Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion

• URL: http://arxiv.org/abs/2506.14488v1
• Date: Tue, 17 Jun 2025 13:09:11 GMT
• Title: Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion
• Authors: Dong Xu, Zhangfan Yang, Ka-chun Wong, Zexuan Zhu, Jiangqiang Li, Junkai Ji,
• Abstract summary: READ is introduced, which is the first to merge molecular Retrieval-Augmented Generation with an SE(3)-equivariant diffusion model.<n>It can achieve very competitive performance in CBGBench, surpassing state-of-the-art generative models and even native scaffolds.
• Score: 22.204642926984526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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