The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
By Alexander Tropsha, Paola Gramatica, V. Gombar. 2003
2,066 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Igor V. Tetko
- Antony J. Williams
- Alexander Tropsha
- Knut Baumann
- Désirée Baumann
- Roberto Todeschini
- Paola Gramatica
- Gennadiy Poda
In Materials Chemistry
- Igor V. Tetko
- Antony J. Williams
- Knut Baumann
- Désirée Baumann
- John B. O. Mitchell
- Igor I. Baskin
- Paola Gramatica
- Alexander Tropsha
In Molecular Biology
- John B. O. Mitchell
- Knut Baumann
- Désirée Baumann
- Paola Gramatica
- Joanna Jaworska
- Roberto Todeschini
- Antony J. Williams
- Denis Fourches
Citations in the corpus
- Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information (2011, methodology): “We recommend always using one of the two validation options to avoid a common pitfall of model over-fitting, which results to misleading predictions [27, 28].”
- 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.”
- Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs. (2003, positive): “This type of QSAR model follows a path that starts with a statistical validation and further interpretation for their biological and mechanistic meaning (Tropsha et al. 2003).”
- QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review (2005, methodology): “Hotelling T2 test and leverage, also distance methods, have been recommended for assessing QSAR ADs (17, 18).”
- Good practices in LIBS analysis: Review and advices (2014, methodology): “Differentmethods of validation have been implemented, namely cross-validation methods such as i) the leaveone-out procedure (LOO) [41,49], ii) the k-fold method (or LMO) [40], and iii) the leave-one-subject-out procedure (LOSO) [61] and also external validation based on the use of an independent dat”
- Artificial intelligence in cancer target identification and drug discovery (2022, neutral): “12) 258 .”