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Assessment of Machine Learning Reliability Methods for Quantifying the Applicability Domain of QSAR Regression Models
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Classification with reject option
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A Risk Assessment Perspective of Current Practice in Characterizing Uncertainties in QSAR Regression Predictions
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The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
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The Elements of Statistical Learning
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QSAR with experimental and predictive distributions: an information theoretic approach for assessing model quality
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Comparison of approaches for estimating reliability of individual regression predictions
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The importance of the domain of applicability in QSAR modeling
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Automatic selection of reliability estimates for individual regression predictions
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Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR
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Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest
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Comparison Data Sets for Benchmarking QSAR Methodologies in Lead Optimization
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Is Combining Classifiers with Stacking Better than Selecting the Best One?
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QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review
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Summary of a workshop on regulatory acceptance of (Q)SARs for human health and environmental endpoints.
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