用生物医学知识图谱增强生成模型，提升靶向药物设计效果
Augmenting generative models with biomedical knowledge graphs improves targeted drug discovery
- 将知识图谱嵌入扩散生成模型，引导分子生成更符合治疗目标
- 生成分子的结合亲和力与预测疗效优于现有顶尖模型
- 可针对多个靶点设计分子，适合复杂疾病药物研发
生成模型在分子生成方面取得显著进展，但如何整合全面的生物医学知识仍是未解难题。本文提出K-DREAM（Knowledge-Driven Embedding-Augmented Model）框架，利用大规模知识图谱嵌入结构化信息，增强基于扩散的生成模型用于药物发现。该方法使生成分子更贴近特定治疗靶点，突破传统基于经验的筛选方式。在靶向药物设计任务中，K-DREAM生成的候选分子表现出更高结合亲和力和预测疗效，超越当前最先进生成模型。其灵活性还体现在可针对多个靶点生成分子，适用于复杂疾病机制研究。结果表明，知识增强型生成模型在理性药物设计中具有重要价值，对实际治疗开发有直接意义。
原文摘要 · Abstract (English)
Recent breakthroughs in generative modeling have demonstrated remarkable capabilities in molecular generation, yet the integration of comprehensive biomedical knowledge into these models has remained an untapped frontier. In this study, we introduce K-DREAM (Knowledge-Driven Embedding-Augmented Model), a novel framework that leverages knowledge graphs to augment diffusion-based generative models for drug discovery. By embedding structured information from large-scale knowledge graphs, K-DREAM directs molecular generation toward candidates with higher biological relevance and therapeutic suitability. This integration ensures that the generated molecules are aligned with specific therapeutic targets, moving beyond traditional heuristic-driven approaches. In targeted drug design tasks, K-DREAM generates drug candidates with improved binding affinities and predicted efficacy, surpassing current state-of-the-art generative models. It also demonstrates flexibility by producing molecules designed for multiple targets, enabling applications to complex disease mechanisms. These results highlight the utility of knowledge-enhanced generative models in rational drug design and their relevance to practical therapeutic development.
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