SIMBA

GitHub Python

About SIMBA

SIMBA is a transformer-based neural network that accurately predicts chemical structural similarity from tandem mass spectrometry (MS/MS) spectra. Unlike traditional methods relying on heuristic metrics (e.g., modified cosine similarity), SIMBA directly models structural differences, enabling precise analog identification in metabolomics.

SIMBA predicts two interpretable metrics:

  1. Substructure Edit Distance: Number of molecular graph edits required to convert one molecule into another.

  2. Maximum Common Edge Substructure (MCES) Distance: Number of bond modifications required to achieve molecular equivalence.

See the documentation for more information and detailed examples on how to get started with SIMBA for mass spectrometry-based analog discovery.

Citation

SIMBA is freely available as open source under the Apache License 2.0.

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