Scientific validation

MolScope is a lightweight teaching and prototyping toolkit, so validation is not just "tests pass". For every scientific method, ask:

• What is the reference?
• What assumptions does the method make?
• Where does it fail?
• What tolerance is scientifically reasonable?

The validation suite is split into two tiers:

• Tier 1 invariants run everywhere and check mathematical or conservation truths that do not need an external tool.
• Tier 2 reference comparisons run when optional scientific tools are installed: MDAnalysis, RDKit, and mkdssp/dssp.

Run the full validation layer locally:

uv run pytest tests/validation -v -rs -s

The binding-site panel includes opt-in RCSB downloads. Run it explicitly with:

MOLSCOPE_RUN_REMOTE_PDB=1 uv run pytest tests/validation/test_binding_sites_ref.py

Install optional Python references with:

uv sync --extra validation

The secondary-structure reference additionally needs a system mkdssp or dssp executable on PATH.

Current panel scope

The reference checks are targeted scientific smoke tests rather than a full benchmark. The DSSP panel spans three fold classes (1fqy helical, 1ubq mixed alpha/beta, 1shg all-beta); the bond and chemistry panels cover 18 and 12 small molecules respectively across fused rings, O/N/S heteroaromatics, halogens, sulfur, amides, a strained ring, and charged/zwitterionic species, plus a stretched-bond negative case where distance-only perception is expected to fail. a panel of real solution-NMR ensembles (1aml, plus the gzipped 1d3z, 2lz3, 6qfp, 1gab, and the opt-in remote 6v5d) cross-checks the alignment metrics against MDAnalysis, complemented by deterministic synthetic-ensemble invariants. 3ptb exercises the bundled binding-site path, and the opt-in remote panel adds 1stp, 1iep, 3ert, 1hsg, 4hvp, and 2br1 for ligand ambiguity, multi-chain complexes, cofactors and larger inhibitors.

The public Delaney ESOL solubility set (1128 compounds) exercises the dataset-prep pipeline end to end on real, messy SMILES -- scaffold/random splits, canonical deduplication and fingerprinting -- and doubles as a large-scale descriptor wrapper-transparency check against a fresh RDKit call. This is enough to catch regressions across fold classes and chemistry families, but it is still a curated mini-panel, not an exhaustive benchmark.

Reference-tool checks

Area	Reference	Validation file	Panel	Tolerance / threshold	Rationale
Mass geometry	MDAnalysis	tests/validation/test_geometry_ref.py	1fqy.pdb	radius_of_gyration relative 1e-6; center of mass absolute 1e-5; inertia relative 1e-5	Same formulas and same PDB coordinates should agree to floating-point precision.
Geometry primitives	MDAnalysis	tests/validation/test_geometry_ref.py	1fqy.pdb	distances relative 1e-5; angles/dihedrals absolute 1e-4 degrees	Coordinate precision and degree conversion dominate error.
CA distance/contact maps	MDAnalysis	tests/validation/test_geometry_ref.py	1fqy.pdb alpha carbons	distance matrix absolute 1e-5; contact pairs exact at 8 A	Contact-map logic should match an independent distance-threshold implementation.
Ensemble RMSF/RMSD	MDAnalysis	tests/validation/test_geometry_ref.py	1aml.pdb NMR ensemble	RMSF absolute 1e-3; Kabsch RMSD absolute 1e-4	Alignment and trajectory APIs differ slightly, but biologically meaningful values should agree tightly.
Ensemble RMSF/RMSD (NMR panel)	MDAnalysis	tests/validation/test_ensembles_ref.py	bundled 1d3z, 2lz3, 6qfp, 1gab; opt-in remote 6v5d	RMSF absolute 1e-3; Kabsch RMSD absolute 1e-4	Real solution-NMR ensembles across sizes and folds, gzipped to ~1 MB. 2hyn (remote) additionally confirms that an ensemble whose models carry inconsistent atom counts is rejected, not silently misaligned.
Distance bond perception	RDKit topology	tests/validation/test_bonds_ref.py	18 small molecules spanning fused rings, O/N/S heteroaromatics, halogens, sulfoxide, amide and a strained ring; plus a stretched-bond negative case	bond precision and recall each >= 0.98 on clean geometries; the stretched bond is expected to be missed	Geometry-only perception should recover clean equilibrium topologies, and should provably fail on non-equilibrium geometry (the honest reason template bonds exist).
Chemical features	RDKit atom/bond APIs	tests/validation/test_chem_ref.py	12 molecules: aromatics, O/N/S heteroaromatics, anion, cation, zwitterion, and histidine	formal charges and aromatic flags exact; bond orders exact within 1e-12	MolScope delegates optional chemical perception to RDKit, so direct RDKit arrays are the reference.
RDKit descriptors	RDKit descriptor APIs	tests/validation/test_chem_ref.py	Same chemistry panel	selected scalar descriptors relative/absolute 1e-12	Descriptor wrappers should not alter RDKit descriptor values.
Descriptors at scale	RDKit	tests/validation/test_esol_ref.py	Delaney ESOL solubility set (1128 compounds)	absolute 1e-9 vs a fresh RDKit call	Stretches the wrapper-transparency contract across a large, diverse, real chemistry set; version-proof since both sides use the installed RDKit.
Secondary structure	mkdssp / dssp	tests/validation/test_dssp_ref.py	1fqy.pdb (helical), 1ubq.pdb (mixed alpha/beta), 1shg.pdb (all-beta)	3-state helix/strand/coil agreement per fold (>= 0.95 helical, >= 0.90 mixed and all-beta); helix fraction within 0.15	MolScope's DSSP is simplified and educational, so reduced-state agreement is the honest target rather than byte-for-byte 8-state equality. The set spans three fold classes so agreement is reported as a range, not a single helical best case.
Binding sites	RCSB structures with HETATM ligands	tests/validation/test_binding_sites_ref.py	3ptb; opt-in remote panel 1stp, 1iep, 3ert, 1hsg, 4hvp, 2br1	residue records and pocket-basic descriptors finite and internally consistent	Real protein-ligand files expose ambiguity, multi-chain sites, cofactors, ions and larger inhibitors better than synthetic fixtures.
Multi-pose SDF parsing	RDKit SDMolSupplier	tests/validation/test_docking_ref.py	Hand-authored docking_poses.sdf; generated bonded ligands (benzene, aspirin, caffeine)	pose count and titles exact; score data fields exact; coordinates absolute 1e-4	read_poses underlies every dock-* tool; an independent SDF parser is the natural reference. The hand-authored fixture is written by neither library, so this is a true two-parser cross-check.

