Quantitative Structure Activity Relationship

Introduction

Quantitative Structure Activity Relationships (QSARs) are mathematical models that are used to predict measures of toxicity from physical characteristics of the structure of chemicals (known as molecular descriptors). Acute toxicities (such as the concentration that causes half of a fish population to die) are one example of the toxicity measures that may be predicted from QSARs. Simple QSAR models calculate the toxicity of chemicals using a simple linear function of molecular descriptors:

Toxicity = ax₁+bx₂+c

where x₁ and x₂ are the independent descriptor variables and a, b, and c are fitted parameters. Examples of molecular descriptors include the molecular weight and the octanol-water partition coefficient. Additional examples are provided in our Molecular Descriptors Guide (PDF) (47 pp, 279 KB).

Uses of QSAR toxicity models

• QSAR toxicity predictions may be used to screen untested compounds in order to establish priorities for traditional bioassays, which are expensive and time-consuming.
• QSAR models are useful for estimating toxicities needed for green process design algorithms such as the Waste Reduction Algorithm.

Objectives

• Develop quantitative structure activity relationship (QSAR) methodologies to estimate toxicity from molecular structure
• Develop software, such as the Toxicity Estimation Software Tool (T.E.S.T.), that will enable users to easily estimate toxicity from molecular structure

QSAR Methodologies

Several QSAR methodologies have been developed:

• Hierarchical method - The toxicity for a given query compound is estimated using the weighted average of the predictions from several different models. The different models are obtained by using Ward’s method to divide the training set into a series of structurally similar clusters. A genetic algorithm-based technique is used to generate models for each cluster. The models are generated prior to runtime.
• FDA method - The prediction for each test chemical is made using a new model that is fit to the chemicals that are most similar to the test compound. Each model is generated at runtime.
• Single-model method - Predictions are made using a multilinear regression model that is fit to the training set (using molecular descriptors as independent variables) using a genetic algorithm-based approach. The regression model is generated prior to runtime.
• Group contribution method - Predictions are made using a multilinear regression model that is fit to the training set (using molecular fragment counts as independent variables). The regression model is generated prior to runtime.
• Nearest neighbor method - The predicted toxicity is estimated by taking an average of the three chemicals in the training set that are most similar to the test chemical.

These methodologies are explained in detail in the publications below.

Toxicity Estimation Software Tool (T.E.S.T.)

T.E.S.T. will enable users to easily estimate acute toxicity using the above QSAR methodologies. The software is now available for download. The software is described in further detail in the User's Guide (PDF) (41 pp, 467 KB). The software is based on the Chemistry Development Kit , an open-source Java library for computational chemistry.

Currently, the software includes models for the following endpoints:

• 96-hour fathead minnow 50% lethal concentration (LC₅₀)
• Tetrahymena pyriformis 50% growth inhibition concentration (LD₅₀) - The University of Tennessee Tetratox database
• Oral rat 50% lethal dose (LD₅₀) - US National Library of Medicine - ChemIDplus Advanced database

Models for additional endpoints will be added as they are completed.

Get email alerts when new versions of the T.E.S.T. software are posted.

Download T.E.S.T. version 1.0.2 (EXE) (1 file, 62 MB)

Currently, the executable installation file works only for users running Windows. In the future, installation files will be developed for other operating systems (i.e. Linux and Mac).

System requirements

• Java version 1.5 or higher
• Memory:
• For Windows XP®, 1 GB of RAM is recommended.
• For Windows Vista®, 2 GB of RAM is recommended.

Download Instructions

1. Save the test.exe file to your hard drive. Due to the large size of the file, the download may take 15 minutes or longer depending on the speed of the connection.
2. Double-click the test.exe file.
3. Double-click the T.E.S.T. icon (icon with the EPA logo on your desktop) to start the program.
4. The program will briefly flash a window with a black background and then load the T.E.S.T. software.

Publications

Martin, T.M., P. Harten, R. Venkatapathy, S. Das and D.M. Young. (2008). “A Hierarchical Clustering Methodology for the Estimation of Toxicity.” Toxicology Mechanisms and Methods, 18, 2: 251–266.

Martin, T.M., and D.M. Young. (2001). “Prediction of the Acute Toxicity (96-h LC₅₀) of Organic Compounds in the Fathead Minnow (Pimephales Promelas) Using a Group Contribution Method.” Chemical Research in Toxicology, 14, 10: 1378–1385.

Contact

Todd Martin, PhD.
Research Chemical Engineer