Uncertainty Qsar

Uncertainty-Aware QSAR

QSAR models that output only point predictions are insufficient for drug discovery decisions. Uncertainty quantification (UQ) transforms predictions into actionable confidence intervals: "LogD = 2.3 ± 0.4 (90% CI)" is far more useful than "LogD = 2.3".

When to Use This Skill

• Build QSAR models with calibrated prediction intervals (not just point predictions)
• Assess whether a query molecule is within the applicability domain (AD) of the model
• Design active learning loops: prioritize compounds with high epistemic uncertainty
• Rank compounds when model uncertainty is high (don't trust raw predictions alone)
• Regulatory/submission context requiring prediction confidence bounds
• Compare model calibration (is the stated 90% CI actually 90% coverage?)

Uncertainty Types

| Type | What it means | How to reduce | Methods | |------|--------------|--------------|---------| | Epistemic | Model doesn't know (lack of training data) | Add more training data | GP variance, ensemble disagreement, MC dropout std | | Aleatoric | Intrinsic noise (measurement error) | Can't be reduced | Heteroscedastic models, learned noise σ | | Total | Combined uncertainty | — | Epistemic + Aleatoric in prediction |

Quick Start — Conformal Prediction (MAPIE)

from mapie.regression import MapieRegressor
from sklearn.ensemble import RandomForestRegressor
import numpy as np

# Fit + calibrate
base_model = RandomForestRegressor(n_estimators=100, random_state=42)
mapie = MapieRegressor(base_model, method="plus", cv=5)
mapie.fit(X_train, y_train)

# Predict with intervals (alpha = desired error rate)
y_pred, y_pi = mapie.predict(X_test, alpha=0.10)  # 90% CI
# y_pi shape: (n_samples, 2, n_alpha)
lower = y_pi[:, 0, 0]
upper = y_pi[:, 1, 0]

# Coverage check
coverage = np.mean((y_test >= lower) & (y_test  3p/n |
| AD distance | D = min_train Tanimoto; D 5000 training points use sparse GP (inducing points) or `ExactGP` + KeOps
- **Conformal requires i.i.d. calibration**: if training/calibration sets have distribution shift, coverage guarantee breaks
- **MC dropout depth**: dropout only captures epistemic if placed at multiple layers; single output layer dropout ≈ ensemble of linear heads only
- **AD threshold is task-specific**: there is no universal threshold for "inside AD" — always validate on held-out temporal/spatial split

## Related Skills

- `scientific-skills:scikit-learn` — RF/SVM/GBM base models, cross-validation
- `scientific-skills:pymc` — full Bayesian QSAR (MCMC-based posterior)
- `active-learning` (upcoming) — using uncertainty for experimental design
- `mmpa` — MMPA + uncertainty: only apply transforms where model is confident
- `generative-design` — uncertainty filter on generated molecules (discard low-confidence predictions)

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [Kdevos12](https://github.com/Kdevos12)
- **Source:** [Kdevos12/ALKYL](https://github.com/Kdevos12/ALKYL)
- **License:** MIT

Install and usage instructions live in the source repository linked above.