environmental fate modeling
Recently Published Documents


TOTAL DOCUMENTS

13
(FIVE YEARS 1)

H-INDEX

8
(FIVE YEARS 0)

2021 ◽  
Vol 55 (5) ◽  
pp. 3001-3008
Author(s):  
Zélie Venel ◽  
Hervé Tabuteau ◽  
Alice Pradel ◽  
Pierre-Yves Pascal ◽  
Bruno Grassl ◽  
...  

2017 ◽  
pp. 1012-1043
Author(s):  
Kunal Roy ◽  
Supratik Kar

Quantitative Structure-Activity Relationship (QSAR) models have manifold applications in drug discovery, environmental fate modeling, risk assessment, and property prediction of chemicals and pharmaceuticals. One of the principles recommended by the Organization of Economic Co-operation and Development (OECD) for model validation requires defining the Applicability Domain (AD) for QSAR models, which allows one to estimate the uncertainty in the prediction of a compound based on how similar it is to the training compounds, which are used in the model development. The AD is a significant tool to build a reliable QSAR model, which is generally limited in use to query chemicals structurally similar to the training compounds. Thus, characterization of interpolation space is significant in defining the AD. An attempt is made in this chapter to address the important concepts and methodology of the AD as well as criteria for estimating AD through training set interpolation in the descriptor space.


2015 ◽  
Vol 535 ◽  
pp. 150-159 ◽  
Author(s):  
Nicole Sani-Kast ◽  
Martin Scheringer ◽  
Danielle Slomberg ◽  
Jérôme Labille ◽  
Antonia Praetorius ◽  
...  

Author(s):  
Kunal Roy ◽  
Supratik Kar

Quantitative Structure-Activity Relationship (QSAR) models have manifold applications in drug discovery, environmental fate modeling, risk assessment, and property prediction of chemicals and pharmaceuticals. One of the principles recommended by the Organization of Economic Co-operation and Development (OECD) for model validation requires defining the Applicability Domain (AD) for QSAR models, which allows one to estimate the uncertainty in the prediction of a compound based on how similar it is to the training compounds, which are used in the model development. The AD is a significant tool to build a reliable QSAR model, which is generally limited in use to query chemicals structurally similar to the training compounds. Thus, characterization of interpolation space is significant in defining the AD. An attempt is made in this chapter to address the important concepts and methodology of the AD as well as criteria for estimating AD through training set interpolation in the descriptor space.


2009 ◽  
Vol 43 (1) ◽  
pp. 128-134 ◽  
Author(s):  
Urs Schenker ◽  
Martin Scheringer ◽  
Michael D. Sohn ◽  
Randy L. Maddalena ◽  
Thomas E. McKone ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document