Principles of QSAR models validation: internal and external

By Paola Gramatica. 2007

2,030 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Food Science, Health, Toxicology and Mutagenesis

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Citations in the corpus

Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View (2019, methodology): “MEASURING THE EFFECTIVENESS OF THE APPLIED MODEL For measuring the effectiveness of a machine learning model, there are two main procedures: internal and external validation [163].”
Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation (2014, positive): “How does the prediction error obtained in the outer loop of cross-validation relate to external validation? The concept of external validation is based on the insight that independent test data are required to assess the generalization performance of a model since prediction of unseen data is the mo”
The use of 2D fingerprint methods to support the assessment of structural similarity in orphan drug legislation (2014, methodology): “It is generally considered better, however, to use a distinct test-set that has not been involved in the training [30,31], and this was accomplished here using data kindly provided by the CHMP that typifies their regular work-load.”
Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection (2008, methodology): “To avoid this problem some authors suggested to leave a part of data as an external set, which can be used to estimate the performance of the model.(15,41) We used this approach in our previous study.”