bio-qsar-modeling
Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.
npx skills add BioTender-max/awesome-bio-agent-skills --skill qsar-modeling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## 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>` then `help(module.function)` to check signatures - CLI: `chemprop train --help` (chemprop 2.x); `chemprop_train --help` (1.x legacy) 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 Build quantitative structure-activity relationship models from molecular structure inputs. The choice of model + featurization + split strategy determines whether the model captures real chemical signal or memorizes the training data. chemprop D-MPNN with optional Morgan / RDKit descriptors is the modern open-source standard; transformer-based methods (MolFormer, Uni-Mol, ChemBERTa) compete on benchmarks but offer minimal practical gain when domain-specific data is sparse. The OECD 5 principles structure the model for regulatory acceptance: defined endpoint, unambiguous algorit
- Version Compatibility
- Model Taxonomy
- Decision Tree by Scenario
- OECD 5 Principles
- Applicability Domain Methods
- chemprop 2.0 Training (CLI)
- Scaffold-Balanced Split (chemprop 2.0 default)
- Conformal Prediction for Calibrated Uncertainty
- SHAP / Atomic Attribution
- Bayesian Optimization for Active Learning
- Calibration (Platt / Isotonic)
- Multi-Task QSAR
- Per-Tool Failure Modes
- Random split for QSAR
chemprop 2.x CLI (current): use 'chemprop train' (space; dashes not underscores) chemprop train \ chemprop 1.x legacy CLI (for backwards reference): chemprop_train --data_path data.csv --dataset_type classification ... chemprop train --data-path multitask.csv --task-type classification \
What does the bio-qsar-modeling skill do?
Builds QSAR / QSPR models using chemprop D-MPNN, MolFormer, Uni-Mol, ChemBERTa, random forest baselines, and Gaussian processes with explicit handling of OECD 5 principles, applicability domain (kNN, leverage, conformal prediction, Mahalanobis), scaffold-balanced splits, ensemble uncertainty, calibration (Platt, isotonic), feature importance (SHAP, atomic attribution), and prospective validation. Use when building target-specific predictive models from in-house bioassay data, ADMET endpoints, or selectivity profiles.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill qsar-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.