Identifying molecules from mass spectrometry (MS) data remains a fundamental challenge due to the semantic gap between physical spectral peaks and underlying chemical structures. Existing deep learning approaches often treat spectral matching as a closed-set recognition task, limiting their ability to generalize to unseen molecular scaffolds. To overcome this limitation, we propose a cross-modal alignment framework that directly maps mass spectra into the chemically meaningful molecular structure embedding space of a pretrained chemical language model. On a strict scaffold-disjoint benchmark, our model achieves a Top-1 accuracy of 42.2% in fixed 256-way zero-shot retrieval and demonstrates strong generalization under a global retrieval setting. Moreover, the learned embedding space demonstrates strong chemical coherence, reaching 95.4% accuracy in 5-way 5-shot molecular re-identification. These results suggest that explicitly integrating physical spectral resolution with molecular structure embedding is key to solving the generalization bottleneck in molecular identification from MS data.
Paul G. Allen School of Computer Science and Engineering, University of Washington（保罗·G·阿伦计算机科学与工程学院，华盛顿大学）；Department of Chemistry, University of Washington（华盛顿大学化学系）；Adrem Data Lab, University of Antwerp（阿德雷姆数据实验室，安特卫普大学）；Talus Bioscience（塔卢斯生物科学）；Department of Genome Sciences, University of Washington（华盛顿大学基因组科学系）
S\ ao Carlos Institute of Chemistry, University of S\ ao Paulo, S\ ao Carlos, SP, Brazil；S\ ao Carlos Institute of Physics, University of S\ ao Paulo, S\ ao Carlos, SP, Brazil
The understanding of how science works can contribute to making scientific development more effective. In this paper, we report an analysis of the organization and interconnection between two important issues in chemistry, namely mass spectrometry (MS) and capillary electrophoresis (CE). For that purpose, we employed science of science techniques based on complex networks. More specifically, we considered a citation network in which the nodes and connections represent papers and citations, respectively. Interesting results were found, including a good separation between some clusters of articl…
Causal Foundation Models: Disentangling Physics from Instrument Properties
Massachusetts Institute of Technology（麻省理工学院）；Harvard University（哈佛大学）；New York University（纽约大学）；Flatiron Institute（Flatiron研究所）；The NSF AI Institute for Artificial Intelligence and Fundamental Interactions（NSF人工智能与基本相互作用研究所）
Joint Masked Reconstruction and Contrastive Learning for Mining Interactions Between Proteins
School of Artificial Intelligence and Automation, Huazhong University of Science and Technology（华中科技大学人工智能与自动化学院）；Institute of Artificial Intelligence, Huazhong University of Science and Technology（华中科技大学人工智能研究院）；Hubei Key Laboratory of Brain-Inspired Intelligent Systems, Huazhong University of Science and Technology（华中科技大学湖北省类脑智能系统重点实验室）；Key Laboratory of Image Processing and Intelligent Control (Huazhong University of Science and Technology), Ministry of Education（华中科技大学图像信息处理与智能控制教育部重点实验室）
SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception
Dept. of Intelligent Systems, KTH Royal Institute of Technology（智能系统系，皇家理工学院）；Atmospheric Chemistry Dept., Max Planck Institute for Chemistry（大气化学部，马克斯·普朗克研究所）；Dept. of Information Technology, Uppsala University（信息科技系，乌普萨拉大学）；Science for Life Laboratory (SciLifeLab), Uppsala（生命科学实验室（SciLifeLab），乌普萨拉）
Self-supervised audio representation learning offers an attractive alternative for obtaining generic audio embeddings, capable to be employed into various downstream tasks. Published approaches that consider both audio and words/tags associated with audio do not employ text processing models that are capable to generalize to tags unknown during training. In this work we propose a method for learning audio representations using an audio autoencoder (AAE), a general word embeddings model (WEM), and a multi-head self-attention (MHA) mechanism. MHA attends on the output of the WEM, providing a con…
