FlexMS is a new flexible benchmarking framework that lets researchers dynamically combine deep learning architectures and evaluate their mass spectrum prediction performance on public metabolomics datasets using multiple metrics and retrieval tasks.
Journal of chemical information and computer sciences42(6), 1273–1280 (2002)
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NaFM is a pretrained foundation model for natural products using scaffold-focused contrastive learning and masked graph objectives that achieves SOTA on taxonomy classification, gene/microbial analysis, and virtual screening tasks.
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FlexMS is a flexible framework for benchmarking deep learning-based mass spectrum prediction tools in metabolomics
FlexMS is a new flexible benchmarking framework that lets researchers dynamically combine deep learning architectures and evaluate their mass spectrum prediction performance on public metabolomics datasets using multiple metrics and retrieval tasks.
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Pretraining a Foundation Model for Small-Molecule Natural Products
NaFM is a pretrained foundation model for natural products using scaffold-focused contrastive learning and masked graph objectives that achieves SOTA on taxonomy classification, gene/microbial analysis, and virtual screening tasks.