Example Library

Start with the local workflow and small pipeline. Their synthetic inputs and expected outputs are included and exercised by integration tests. See data provenance.

Scientific configuration templates

These templates accompany the public plugin pages. They need prepared inputs and the scientific runtimes listed below. They are schema-checked against the tagged components in the version matrix; engine execution is not part of the docs-only CI. Replace placeholders, check molecular preparation, and scale compute settings before running a scientific job. They do not download experimental data.

Workflow	Configuration	Runtime / preparation
docking/unidock	YAML	Uni-Dock executable, supported GPU; prepared PDBQT receptor and ligand SDF; replace box center/size
docking/idockya	YAML	pyocl-idor/OpenChemLib (Java 21+); receptor and native ligand SDF in the same frame
docking/gnina	YAML	GNINA executable; prepared receptor and ligands; review CNN/GPU settings
descriptors/ani-psa	YAML	RDKit, TorchANI and model weights; check allowed elements and conformer settings
descriptors/phesa	YAML	pyocl-idor/OpenChemLib (Java 21+); validated reference chemistry and coordinate-frame handling
descriptors/ani-conformers	YAML	RDKit, TorchANI and model weights; SMILES and supported elements
descriptors/cluster-molecules	YAML	RDKit and declared clustering dependencies; CSV/SMILES/SDF inputs; select a sensible cluster count
md/fix-structure	YAML	OpenMM/PDBFixer and matching force fields; check protonation, termini, and untouched cofactors
md/center-and-align	YAML	MDAnalysis; matching topology and trajectory, atom selections
md/extract-frames	YAML	MDAnalysis and applicable trajectory readers; frame/time selections
md/cluster-representative	YAML	MDAnalysis and clustering stack; matching topology/trajectory and representative criteria
md/plain-md	YAML	OpenMM and preparation/parameterization stack; authoritative protein/ligand chemistry
md/minimize	YAML	OpenMM, ParmEd and AmberTools for automatic preparation; or validated prepared topology and mapping
md/openbpmd	YAML	OpenMM, OpenBPMD preparation stack and AmberTools; complete complex and ligand chemistry
md/grand	YAML	grand, OpenMM, OpenMMTools/force fields and OpenFF; ligand-centered pocket and parameterized complex
md/inducia	YAML	Public descriptor/docking dependencies, GNINA, grand, OpenMM, AmberTools and OpenBPMD; prepared inputs
fep/openfe-abfe	YAML	OpenFE, OpenFF and parameterization stack; prepared complex, convergence assessment
fep/openfe-rbfe-septop	YAML	OpenFE/SepTop runtime, ligand mapping and parameterization; scientifically suitable paired systems
qm/transfer-free-energy	YAML	PySCF/openCOSMO-RS stack; conformers, solvent structures, and standard state
ml/simple-gp	YAML	Declared GP/ML stack; numeric CSV features and training/prediction split
ml/pca	YAML	Declared ML stack; numeric CSV and appropriate preprocessing
ml/model-zoo	YAML	Dependencies for selected registry models; leakage-free training/validation data
ml/model-factory	YAML	Dependencies for selected registry models; explicit target/task and split
ml/explore-classifier	YAML	Interactive UI dependencies and prepared classification data
datascience/csv-scatter	YAML	pandas/Bokeh; CSV columns and optional RDKit for molecule hover
datascience/distribution-diagnostics	YAML	pandas/SciPy/Matplotlib; numeric CSV column; runnable synthetic tutorial also supplied

Run a downloaded template

Save run_tool.py beside a chosen configuration. Supply all input files, then run from that directory so relative workflow paths resolve consistently:

caddforge show-config datascience distribution-diagnostics
caddforge run datascience distribution-diagnostics --config diagnostics.yaml
python run_tool.py datascience distribution-diagnostics diagnostics.yaml

Use your template's section, tool, and filename in these commands. The Python runner uses get_tool and submit_workflow, passes declared input ports for runtime staging, and checks the canonical result. For Slurm, it prints the submitted job ID; inspect status before reading completed outputs. Per-plugin pages explain expected files and result keys. No generic CLI command validates the scientific suitability of inputs.

Units and preparation

Family	Quantities to record
Docking / alignment	Coordinate frame and distance units; score definition, not an inferred affinity unit
MD / minimization	Length/time/temperature units in field names; engine energy units, force field, and restraint definitions
FEP / QM	Energy units reported by the selected engine, temperature, standard state, uncertainty/convergence
Descriptors	Descriptor/model definition; PheSA frame behavior; dimensionless similarities where appropriate
ML / data science	Feature/target units, transformations, split and seed; synthetic examples are dimensionless

See scientific interpretation and method references. Advanced pipeline templates and minimization configurations require prepared input data; their schema checks are separate from real scientific execution.