AI 中文总结
研究针对多因素疾病治疗中传统药物设计的不足，将几何深度学习作为有力方法，综述其架构、应用于三个核心维度的情况，强调新兴算法，评估相关要素作用，阐明药物发现从偶然探索到理性、结构驱动的多药理学分子工程的范式转变。
AI 中文摘要
传统基于结构的药物设计（SBDD）的“一药一靶”模式在治疗癌症和神经退行性疾病等多因素疾病时往往不足。多药理学提供了协同治疗策略，但合理设计能同时满足多个靶点几何约束的配体仍是计算瓶颈。本文将几何深度学习（GDL）定位为克服这些限制的有力综合方法，系统综述了相关架构，分析其在三个核心维度的应用，强调新兴算法，评估多组学整合和几何基准测试基础设施的作用，阐明药物发现范式转变。
英文摘要
The traditional "one drug, one target" paradigm of structure-based drug design (SBDD) frequently proves inadequate for treating multifactorial diseases such as cancer and neurodegenerative disorders, owing to compensatory signaling pathways and the emergence of drug resistance. While polypharmacology offers a synergistic therapeutic strategy, the rational design of ligands capable of simultaneously satisfying the geometric constraints imposed by multiple targets remains a major computational bottleneck. This review positions geometric deep learning (GDL) as a powerful integrative approach to overcome these limitations. We systematically survey GDL architectures ranging from invariant graph neural networks to SE(3)-equivariant diffusion models that harness non-Euclidean molecular data to capture intrinsic three-dimensional (3D) structural interdependencies. We critically analyze GDL applications across three core dimensions, including the characterization of shared binding pockets via geometric embeddings, multi-target bioactivity prediction through heterogeneous graph fusion, and de novo generation of dual-target ligands. Particular emphasis is placed on emerging structure-conditioned generative algorithms that integrate diffusion models with reinforcement learning to autonomously resolve complex geometric conflicts between competing binding sites. Furthermore, we evaluate the pivotal role of multimodal omics integration and specialized geometric benchmarking infrastructures in validating these models. By synthesizing these methodological advances, this review elucidates the paradigm shift in drug discovery from serendipitous exploration to rational, structure-driven polypharmacological molecular engineering, thereby providing a clear, structured guide for navigating the complexities of next-generation therapeutics.
