480-DeepMet：生成式化学语言模型预测未知代谢物

这项研究介绍了一种名为 DeepMet 的化学语言模型，旨在解决代谢组学中大量未知化学结构导致的“暗物质”难题。研究人员利用数千种已知哺乳动物代谢物的结构对该深度学习模型进行训练，使其能够学习生物合成的逻辑并预测尚未被发现的新型分子。通过将 DeepMet 生成的结构建议与质谱数据相结合，科学家们能够更精准地鉴定复杂生物样本中的未知物质。实验结果证实，该系统成功预测并发现了数十种先前未被识别的代谢物，并填补了现有数据库中的诸多空白。这种人工智能驱动的方法为探索生命体内的代谢网络提供了全新的系统性手段，显著提升了从海量生物数据中发掘新生物标志物的能力。References:* Qiang H, Wang F, Lu W, et al. Language model-guided anticipation and discovery of mammalian metabolites[J]....去小宇宙查看完整单集简介前往小宇宙评论区与主播互动

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这项研究介绍了一种名为 DeepMet 的化学语言模型，旨在解决代谢组学中大量未知化学结构导致的“暗物质”难题。研究人员利用数千种已知哺乳动物代谢物的结构对该深度学习模型进行训练，使其能够学习生物合成的逻辑并预测尚未被发现的新型分子。通过将 DeepMet...

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