发表机构
The Hong Kong University of Science and Technology (Guangzhou); The University of Hong Kong; South China University of Technology; Hong Kong Polytechnic University; Jinan University; The Hong Kong University of Science and Technology（香港科技大学（广州）; 香港大学; 华南理工大学; 香港理工大学; 暨南大学; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SPARC框架，通过检索参考谱图在测试时特化预训练质谱预测器，无需访问测试谱图，在多个数据集上提升跨领域预测性能。
AI 中文摘要
串联质谱预测支持代谢组学、天然产物发现和环境分析中的化合物鉴定。然而，预训练预测器在化学空间和采集条件变化下性能常会下降，而从头重新训练领域特定模型成本高昂。我们提出SPARC，一种检索引导的测试时特化框架，利用谱图参考库在不访问测试查询谱图的情况下调整预训练预测器。对于每个目标查询，SPARC检索化学相关的参考谱图，以在学习的碎片化空间内重新校准碎片强度。在迁移过程中，SPARC将参考引导的谱图适应与可靠性感知一致性相结合，利用检索谱图上的重建行为在持续特化期间选择性地保留可信预测。在MassSpecGym、NPLIB1和应用特定的GNPS库上，SPARC在多种迁移设置下改善了谱图预测。这些结果确立了检索引导的测试时特化作为将预训练MS/MS预测器扩展到特定化学和采集领域的实用策略，持续测试时训练在部署期间提供进一步细化。
英文摘要
Tandem mass spectrum prediction supports compound identification across metabolomics, natural-product discovery, and environmental analysis. However, pretrained predictors often degrade under shifts in chemical space and acquisition conditions, while retraining domain-specific models from scratch is costly. We introduce SPARC, a retrieval-guided test-time specialization framework that adapts a pretrained predictor using a spectral reference library without accessing test-query spectra. For each target query, SPARC retrieves chemically related reference spectra to recalibrate fragment intensities within the learned fragmentation space. During Transfer, SPARC combines reference-guided spectral adaptation with reliability-aware consistency, using reconstruction behavior on retrieved spectra to selectively preserve trustworthy predictions during continual specialization. Across MassSpecGym, NPLIB1 and application-specific GNPS libraries, SPARC improves spectral prediction under multiple transfer settings. These results establish retrieval-guided test-time specialization as a practical strategy for extending pretrained MS/MS predictors to specific chemical and acquisition domains, with continual test-time training providing further refinement during deployment.
