Journal ArticleDOI
How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
TLDR
21 types of error that continue to be perpetrated in the QSAR/QSPR literature are identified and each is discussed, with examples (including some of the authors' own).Abstract:
Although thousands of quantitative structure–activity and structure–property relationships (QSARs/QSPRs) have been published, as well as numerous papers on the correct procedures for QSAR/QSPR analysis, many analyses are still carried out incorrectly, or in a less than satisfactory manner. We have identified 21 types of error that continue to be perpetrated in the QSAR/QSPR literature, and each of these is discussed, with examples (including some of our own). Where appropriate, we make recommendations for avoiding errors and for improving and enhancing QSAR/QSPR analyses.read more
Citations
More filters
Journal ArticleDOI
Best Practices for QSAR Model Development, Validation, and Exploitation.
TL;DR: Most critical QSAR modeling routines that are regarded as best practices in the field are examined, including procedures used to validate models, both internally and externally, as well as the need to define model applicability domains that should be used when models are employed for the prediction of external compounds or compound libraries.
Journal ArticleDOI
QSAR Modeling: Where have you been? Where are you going to?
Artem Cherkasov,Eugene N. Muratov,Eugene N. Muratov,Denis Fourches,Alexandre Varnek,Igor I. Baskin,Mark T. D. Cronin,John C. Dearden,Paola Gramatica,Yvonne C. Martin,Roberto Todeschini,Viviana Consonni,Victor E. Kuz’min,Richard D. Cramer,Romualdo Benigni,Chihae Yang,James F. Rathman,Lothar Terfloth,Johann Gasteiger,Ann M. Richard,Alexander Tropsha +20 more
TL;DR: In this paper, the authors provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive quantitative structure-activity relationship models.
Journal ArticleDOI
Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
TL;DR: The need for a standardized chemical data curation strategy that should be followed at the onset of any molecular modeling investigation is emphasized and it is demonstrated that in some cases rigorously developed QSAR models could be even used to correct erroneous biological data associated with chemical compounds.
Journal ArticleDOI
Be aware of error measures. Further studies on validation of predictive QSAR models
TL;DR: This paper shows the problems associated with the R2 based validation metrics commonly used in QSAR studies, and proposes a guideline for determining the quality of predictions based on MAE and its standard deviation computed from the test set predictions after omitting 5% high residual data points to obviate the influence of any rarely occurring high prediction errors.
References
More filters
Book
CRC Handbook of Chemistry and Physics
TL;DR: CRC handbook of chemistry and physics, CRC Handbook of Chemistry and Physics, CRC handbook as discussed by the authors, CRC Handbook for Chemistry and Physiology, CRC Handbook for Physics,
Book
Handbook of Molecular Descriptors
TL;DR: This Users guide notations acronyms list of molecular descriptors contains abbreviations for molecular descriptor values that are useful for counting and topological descriptors calculation.
Journal ArticleDOI
Beware of q2
TL;DR: It is argued that the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power, which is the general property of QSAR models developed using LOO cross-validation.
Journal ArticleDOI
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
TL;DR: A set of simple guidelines for developing validated and predictive QSPR models is presented, highlighting the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and some algorithms that can be used for this purpose.
Journal ArticleDOI
Principles of QSAR models validation: internal and external
TL;DR: Evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes.
Related Papers (5)
QSAR Modeling: Where have you been? Where are you going to?
Artem Cherkasov,Eugene N. Muratov,Eugene N. Muratov,Denis Fourches,Alexandre Varnek,Igor I. Baskin,Mark T. D. Cronin,John C. Dearden,Paola Gramatica,Yvonne C. Martin,Roberto Todeschini,Viviana Consonni,Victor E. Kuz’min,Richard D. Cramer,Romualdo Benigni,Chihae Yang,James F. Rathman,Lothar Terfloth,Johann Gasteiger,Ann M. Richard,Alexander Tropsha +20 more