Strategic selection of chemicals for testing. Part I. Functionalities and performance of basic selection methods.
To develop quantitative structure-activity relationships (QSAR) models capable of predicting adverse effects for large chemical inventories and diverse structures, an interactive approach is presented that includes testing of strategically selected chemicals to expand the scope of a preliminary model to cover a target inventory. The goal of chemical selection in this context is to make the testing more effective in terms of adding maximal new structural information to the predictive model with minimal testing. The aim of this paper is to describe a set of algorithmic solutions and modelling techniques that can be used to efficiently select chemicals for testing to achieve a variety of goals. One purpose of chemical selection is to refine the model thus extending our knowledge about the modelled subject. Alternatively, the selection of chemicals for testing could be targeted at achieving a more adequate structural representation of a specific universe of untested chemicals to extend the model applicability domain on each subsequent step of model development. The chemical selection tools are collectively provided in a software package referred to as ChemPick. The system also allows the visualisation of chemical inventories and training sets in multidimensional (two and three dimensions) descriptor space. The software environment allows one or more datasets to be simultaneously loaded in a three-dimensional (or N-dimensional) chart where each point represents a combination of values for the descriptors for a given conformation of a chemical. The application of the chemical selection tools to select chemicals to expand a preliminary model of human oestrogen receptor (hER) ligand binding to more adequately cover a diverse chemical inventory is presented to demonstrate the application of these tools.
Aladjov
H
H
US EPA, Mid-Continent Ecology Division, Duluth, MN 55804, USA.
Todorov
M
M
Schmieder
P
P
Serafimova
R
R
Mekenyan
O
O
Veith
G
G