MuCoS通过多上下文采样提升药物靶点预测效率与准确率。
MuCoS: Efficient Drug-Target Prediction through Multi-Context-Aware Sampling
- 基于高密度邻域优先采样，降低计算复杂度。
- 在KEGG50k数据集上，各项指标最高提升18%。
- 无需负样本采样，适合长尾关系预测任务。
药物-靶点相互作用对理解生物过程和推进药物发现至关重要。然而，传统方法如ComplEx-SE、TransE和DistMult在处理未见关系和负三元组时表现不佳，限制了其在药物-靶点预测中的有效性。为此，我们提出多上下文感知采样（MuCoS），一种高效且正向准确的药物-靶点预测方法。MuCoS通过优先选择高密度邻域来降低计算复杂度，捕捉有信息量的结构模式。这些优化后的邻域表示与BERT结合，生成上下文感知嵌入，以准确预测缺失关系或尾部实体。MuCoS避免了负三元组采样的需求，在减少计算量的同时提升了对未见实体和关系的性能。在KEGG50k生物医学数据集上的实验表明，MuCoS在一般关系预测中相比现有模型，MRR提升13%，Hits@1提升7%，Hits@3提升4%，Hits@10提升18%；在药物-靶点关系预测中，MRR提升6%，Hits@1提升1%，Hits@3提升3%，Hits@10提升12%。
原文摘要 · Abstract (English)
Drug-target interactions are critical for understanding biological processes and advancing drug discovery. However, traditional methods such as ComplEx-SE, TransE, and DistMult struggle with unseen relationships and negative triplets, which limits their effectiveness in drug-target prediction. To address these challenges, we propose Multi-Context-Aware Sampling (MuCoS), an efficient and positively accurate method for drug-target prediction. MuCoS reduces computational complexity by prioritizing neighbors of higher density to capture informative structural patterns. These optimized neighborhood representations are integrated with BERT, enabling contextualized embeddings for accurate prediction of missing relationships or tail entities. MuCoS avoids the need for negative triplet sampling, reducing computation while improving performance over unseen entities and relations. Experiments on the KEGG50k biomedical dataset show that MuCoS improved over existing models by 13\% on MRR, 7\% on Hits@1, 4\% on Hits@3, and 18\% on Hits@10 for the general relationship, and by 6\% on MRR, 1\% on Hits@1, 3\% on Hits@3, and 12\% on Hits@10 for prediction of drug-target relationship.
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