Pushing the boundaries of Structure-Based Drug Design through Collaboration with Large Language Models
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912256104595456 |
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| author | Gao, Bowen Huang, Yanwen Liu, Yiqiao Xie, Wenxuan Ma, Wei-Ying Zhang, Ya-Qin Lan, Yanyan |
| author_facet | Gao, Bowen Huang, Yanwen Liu, Yiqiao Xie, Wenxuan Ma, Wei-Ying Zhang, Ya-Qin Lan, Yanyan |
| contents | Structure-Based Drug Design (SBDD) has revolutionized drug discovery by enabling the rational design of molecules for specific protein targets. Despite significant advancements in improving docking scores, advanced 3D-SBDD generative models still face challenges in producing drug-like candidates that meet medicinal chemistry standards and pharmacokinetic requirements. These limitations arise from their inherent focus on molecular interactions, often neglecting critical aspects of drug-likeness. To address these shortcomings, we introduce the Collaborative Intelligence Drug Design (CIDD) framework, which combines the structural precision of 3D-SBDD models with the chemical reasoning capabilities of large language models (LLMs). CIDD begins by generating supporting molecules with 3D-SBDD models and then refines these molecules through LLM-supported modules to enhance drug-likeness and structural reasonability. When evaluated on the CrossDocked2020 dataset, CIDD achieved a remarkable success ratio of 37.94%, significantly outperforming the previous state-of-the-art benchmark of 15.72%. Although improving molecular interactions and drug-likeness is often seen as a trade-off, CIDD uniquely achieves a balanced improvement in both by leveraging the complementary strengths of different models, offering a robust and innovative pathway for designing therapeutically promising drug candidates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01376 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pushing the boundaries of Structure-Based Drug Design through Collaboration with Large Language Models Gao, Bowen Huang, Yanwen Liu, Yiqiao Xie, Wenxuan Ma, Wei-Ying Zhang, Ya-Qin Lan, Yanyan Biomolecules Structure-Based Drug Design (SBDD) has revolutionized drug discovery by enabling the rational design of molecules for specific protein targets. Despite significant advancements in improving docking scores, advanced 3D-SBDD generative models still face challenges in producing drug-like candidates that meet medicinal chemistry standards and pharmacokinetic requirements. These limitations arise from their inherent focus on molecular interactions, often neglecting critical aspects of drug-likeness. To address these shortcomings, we introduce the Collaborative Intelligence Drug Design (CIDD) framework, which combines the structural precision of 3D-SBDD models with the chemical reasoning capabilities of large language models (LLMs). CIDD begins by generating supporting molecules with 3D-SBDD models and then refines these molecules through LLM-supported modules to enhance drug-likeness and structural reasonability. When evaluated on the CrossDocked2020 dataset, CIDD achieved a remarkable success ratio of 37.94%, significantly outperforming the previous state-of-the-art benchmark of 15.72%. Although improving molecular interactions and drug-likeness is often seen as a trade-off, CIDD uniquely achieves a balanced improvement in both by leveraging the complementary strengths of different models, offering a robust and innovative pathway for designing therapeutically promising drug candidates. |
| title | Pushing the boundaries of Structure-Based Drug Design through Collaboration with Large Language Models |
| topic | Biomolecules |
| url | https://arxiv.org/abs/2503.01376 |