Improving regulatory confidence in, and acceptance of, a prediction of toxicity from a quantitative structure-activity relationship (QSAR) requires assessment of its uncertainty and determination of whether the uncertainty is acceptable. Thus, it is crucial to identify potential uncertainties fundamental to QSAR predictions. Based on expert review, sources of uncertainties, variabilities and biases, as well as areas of influence in QSARs for toxicity prediction were established. These were grouped into three thematic areas: uncertainties, variabilities, potential biases and influences associated with 1) the creation of the QSAR, 2) the description of the QSAR, and 3) the application of the QSAR, also showing barriers for their use. Each the...
Abstract The computational approaches used to predict toxicity are evolving rapidly, a process haste...
Structure Activity Relationships are computational techniques used to predict biological activities ...
In order to efficiently and effectively assess the risks of large numbers of existing chemicals and ...
Improving regulatory confidence in, and acceptance of, a prediction of toxicity from a quantitative ...
The description of quantitative structure¿activity relationship (QSAR) models has been a topic for s...
The description of quantitative structure¿activity relationship (QSAR) models has been a topic for s...
In silico models are used to predict toxicity and molecular properties in chemical safety assessment...
It is relevant to consider uncertainty in individual predictions when quantitative structure-activit...
The conditions and methods for constructing reliable QSARs are revised in relation to each component...
Little or nothing is known about the toxicity of most of the >100,000 chemicals released into the en...
The aim of this chapter is to outline the different ways in which Quantitative Structure-Activity Re...
Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction ...
This article is a review of the use of quantitative (and qualitative) structure-activity relationshi...
This article provides an overview of methods for reliability assessment of quantitative structure–ac...
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur U...
Abstract The computational approaches used to predict toxicity are evolving rapidly, a process haste...
Structure Activity Relationships are computational techniques used to predict biological activities ...
In order to efficiently and effectively assess the risks of large numbers of existing chemicals and ...
Improving regulatory confidence in, and acceptance of, a prediction of toxicity from a quantitative ...
The description of quantitative structure¿activity relationship (QSAR) models has been a topic for s...
The description of quantitative structure¿activity relationship (QSAR) models has been a topic for s...
In silico models are used to predict toxicity and molecular properties in chemical safety assessment...
It is relevant to consider uncertainty in individual predictions when quantitative structure-activit...
The conditions and methods for constructing reliable QSARs are revised in relation to each component...
Little or nothing is known about the toxicity of most of the >100,000 chemicals released into the en...
The aim of this chapter is to outline the different ways in which Quantitative Structure-Activity Re...
Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction ...
This article is a review of the use of quantitative (and qualitative) structure-activity relationshi...
This article provides an overview of methods for reliability assessment of quantitative structure–ac...
Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur U...
Abstract The computational approaches used to predict toxicity are evolving rapidly, a process haste...
Structure Activity Relationships are computational techniques used to predict biological activities ...
In order to efficiently and effectively assess the risks of large numbers of existing chemicals and ...