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

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