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Use when you have canonicalized SMILES strings from a chemical database (e.
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Basierend auf der SOC-Berufsklassifikation
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 | atom-feature-extraction-chemistry |
| description | Use when you have canonicalized SMILES strings from a chemical database (e. |
| license | CC-BY-4.0 |
| metadata | {"edam_operation":"http://edamontology.org/operation_3357","edam_topics":["http://edamontology.org/topic_0209","http://edamontology.org/topic_3314","http://edamontology.org/topic_3372"],"tools":["manual expert review","RDKit","train-test.py","PyTorch"],"techniques":["ion-mobility-MS"],"license_tier":"open"} |
| derived_from | [{"doi":"10.1186/s13321-024-00899-w","title":"mol2ccs"}] |
| evidence_spans | [] |
| claims | [] |
| provenance | {"collection":"https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v2","assembled_by":"scripts/collect_metabolomics_collection.py","sources":[{"build":"coll_mol2ccs","doi":"10.1186/s13321-024-00899-w","title":"mol2ccs"}],"dedup_kept_from":"coll_mol2ccs"} |
| schema_version | 0.2.0 |
| attribution | {"generator":"AgenticScienceBuilder","original_doi":"10.1186/s13321-024-00899-w","all_source_dois":["10.1186/s13321-024-00899-w"],"zenodo_doi":"10.5281/zenodo.20794027","curators":[],"promoter":"Louis-Félix Nothias","sponsor":"CNRS & Université Côte d'Azur"} |
Extract atomic-level chemical features (atomic number, formal charge, hybridization, aromaticity) and bond-level features (bond type, aromaticity, stereochemistry) from canonicalized SMILES strings to construct molecular graph representations for GNN input. This skill bridges chemical structure notation and learnable tensor representations required by graph neural networks.
You have canonicalized SMILES strings from a chemical database (e.g., METLIN-CCS, CCSBase, or the CCS dataset) and need to convert them into graph tensor representations suitable for training or inference with a graph neural network for molecular property prediction tasks such as collision cross section (CCS) estimation.
Load each canonical SMILES string using RDKit and construct a molecular graph object. Extract atom-level features—atomic number, formal charge, hybridization state, and aromaticity flag—for each atom in the molecule. Simultaneously extract bond-level features—bond type (single, double, triple, aromatic), aromaticity, and stereochemistry—for each bonded pair. Encode these features as numerical tensors (e.g., one-hot or continuous vectors) alongside adjacency matrices representing the graph topology. Serialize the resulting graph objects (atom features, bond features, adjacency matrices, and metadata) to PyTorch (.pt) or pickle (.pkl) format for downstream GNN input. The rationale is that GNNs require explicit feature vectors and connectivity patterns; raw SMILES strings lack the structured graph representation needed for message-passing algorithms.