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 <= upper))
print(f"Empirical coverage: {coverage:.2%}")  # should be ~90%

Quick Start — Tanimoto GP

import gpytorch
import torch
from rdkit.Chem import rdMolDescriptors

class TanimotoKernel(gpytorch.kernels.Kernel):
    """Tanimoto similarity kernel for binary fingerprints."""
    has_lengthscale = False

    def forward(self, x1, x2, **params):
        x1_sum = x1.sum(-1, keepdim=True)
        x2_sum = x2.sum(-1, keepdim=True)
        dot = torch.matmul(x1, x2.transpose(-1, -2))
        intersection = dot
        union = x1_sum + x2_sum.transpose(-1, -2) - dot
        return intersection / union.clamp(min=1e-8)

# Usage:
# fps = np.array([get_morgan_fp(smi) for smi in smiles_list])
# X = torch.tensor(fps, dtype=torch.float32)
# y = torch.tensor(activities, dtype=torch.float32)
# → see references/gaussian-processes.md for full GP model