融合因果关系的多模态药物设计框架，提升分子生成合理性
TransDiffSBDD: Causality-Aware Multi-Modal Structure-Based Drug Design
- 用自回归变压器与扩散模型联合生成分子图和3D坐标
- 在CrossDocked2020上优于现有方法，提升生成可信度
- 适合药物发现中需兼顾结构与功能的场景
基于结构的药物设计（SBDD）是药物发现中的关键任务，需在离散分子图和连续3D坐标两种模态间生成分子信息。然而，现有方法常忽视该任务的多模态特性及模态间的因果关系，限制了生成结果的合理性与性能。为此，我们提出TransDiffSBDD，一个结合自回归变压器与扩散模型的集成框架。自回归变压器建模离散分子信息，扩散模型采样连续分布，有效解决多模态挑战；同时，我们设计了混合模态序列，显式保留蛋白-配体复合物中模态间的因果关系。在CrossDocked2020基准上的实验表明，TransDiffSBDD优于现有基线方法。
原文摘要 · Abstract (English)
Structure-based drug design (SBDD) is a critical task in drug discovery, requiring the generation of molecular information across two distinct modalities: discrete molecular graphs and continuous 3D coordinates. However, existing SBDD methods often overlook two key challenges: (1) the multi-modal nature of this task and (2) the causal relationship between these modalities, limiting their plausibility and performance. To address both challenges, we propose TransDiffSBDD, an integrated framework combining autoregressive transformers and diffusion models for SBDD. Specifically, the autoregressive transformer models discrete molecular information, while the diffusion model samples continuous distributions, effectively resolving the first challenge. To address the second challenge, we design a hybrid-modal sequence for protein-ligand complexes that explicitly respects the causality between modalities. Experiments on the CrossDocked2020 benchmark demonstrate that TransDiffSBDD outperforms existing baselines.
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