A Novel 4-D Dataset Paradigm for Studying Complete Ligand-Protein Dissociation Dynamics
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908833925824512 |
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| author | Li, Maodong Zhang, Jiying Wang, Zhe Feng, Bin Zeng, Wenqi Chen, Dechin Pan, Zhijun Li, Yu Liu, Zijing Yang, Yi Isaac |
| author_facet | Li, Maodong Zhang, Jiying Wang, Zhe Feng, Bin Zeng, Wenqi Chen, Dechin Pan, Zhijun Li, Yu Liu, Zijing Yang, Yi Isaac |
| contents | The kinetics and dynamics of drug-protein binding and dissociation are crucial to understanding drug absorption and metabolism. Despite advances in artificial intelligence (AI) tools for drug-protein interaction studies, existing training datasets remain limited to static structures or quasi-static conformations. This paper proposes a novel computational approach for rapidly generating drug-protein dissociation trajectories and presents the inaugural dynamically time-resolved 4-D (t, x, y, z) trajectory database DD-13M. This dataset captures over 26,000 complete dissociation processes for 565 ligand-protein complexes, providing nearly 13 million frames of all-atom simulation trajectories. A deep equivariant generative model, UnbindingFlow, was trained using the DD-13M dataset. This model has the capacity to produce dissociation trajectories for novel targets whilst accurately predicting their rate constants (koff). DD-13M introduces a new type of training dataset for AI models, establishing a de novo paradigm for studying the dynamics of drug-protein interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Novel 4-D Dataset Paradigm for Studying Complete Ligand-Protein Dissociation Dynamics Li, Maodong Zhang, Jiying Wang, Zhe Feng, Bin Zeng, Wenqi Chen, Dechin Pan, Zhijun Li, Yu Liu, Zijing Yang, Yi Isaac Computational Physics Machine Learning Chemical Physics Biomolecules The kinetics and dynamics of drug-protein binding and dissociation are crucial to understanding drug absorption and metabolism. Despite advances in artificial intelligence (AI) tools for drug-protein interaction studies, existing training datasets remain limited to static structures or quasi-static conformations. This paper proposes a novel computational approach for rapidly generating drug-protein dissociation trajectories and presents the inaugural dynamically time-resolved 4-D (t, x, y, z) trajectory database DD-13M. This dataset captures over 26,000 complete dissociation processes for 565 ligand-protein complexes, providing nearly 13 million frames of all-atom simulation trajectories. A deep equivariant generative model, UnbindingFlow, was trained using the DD-13M dataset. This model has the capacity to produce dissociation trajectories for novel targets whilst accurately predicting their rate constants (koff). DD-13M introduces a new type of training dataset for AI models, establishing a de novo paradigm for studying the dynamics of drug-protein interactions. |
| title | A Novel 4-D Dataset Paradigm for Studying Complete Ligand-Protein Dissociation Dynamics |
| topic | Computational Physics Machine Learning Chemical Physics Biomolecules |
| url | https://arxiv.org/abs/2504.18367 |