用扩散模型零样本设计同时作用于两个靶点的药物。
Reprogramming Pretrained Target-Specific Diffusion Models for Dual-Target Drug Design
- 将单靶点预训练扩散模型重用于双靶点药物生成，通过3D结构对齐与共享配体图建模。
- 在多个靶点组合上生成药物分子，其结合亲和力显著优于基线方法。
- 适合药物研发人员快速探索协同治疗新药，尤其关注癌症耐药性问题。
双靶点治疗策略因在克服癌症耐药性等方面的潜力而备受关注。近年来，深度生成模型在基于结构的药物设计中取得显著进展。本文将双靶点药物设计视为生成任务，并基于协同药物组合构建了一个新的潜在靶点对数据集。提出使用在单靶点蛋白-配体复合物对上训练的扩散模型来设计双靶点药物。具体地，通过在三维空间对齐两个结合口袋，并利用蛋白-配体结合先验信息构建共享配体节点的双复合图，实现SE(3)等变的组合消息传递。在此基础上，推导出生成过程中的三维空间与类别概率空间联合漂移项。该方法可零样本地将单靶点预训练知识迁移至双靶点场景。同时，将连接子设计方法作为强基线进行对比。大量实验表明，本方法在多种基线中表现更优。
原文摘要 · Abstract (English)
Dual-target therapeutic strategies have become a compelling approach and attracted significant attention due to various benefits, such as their potential in overcoming drug resistance in cancer therapy. Considering the tremendous success that deep generative models have achieved in structure-based drug design in recent years, we formulate dual-target drug design as a generative task and curate a novel dataset of potential target pairs based on synergistic drug combinations. We propose to design dual-target drugs with diffusion models that are trained on single-target protein-ligand complex pairs. Specifically, we align two pockets in 3D space with protein-ligand binding priors and build two complex graphs with shared ligand nodes for SE(3)-equivariant composed message passing, based on which we derive a composed drift in both 3D and categorical probability space in the generative process. Our algorithm can well transfer the knowledge gained in single-target pretraining to dual-target scenarios in a zero-shot manner. We also repurpose linker design methods as strong baselines for this task. Extensive experiments demonstrate the effectiveness of our method compared with various baselines.
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