Applicability domains for cheminformactics.
Project description
MLChemAD
Applicability domain definitions for cheminformatics modelling.
Getting Started
Install
pip install mlchemad
Example Usage
from mlchemad import TopKatApplicabilityDomain, data
# Create the applicability domain
app_domain = TopKatApplicabilityDomain()
# Fit it to the training set
app_domain.fit(data.training)
# Determine outliers from multiple samples (rows) ...
print(app_domain.contains(data.test))
# ... or a unique sample
sample = data.test[5] # Obtain the 5th row as a pandas.Series object
print(app_domain.contains(sample))
Depending on the definition of the applicability domain, some samples of the training set might be outliers themselves.
Applicability domains
The applicability domain defined by MLChemAD as the following:
- Bounding Box
- PCA Bounding Box
- Convex Hull
(does not scale well) - TOPKAT's Optimum Prediction Space
(recommended with molecular descriptors) - Leverage
- Hotelling T²
- Distance to Centroids
- k-Nearest Neighbors
(recommended with molecular fingerprints with the use ofdist='rogerstanimoto'
andhard_threshold=0.75
for ECFP fingerprints) - Isolation Forests
- Non-parametric Kernel Densities
Project details
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