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Use when when you have derivatized metabolite structures (SMILES or mol format) and need to predict their ionization products in MS imaging, particularly when the derivatizing matrix produces non-standard adducts (e.
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SOC 職業分類に基づく
Use when when you have a neural network or machine learning model with multiple tunable hyperparameters (layer size, regularization strength, dropout) or design choices (e.
Use when when building a visualization library that must support multiple plotting backends (e.g., matplotlib, bokeh, plotly) and multiple data types (e.g., chromatograms, spectra, peak maps) without duplicating core logic or configuration handling across backend–plot-type combinations.
Use when after peaks have been assigned to heteroatom classes (e.g., CHO, CHON, CHOS, CHOP) and you need to compare molecular composition across samples, classes, or time series.
Use when after running inference on a trained structure prediction model with one or more input modalities (1H NMR, 13C NMR, or combined), you have generated predicted molecular formulas and connectivity graphs that need to be compared against known ground truth structures.
Use when after computing a histogram of all pairwise mass differences from an MSI dataset, use this skill when you have observed mass difference peaks that may correspond to known adducts (e.g., [M+H]+, [M+Na]+, [M−H2O]+).
Use when you have a trained formula ranking model (such as MIST-CF) and want to measure the specific performance gain from incorporating multiple positive-mode adduct types (e.g., [M+H]+, [M+Na]+, [M+K]+, [M+NH4]+) instead of restricting predictions to [M+H]+ only.
| name | adduct-ion-mass-calculation |
| description | Use when when you have derivatized metabolite structures (SMILES or mol format) and need to predict their ionization products in MS imaging, particularly when the derivatizing matrix produces non-standard adducts (e. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3632","edam_topics":["http://edamontology.org/topic_0121","http://edamontology.org/topic_3172"],"tools":["RDKit","Met-ID"],"techniques":["MS-imaging"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1021/acs.analchem.5c00633","title":"metid"}] |
| evidence_spans | ["Powered by RDKit","[![Powered by RDKit]"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_metid_cq","doi":"10.1021/acs.analchem.5c00633","title":"metid"}],"dedup_kept_from":"coll_metid_cq"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1021/acs.analchem.5c00633","all_source_dois":["10.1021/acs.analchem.5c00633"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Calculate m/z values for derivatized metabolite adduct ions beyond common [M+H]+ and [M-H]- forms, with explicit support for matrix-specific ions like those produced by FMP-10 derivatizing agents. This skill automates adduct enumeration for high-throughput metabolite identification in mass spectrometry imaging.
When you have derivatized metabolite structures (SMILES or mol format) and need to predict their ionization products in MS imaging, particularly when the derivatizing matrix produces non-standard adducts (e.g., FMP-10-tagged ions, [M+Na]+, [M+K]+, or matrix-specific ion forms) rather than the common [M+H]+ and [M-H]- ions. Use this skill to generate a searchable list of expected m/z values for spectral matching and compound identification.
Parse input metabolite structures using RDKit to obtain molecular graphs. Load the derivatizing matrix chemistry rules (e.g., FMP-10 reaction scheme) and apply the derivatization transformation to each metabolite structure using RDKit chemistry operations. Compute the molecular weight of the derivatized product. Enumerate matrix-specific adduct forms (including [M+derivatization_tag]+, [M+Na]+, [M+K]+, and any other known ionization products for that matrix) and calculate their m/z values by dividing the mass by charge state. Annotate each adduct ion with its type, calculated m/z, and charge state. Output the adduct ion list for use in spectral library matching and metabolite annotation workflows.
