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The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. Expand
Principles of QSAR models validation: internal and external
- P. Gramatica
- Computer Science
- 1 May 2007
The recent REACH Policy of the European Union has led to scientists and regulators to focus their attention on establishing general validation principles for QSAR models in the context of chemical regulation (previously known as the Setubal principles). Expand
Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.
- L. Eriksson, J. Jaworska, A. Worth, M. Cronin, Robert M. McDowell, P. Gramatica
- Computer Science, Medicine
- Environmental health perspectives
- 1 August 2003
We provide an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Expand
Real External Predictivity of QSAR Models: How To Evaluate It? Comparison of Different Validation Criteria and Proposal of Using the Concordance Correlation Coefficient
The main utility of QSAR models is their ability to predict activities/properties for new chemicals, and this external prediction ability is evaluated by means of various validation criteria. Expand
Real External Predictivity of QSAR Models. Part 2. New Intercomparable Thresholds for Different Validation Criteria and the Need for Scatter Plot Inspection
The evaluation of regression QSAR model performance, in fitting, robustness, and external prediction, is of pivotal importance. Expand
QSARINS: A new software for the development, analysis, and validation of QSAR MLR models
- P. Gramatica, N. Chirico, E. Papa, S. Cassani, S. Kovarich
- Computer Science
- J. Comput. Chem.
- 15 September 2013
QSARINS is a user‐friendly platform for QSAR modeling in agreement with the OECD Principles and for the analysis of the reliability of the obtained predicted data. Expand
Joint algal toxicity of 16 dissimilarly acting chemicals is predictable by the concept of independent action.
For a predictive assessment of the aquatic toxicity of chemical mixtures, two competing concepts are available: concentration addition and independent action. Concentration addition is generally… Expand
QSAR modeling: where have you been? Where are you going to?
- A. Cherkasov, E. Muratov, +17 authors A. Tropsha
- Biology, Medicine
- Journal of medicinal chemistry
- 6 January 2014
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and… Expand
Predicting the joint algal toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants.
Herbicidal s-triazines are widespread contaminants of surface waters. They are highly toxic to algae and other primary producers in aquatic systems. This results from their specific interference with… Expand
QSARINS‐chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS
A database of environmentally hazardous chemicals, collected and modeled by QSAR by the Insubria group, includes several datasets of chemical structures and their corresponding endpoints (physicochemical properties and biological activities). Expand