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English(EN) SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception

新AI模型从质谱预测气味感知

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了SCENT，一个新颖的多模态对比学习框架，旨在直接从质谱数据预测人类嗅觉感知。该方法将电子电离质谱（EI-MS）表示与化学结构嵌入对齐，在推理过程中无需明确的分子结构。SCENT在气味描述符预测方面表现强劲，优于现有的仅质谱方法，并可与基于结构的模型相媲美，同时在近似连续的人类感知评分和泛化到真实世界光谱方面也显示出潜力。 AI

影响 这项研究可能在分子结构不可用的情况下实现AI驱动的嗅觉预测，推动化学传感和感知领域的应用。

排序理由 该集群包含一篇详细介绍新AI模型和框架的学术论文。

在 arXiv cs.LG 阅读 →

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新AI模型从质谱预测气味感知

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ziqi Zhang, Eunyeong Jin, Miguel Vasco, Farzaneh Taleb, Nona Rajabi, Alexandra Gutmann, Jonathan Williams, Ant\^onio H. Ribeiro, Danica Kragic ·

    SCENT：将质谱与分子结构对齐以实现嗅觉感知

    arXiv:2605.27009v1 Announce Type: new Abstract: Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at inference, which is not available in practical sensing settings. We address thi…

  2. arXiv cs.LG TIER_1 English(EN) · Danica Kragic ·

    SCENT: 匹配质谱与分子结构以实现嗅觉感知

    Predicting human olfactory perception from molecular structure has seen remarkable progress, yet these approaches require explicit chemical structure at inference, which is not available in practical sensing settings. We address this gap by exploring direct electron ionization ma…

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