Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion
This addresses the problem of faster and more reliable structure-based drug design for early-phase drug discovery, representing a novel method rather than an incremental improvement.
The paper tackled the challenge of balancing pocket-specific geometric fit with chemical constraints in receptor-based molecule design by introducing READ, a Retrieval-Enhanced Aligned Diffusion model, which achieved very competitive performance in CBGBench, surpassing state-of-the-art generative models and native ligands.
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.