Version Compatibility

Reference examples tested with: chemprop 2.0+ (major API change from 1.x), RDKit 2024.09+, scikit-learn 1.4+, MAPIE 0.8+ (conformal prediction), shap 0.44+, pytorch 2.1+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> to check signatures
• CLI: chemprop train --help

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

QSAR Modeling

Hard rules

1. No fabricated citations. Every cited work must resolve to a verifiable
2. No claim without provenance. Every quantitative or factual claim
3. No silent failure. Every script invocation, API call, or tool use must declare its exit status and what to do on non-zero. A skill that silently swallows errors is a violation.

When to use

Load this skill when the user asks a question that matches its declared trigger conditions (see the frontmatter description for the most common ones). Do not load it for questions outside its scope — defer to the appropriate ORS skill instead.

When NOT to use

Do not load this skill if the question is in a sibling skill's domain (see ## Cross-references), if the user explicitly asks for a different tool, or if the task is outside the skill's declared category.

Model Taxonomy

Decision: For 200-10k compounds, chemprop 2.0 D-MPNN is the modern standard. For <200 compounds, Random Forest + ECFP4 is competitive.

chemprop 2.0 Training (CLI)

Goal: Train a chemprop D-MPNN ensemble with scaffold-balanced split.

chemprop train \
    --data-path data.csv \
    --task-type classification \
    --save-dir model_dir \
    --molecule-featurizers rdkit_2d_normalized \
    --num-folds 5 \
    --ensemble-size 5 \
    --epochs 50 \
    --batch-size 128 \
    --split scaffold_balanced \
    --split-sizes 0.8 0.1 0.1

OECD 5 Principles

1. Defined endpoint: specific bioassay, units, threshold definitions
2. Unambiguous algorithm: reproducible code, fixed seeds
3. Defined applicability domain (AD): where the model is valid
4. Appropriate statistical validation: external test set, cross-validation
5. Mechanistic interpretation: biological/chemical rationale

Applicability Domain Methods

Operational rule: Set --ensemble-size 5 at training; at predict time, flag predictions with ensemble std > P95 as out-of-AD.

References

• Yang et al., J. Chem. Inf. Model. 59:3370 -- chemprop scaffold split.
• OECD -- QSAR validation principles.

Related Skills

• chemoinformatics/molecular-descriptors - Features
• chemoinformatics/molecular-standardization - Critical upstream
• chemoinformatics/admet-prediction - ADMET QSAR"

Cross-references

Other skills in this category:

• admet-prediction
• conformer-generation
• covalent-design
• docking-rescoring
• free-energy-calculations
• generative-design
• molecular-descriptors
• molecular-io
• molecular-standardization
• pharmacophore-modeling
• pose-validation
• protac-degraders
• retrosynthesis
• scaffold-analysis
• shape-similarity
• similarity-searching
• substructure-search
• virtual-screening

Changelog

• 1.1.0 (migration) — Bulk-migrated to v0.4.0 schema: canonical metadata block, base Hard rules, Cross-references. Body content unchanged; author should review and fill in any domain-specific extensions to the Hard rules.
• 1.0.0 — Initial release.