Invariant checks

Area	Validation file	Assertion	Tolerance
Rigid-body alignment	tests/validation/test_invariants.py	Kabsch alignment recovers a known rotation/translation	RMSD < 1e-9
Self RMSD	tests/validation/test_invariants.py	A structure aligned to itself has zero RMSD	RMSD < 1e-12
Geometry primitives	tests/validation/test_invariants.py	Euclidean distances, right angles, planar torsions	Exact or near machine precision
Radius of gyration	tests/validation/test_invariants.py	Uniform shell has radius of gyration equal to shell radius	Absolute < 1e-3
Coarse-graining	tests/validation/test_invariants.py	Residue COM and centroid beads equal direct reductions of source atoms	Absolute < 1e-9
Contact maps	tests/validation/test_invariants.py	Atom contact map equals brute-force all-pairs threshold	Exact matrix equality
Ensemble alignment	tests/validation/test_invariants.py	A rigidly-moving ensemble has zero RMSF and zero pairwise RMSD after superposition; a single displaced atom carries the largest RMSF	Absolute < 1e-5; argmax exact
Consensus ranking	tests/validation/test_docking_ref.py	A single score field reproduces that field's ranking; a pose best on every field takes rank 1	Exact order
Ligand efficiency	tests/validation/test_docking_ref.py	Equals the signed score per heavy atom	Relative 1e-9
Diversity selection	tests/validation/test_docking_ref.py	Identical molecules collapse to one (best-scoring) representative; representatives come from distinct clusters	Exact
Graph export	tests/validation/test_graph_invariants.py	Node count equals atom count, edge set equals the bond set, adjacency is symmetric; NetworkX and PyG exports preserve the same nodes/edges	Exact
Dataset assembly	tests/validation/test_graph_invariants.py	build_dataset keeps ids/labels/graphs aligned and survives a raw save/load round trip	Exact
Dataset-prep pipeline	tests/validation/test_esol_ref.py	On the real 1128-molecule ESOL table: random/scaffold splits are disjoint covers, canonical dedup collapses exactly the 11 duplicates, fingerprinting and diverse selection produce valid output	Exact

Assumptions and failure modes

Method	Key assumptions	Expected failure modes
Geometric bonds	Clean 3D coordinates, normal covalent distances, standard elements	Missing/extra bonds for strained structures, metals, unusual valence, bad coordinates, or raw PDB files without explicit chemistry.
RDKit chemical features	Explicit bond orders/formal charges or a geometry whose inferred single-bond graph RDKit can sanitize	Sanitization errors for inconsistent valence or missing bond-order chemistry; aromaticity depends on RDKit's model and version.
Contact maps	Static coordinates and a chosen distance cutoff/method (ca, com, or min)	Different cutoffs or representative atoms change the result; dense atom maps are O(N^2).
Simplified DSSP	Complete protein backbone atoms (N, CA, C, O) and standard residue ordering	Not canonical mkdssp; boundary residues of helices and strands are where disagreements concentrate; bare XYZ input is insufficient.
Coarse-graining	Beads are coordinate reductions and simple bead graphs for inspection	No force-field parameters, charges, exclusions, elastic networks, or validation of simulation behavior.
Docking triage	A score data field is present in the SDF; V2000 records; fingerprint-based similarity for clustering	Reads docking output but does not dock, prepare, or re-score; the consensus rank is rank aggregation, not a calibrated affinity; diversity depends on the fingerprint and Tanimoto threshold.

Updating validation

When adding a scientific feature, add at least one of:

• an invariant test if the expected behavior follows from math or conservation,
• a reference-tool comparison if a credible external implementation exists,
• a limitations-table row if the method is intentionally approximate.

Prefer tight tolerances when two implementations should be numerically equivalent. Use looser, justified thresholds only when the method is explicitly approximate, as with simplified DSSP.