Fig. 2: Conceptual comparison of spectral embedding generation strategies. | Communications Chemistry

Fig. 2: Conceptual comparison of spectral embedding generation strategies.

From: A large language model for deriving spectral embeddings for accurate compound identification in mass spectrometry

Fig. 2

A Schematic representation of the Spec2Vec method. A Word2vec model is trained using relationships derived from a mass spectral (MS) library to learn the spectral embeddings. B Schematic representation of the proposed LLM4MS approach. A large language model, pre-trained on extensive and diverse knowledge domains, undergoes fine-tuning using the MS library. This process leverages the LLM's embedded knowledge to generate chemically informed spectral embeddings for improved mass spectral matching.

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