Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916796904243200 |
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| author | Xu, Dong Yang, Zhangfan Wong, Ka-chun Zhu, Zexuan Li, Jiangqiang Ji, Junkai |
| author_facet | Xu, Dong Yang, Zhangfan Wong, Ka-chun Zhu, Zexuan Li, Jiangqiang Ji, Junkai |
| contents | Breakthroughs in high-accuracy protein structure prediction, such as AlphaFold, have established receptor-based molecule design as a critical driver for rapid early-phase drug discovery. However, most approaches still struggle to balance pocket-specific geometric fit with strict valence and synthetic constraints. To resolve this trade-off, a Retrieval-Enhanced Aligned Diffusion termed READ is introduced, which is the first to merge molecular Retrieval-Augmented Generation with an SE(3)-equivariant diffusion model. Specifically, a contrastively pre-trained encoder aligns atom-level representations during training, then retrieves graph embeddings of pocket-matched scaffolds to guide each reverse-diffusion step at inference. This single mechanism can inject real-world chemical priors exactly where needed, producing valid, diverse, and shape-complementary ligands. Experimental results demonstrate that READ can achieve very competitive performance in CBGBench, surpassing state-of-the-art generative models and even native ligands. That suggests retrieval and diffusion can be co-optimized for faster, more reliable structure-based drug design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14488 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion Xu, Dong Yang, Zhangfan Wong, Ka-chun Zhu, Zexuan Li, Jiangqiang Ji, Junkai Biomolecules Machine Learning Breakthroughs in high-accuracy protein structure prediction, such as AlphaFold, have established receptor-based molecule design as a critical driver for rapid early-phase drug discovery. However, most approaches still struggle to balance pocket-specific geometric fit with strict valence and synthetic constraints. To resolve this trade-off, a Retrieval-Enhanced Aligned Diffusion termed READ is introduced, which is the first to merge molecular Retrieval-Augmented Generation with an SE(3)-equivariant diffusion model. Specifically, a contrastively pre-trained encoder aligns atom-level representations during training, then retrieves graph embeddings of pocket-matched scaffolds to guide each reverse-diffusion step at inference. This single mechanism can inject real-world chemical priors exactly where needed, producing valid, diverse, and shape-complementary ligands. Experimental results demonstrate that READ can achieve very competitive performance in CBGBench, surpassing state-of-the-art generative models and even native ligands. That suggests retrieval and diffusion can be co-optimized for faster, more reliable structure-based drug design. |
| title | Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2506.14488 |