831363a1f4e7d0222025-08-20T04:01:23ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-05555-3Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learningAn Su0Yanlin Luo1Chengwei Zhang2Hongliang Duan3Zhejiang Key Laboratory of Green Manufacturing Technology for Chemical Drugs, Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Key Laboratory for Green Pharmaceutical Technologies and Related Equipment of Ministry of Education, College of Pharmaceutical Science, Zhejiang University of TechnologyCollege of Chemical Engineering, Zhejiang University of TechnologyCollege of Chemical Engineering, Zhejiang University of TechnologyFaculty of Applied Sciences, Macao Polytechnic UniversityAbstract The stability and therapeutic efficacy of antibody-drug conjugates (ADCs) are critically determined by the chemical linkers that connect the antibody to the cytotoxic payload, which is a key factor influencing drug release, plasma stability, and off-target toxicity. However, the current linker design space remains highly constrained, with most approved ADCs relying on a narrow set of established motifs. This limitation highlights an urgent need for computational tools capable of generating structurally diverse and synthetically accessible linkers. In this study, we introduce Linker-GPT, a Transformer-based deep learning framework leveraging self-attention mechanisms to generate novel ADC linkers with high structural diversity and synthetic feasibility. The model integrates transfer learning from large-scale molecular datasets and reinforcement learning (RL) to iteratively refine molecular properties such as drug-likeness and synthetic accessibility. During transfer learning, a pre-trained model was fine-tuned on a curated linker dataset, yielding molecules with high validity (0.894), novelty (0.997), and uniqueness (0.814 at 1k generation). RL further optimized the model to prioritize synthesizability and drug-like properties, resulting in 98.7% of generated molecules meeting target thresholds for QED (> 0.6), LogP (< 5), and synthetic accessibility score (SAS < 4). Linker-GPT demonstrates strong potential as a computational platform for accelerating the discovery and optimization of novel ADC linkers, offering a scalable solution for early-stage linker design. While these results are currently computational, they provide a foundation for future experimental validation and optimization.https://doi.org/10.1038/s41598-025-05555-3Machine learningTransfer learningAntibody-drug conjugatesLinker designReinforcement learningMolecule generation |
| spellingShingle | An Su Yanlin Luo Chengwei Zhang Hongliang Duan Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning Scientific Reports Machine learning Transfer learning Antibody-drug conjugates Linker design Reinforcement learning Molecule generation |
| title | Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning |
| title_full | Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning |
| title_fullStr | Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning |
| title_full_unstemmed | Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning |
| title_short | Linker-GPT: design of Antibody-drug conjugates linkers with molecular generators and reinforcement learning |
| title_sort | linker gpt design of antibody drug conjugates linkers with molecular generators and reinforcement learning |
| topic | Machine learning Transfer learning Antibody-drug conjugates Linker design Reinforcement learning Molecule generation |
| url | https://doi.org/10.1038/s41598-025-05555-3 |
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