ADAPTIVE

ADAPTIVE generates compact, data-dependent molecular vectors to improve metabolite identification from tandem mass spectra.

Key Features:

• Two-Part Mapping System: A structure-to-molecular vector mapping using a message passing neural network trained with the Hilbert-Schmidt Independence Criterion to maximize correlation with spectra, and a spectrum-to-molecular vector mapping that projects mass spectra directly to molecular vectors without intermediate chemical fingerprints.
• Compact and Adaptive Molecular Vectors: Produces compact, data-dependent molecular vectors tailored for metabolite identification, reducing reliance on large, redundant molecular fingerprints.
• Integration with Input-Output Kernel Regression (IOKR): Embeds learned molecular vectors into the IOKR framework to improve predictive performance and computational efficiency for metabolomics identification tasks.

Scientific Applications:

• Enhancing Metabolite Identification: Improves accuracy and specificity of chemical structure identification from tandem mass spectra in metabolomics studies.
• Reducing Computational Load: Reduces redundancy and computational demands in large-scale metabolomics analyses through compact vector representations.

Methodology:

Learn molecular vectors from chemical structures using a message passing neural network, optimize vector–spectrum correspondence with the Hilbert-Schmidt Independence Criterion, map mass spectra directly to these molecular vectors, and integrate the vectors into Input-Output Kernel Regression (IOKR).