A feature-aligned diffusion model for controllable generation of 3D drug-like molecules

Abstract

Structure-based drug design has increasingly leveraged deep learning approaches, particularly diffusion models. Nevertheless, their practical application is hindered by noise-induced deterioration of biological relevance and binding affinity. We propose a dynamic, feature-aligned diffusion framework guided by expert knowledge. The core innovation lies in a feature fusion mechanism that extracts molecular features in real time through a pre-trained expert network and aligns them cross-modally with the latent space of the diffusion trajectory. Additionally, we introduce a dynamic weight adjustment module, which adaptively adjusts the strength of the feature alignment constraint based on the noise intensity, enabling a progressive optimization from coarse- to fine-grained structures. Experimental results demonstrate that our model achieves an average Vina docking score of −10.06, together with favorable binding free energies. Moreover, it reduces structural clashes and improves drug-likeness with respect to bonding and geometry. This work presents a new paradigm that synergistically integrates diffusion models with autoregressive mechanisms and holds strong potential for advancing AI-driven drug discovery.