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Oversampling to Overcome Overfitting: Exploring the Relationship between Data Set Composition, Molecular Descriptors, and Predictive Modeling Methods

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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“Scaffold-Hopping” by Topological Pharmacophore Search: A Contribution to Virtual Screening

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Pharmacophores and Pharmacophore Searches

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The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform

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The value of in silico chemistry in the safety assessment of chemicals in the consumer goods and pharmaceutical industries

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The effect of imbalanced data sets on LDA: A theoretical and empirical analysis

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Pitfalls in QSAR

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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

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Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

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4D-Fingerprints, Universal QSAR and QSPR Descriptors

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Predictive Toxicology Modeling: Protocols for Exploring hERG Classification and Tetrahymena pyriformis End Point Predictions

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y-Randomization and Its Variants in QSPR/QSAR

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Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

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Derivation and Validation of Toxicophores for Mutagenicity Prediction

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A Comprehensive Support Vector Machine Binary hERG Classification Model Based on Extensive but Biased End Point hERG Data Sets

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Random Forests

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Cytotoxicity of selected imidazolium-derived ionic liquids in the human Caco-2 cell line. Sub-structural toxicological interpretation through a QSAR study

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Statistical Learning Theory

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New Oversampling Approaches Based on Polynomial Fitting for Imbalanced Data Sets

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LIBSVM

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New Directions in Toxicity Testing

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A Coefficient of Agreement for Nominal Scales

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