HolobiomicsLab GNN Molecular Property Prediction for Annotation Rescoring (RDKit graph -> PyTorch GNN -> candidate re-ranking)
Summary
SMILES + labelled property dataset in, a property-rescored candidate table out: RDKit/PyG molecular graph construction, GNN architecture design and supervised training, trained-model inference on candidate structures, and predicted-vs-observed candidate re-ranking.
When to use
Use when you want to train or apply a graph neural network over molecular graphs to predict a structure-dependent property — retention time, collision cross section (CCS), or an MS2 spectrum — and use that predicted property to filter or re-rank candidate structures for untargeted metabolomics annotation: build RDKit/PyTorch-Geometric molecular graphs from SMILES, design and train a GNN against a labelled property dataset, run inference to predict the property for candidate structures, and rescore a candidate pool by comparing predicted vs observed property values.
When NOT to use
- The data is not LC-MS, ion-mobility-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).
Stages
Stage 1 — featurize
Goal: SMILES structures -> attributed molecular graphs (RDKit + PyTorch Geometric)
EDAM operation: operation_0292
Inputs: tsv · Outputs: pyg-graph-dataset
Candidate leaf skills: molecular-graph-construction-from-smiles (primary), molecular-graph-construction-pytorch-geometric, feature-encoding-atoms-bonds, molecular-structure-parsing-rdkit, pytorch-graph-serialization
Tools (primary): manual expert review, RDKit, PyTorch
Other candidate tools: torch_geometric, torch, Python, rdkit-pypi, RT-Transformer, Python pickle module, HDF5 (h5py)
Grounding: 4 KB(s); DOIs: 10.1021/acs.analchem.0c04071, 10.1038/s41467-019-13680-7, 10.1093/bioinformatics/btae084, 10.1186/s13321-024-00899-w
Stage 2 — architect
Goal: molecular graph representation -> GNN architecture (message-passing layers + property head)
EDAM operation: operation_0337
Inputs: pyg-graph-dataset · Outputs: model-architecture
Candidate leaf skills: graph-neural-network-architecture-assembly (primary), graph-neural-network-architecture-design, gnn-architecture-design-for-molecular-graphs, graph-neural-network-implementation, graph-neural-network-architecture-implementation
Tools (primary): Graphormer, DGL, RDKit, PyTorch
Other candidate tools: Python, torch, torch-scatter, torch-sparse, torch-cluster, torch_geometric, PyTorch (torch), RDKit (rdkit-pypi), torch-scatter, torch-sparse, torch-cluster, scanpy, STAGATE, pandas, h5py, GNN-RT (repository), Python 3, PyG, NumPy, conda, pip, PyTorch Geometric (PyG), NumPy and pandas
Grounding: 8 KB(s); DOIs: 10.1002/cem.70040, 10.1021/acs.analchem.0c04071, 10.1021/acs.analchem.3c03177, 10.1021/acs.analchem.4c05859 …
Stage 3 — train
Goal: GNN architecture + labelled property dataset -> trained model checkpoint
EDAM operation: operation_3445
Inputs: model-architecture, pyg-graph-dataset · Outputs: model-checkpoint
Candidate leaf skills: graph-neural-network-model-training (primary), pytorch-model-checkpoint-management, pytorch-model-training-and-optimization, retention-time-prediction-validation
Tools (primary): Python, PyG, RDKit, NumPy, Pandas, torch-scatter, torch-sparse, torch-cluster, PyTorch, PyG (PyTorch Geometric), TorchMetrics, torch-scatter, torch-sparse, torch-cluster
Other candidate tools: Anaconda
Grounding: 2 KB(s); DOIs: 10.1021/acs.analchem.0c04071, 10.1021/acs.jcim.4c02179
Stage 4 — predict
Goal: trained GNN + candidate structures -> predicted property values
EDAM operation: operation_3659
Inputs: model-checkpoint, tsv · Outputs: tsv
Candidate leaf skills: gnn-model-inference-and-prediction (primary), graph-neural-network-model-inference, neural-network-inference-execution, molecular-property-prediction-feature-construction
Tools (primary): PyTorch Geometric
Other candidate tools: PyTorch or TensorFlow, PyTorch, TensorFlow, RDKit, ms-pred, ICEBERG WebUI, ICEBERG model, ms-pred repository, PubChem, chemprop, chemprop-IR
Grounding: 3 KB(s); DOIs: 10.1021/acs.analchem.3c04654, 10.1021/acs.jcim.1c00055, 10.1186/s13321-024-00899-w
Stage 5 — rescore
Goal: predicted property + experimental evidence -> re-ranked / filtered annotation candidates
EDAM operation: operation_3800
Inputs: tsv, feature-table · Outputs: tsv
Candidate leaf skills: metabolite-annotation-by-chromatographic-behavior (primary), candidate-ranking-by-score, metabolite-candidate-ranking, candidate-structure-ranking, ranked-annotation-prioritization, metabolite-annotation-ensemble-ranking
Tools (primary): Retip, Retip (R package), pyRetip (Python package), Retip app
Other candidate tools: Python 3.11.7, MVP (MultiView Projection), PyTorch Geometric, DGL (Deep Graph Library), RDKit, Streamlit, MassSpecGym, pip, CUDA, PyTorch, CUDA 11.8, Python, pyrwr, MetFrag, ChemWalker, DiffSpectra, Diffusion Molecule Transformer (DMT), SpecFormer, mWISE, R, FELLA, igraph, MLP (Multi-Layer Perceptron) baseline model, GNN (Graph Neural Network) baseline model, LDA (Latent Dirichlet Allocation), PyTorch & DGL
Grounding: 7 KB(s); DOIs: 10.1021/acs.analchem.1c00238, 10.1021/acs.analchem.9b05765, 10.1093/bioinformatics/btad078/7067745, 10.1093/bioinformatics/btae490 …
Grounding
Each stage carries the kb_slugs/dois of the leaves it draws on. Ground any stage against its source paper with the collection's /ground command or bin/perspicacite_kb_bind.py (Perspicacité KB; serverless local-clone fallback).
Verification contract
workflow.yaml is gradable by asb solve-workflow (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.
Provenance
Generated by compose_workflows.py (semantic binding + EDAM-aware primary selection). derived_from_workflows lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via release_gate.py.