模仿科学家推理方式，用少量数据预测新药靶相互作用。
Inductive-Associative Meta-learning Pipeline with Human Cognitive Patterns for Unseen Drug-Target Interaction Prediction
- 基于人类认知流程设计归纳-关联框架，从弱相关参考中提取规律。
- 在未见蛋白上表现超越现有模型，仅用同源蛋白数据即有效。
- 适合药物研发初期的虚拟筛选，尤其适用于数据稀缺场景。
现有药物-靶点相互作用（DTI）模型因蛋白质结构差异难以泛化，多依赖预训练结合原理或详细标注。BioBridge提出一种受科学家工作流程启发的归纳-关联式预测管道：利用多层级编码器与对抗训练，从有限序列数据中积累可迁移的结合原则；在此基础上，采用动态原型元学习框架，关联弱相关注释中的隐含信息，实现对未见药靶对的鲁棒预测。大量实验表明，该方法在未见蛋白质上显著优于现有模型。特别地，仅使用同源蛋白结合数据时，便成功用于表皮生长因子受体和腺苷受体的虚拟筛选，展现出在药物发现中的应用潜力。
原文摘要 · Abstract (English)
Significant differences in protein structures hinder the generalization of existing drug-target interaction (DTI) models, which often rely heavily on pre-learned binding principles or detailed annotations. In contrast, BioBridge designs an Inductive-Associative pipeline inspired by the workflow of scientists who base their accumulated expertise on drawing insights into novel drug-target pairs from weakly related references. BioBridge predicts novel drug-target interactions using limited sequence data, incorporating multi-level encoders with adversarial training to accumulate transferable binding principles. On these principles basis, BioBridge employs a dynamic prototype meta-learning framework to associate insights from weakly related annotations, enabling robust predictions for previously unseen drug-target pairs. Extensive experiments demonstrate that BioBridge surpasses existing models, especially for unseen proteins. Notably, when only homologous protein binding data is available, BioBridge proves effective for virtual screening of the epidermal growth factor receptor and adenosine receptor, underscoring its potential in drug discovery.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。