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

Hao Lu; Zhiqiang Wei; Xiancong Hou; Wenzheng Han; Yang Zhang; Hao Liu

doi:10.1039/d5dd00440c

January 1, 2026Digital DiscoveryOpen Access

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

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Key Points

The aim is to develop a framework that uses a diffusion model for generating 3D drug-like molecules with better docking affinity.
Integrated molecular representations with a diffusion model
Generated drug-like molecules
Evaluated docking affinity of the compounds
Compounds exhibited improved docking affinity compared to previous methods
Enhanced exploration of potential drug candidates

Implication

A framework integrates molecular representations with a diffusion model to enhance compound generation, suggesting improved drug design strategies.

Abstract

We introduce a molecule-design framework that integrates molecular representations with a diffusion model to generate compounds exhibiting improved docking affinity, enabling more effective exploration of structure-based drug candidates.

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Cite This Study

Lu et al. (2026) studied this question.

synapsesocial.com/papers/699fe44895ddcd3a253e86e2 https://doi.org/10.1039/d5dd00440c

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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