القائمة
Use when when you have a metabolite SMILES structure and need to predict which adduct ions will appear in a mass spectrum acquired with a chemical derivatizing matrix.
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استنادا إلى تصنيف SOC المهني
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-mass-calculation-from-smiles |
| description | Use when when you have a metabolite SMILES structure and need to predict which adduct ions will appear in a mass spectrum acquired with a chemical derivatizing matrix. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3629","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"] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_metid","doi":"10.1021/acs.analchem.5c00633","title":"metid"}],"dedup_kept_from":"coll_metid"} |
| 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"} |
Compute expected m/z values for metabolite adduct ions given a SMILES string and a derivatizing matrix profile (e.g., FMP-10). This skill automates the prediction of non-standard ionization products beyond [M+H]+ and [M-H]−, enabling high-throughput metabolite identification in mass spectrometry imaging.
When you have a metabolite SMILES structure and need to predict which adduct ions will appear in a mass spectrum acquired with a chemical derivatizing matrix. Use this skill especially when the derivatizing matrix produces ions other than common [M+H]+ in positive mode or [M-H]− in negative mode, such as with FMP-10 or other custom matrices. Apply it during the feature-to-compound matching stage of metabolite identification in mass spectrometry imaging workflows.
Parse the input metabolite SMILES string using RDKit to construct and validate the molecular graph structure. Load the matrix-specific adduct ionization ruleset—either from built-in reference data (e.g., FMP-10 Nature Methods profiles) or from a user-defined matrix configuration. For each adduct rule in the matrix profile, apply RDKit's molecular weight calculator to compute the expected m/z by accounting for the derivatizing matrix modification mass, the adduct formula (e.g., +H, +Na, +NH4), and the resulting ionization charge state. Return a ranked table of predicted adduct m/z values with their corresponding formulas and mass shifts. Validate the outputs by comparing against reference adduct masses from published datasets or experimental standards to confirm the matrix-specific modifications are correctly applied.
