How to use and report (Q)SARs : practical guide. August 2025.
Date: 2025-11-04
Institutions: European Chemicals Agency
Subjects: European Chemicals Agency, administrative formalities, alternatives to animal testing, chemical product, chemistry, report, scientific research, software, user guide

Practical guide  

How to use and report (Q)SARs 

Version 3.1.1 – August 2025 

 
 
 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1 

Version 

Changes 

Version 1.0 

First version 

Date 

March 2010 

Version 2.0 

Revision of the Practical guide addressing structure and 
content. The update includes the following: 

December 2012 

•  Correction of broken hyperlinks throughout the

document. 

•  Changes in IUCLID screenshots with associated text

improved for more clarity. 

•  Section 2 on ‘How to get started with (Q)SARs’

addressing approach to tools developed in research 
and development projects. 

Version 3.0 

Revision of the Practical guide addressing structure and 
content. The update includes the following: 

March 2016 

•  Update of information in sections 2 and 3.

•  Addition of a section (section 4) giving practical

examples on how to assess the reliability of QSAR 
predictions with some QSAR programs. 

•  Addition of an appendix (Appendix 1) giving
examples (a non-exhaustive list) of (Q)SAR 
programs available for each of the REACH required 
endpoints. 

Version 3.1 

Revision of section 3.4 to reflect the changes brought by 
IUCLID 6. 

July 2016 

Version 3.1.1  Update of the link to VEGA Hub and other hyperlinks 

August 2025 

Practical Guide – How to use and report (Q)SARs 

Reference:   
Cat. Number: 
ISBN:    
ISSN:    
DOI: 
Publ.date:    
Language:   

ECHA-16-B-09-EN 
ED-01-25-022-EN-N 
978-92-9468-509-4 
1831-6727 
10.2823/8315768 
August 2025
EN

© European Chemicals Agency, 2025 
Cover page © European Chemicals Agency 

If you have questions or comments in relation to this document please send them (quote the 
reference and issue date) using the information request form. The information request form 
can be accessed via the Contact ECHA page at: 
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European Chemicals Agency 

P.O. Box 400, FI-00121 Helsinki, Finland 

Practical Guide - How to use and report (Q)SARs 3.1.1 

3 

Table of Contents 

1. INTRODUCTION 

2. HOW TO GET STARTED WITH (Q)SARS 

4 

5 

2.1 Definitions ........................................................................................................... 5 
2.2 Substance characterisation .................................................................................... 5 
2.3 Experimental results ............................................................................................. 5 
2.4 Conditions for using (Q)SAR results ........................................................................ 6 
2.5 Strategy for using (Q)SAR results ........................................................................... 6 

3. HOW TO CHECK A QSAR PREDICTION 

7 

3.1 Is the (Q)SAR model valid? .................................................................................... 7 
3.2 Does the substance fall within the applicability domain of the (Q)SAR model? .............. 8 
3.3 Is the prediction adequate for the purpose of C&L and/or risk assessment? ................. 9 
3.4 How to report a (Q)SAR prediction in IUCLID ........................................................... 11 

4. PRACTICAL EXAMPLES 

15 

4.1 Log Kow (EPI Suite) .............................................................................................. 15 
4.2 Ready biodegradability (VEGA) ............................................................................... 18 
4.3 Short-term toxicity to fish (ECOSAR) ...................................................................... 21 
4.4 Acute toxicity to rat (T.E.S.T.) ................................................................................ 26 

APPENDIX 1. QSAR MODELS RELATED TO REACH ENDPOINTS 

APPENDIX 2. FURTHER GUIDANCE DOCUMENTS AND LINKS 

30 

36 

 
 
 
 
 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1   

1. Introduction 

REACH foresees in Annex XI that the standard testing regime can be adapted by the use of 
non-test methods, such as (quantitative) structure-activity relationships [(Q)SARs], if certain 
conditions are fulfilled. 

This practical guide extends the chapter on (Q)SARs available in the practical guide on “How to 
use alternatives to animal testing to fulfil your information requirements for REACH 
registration”.  

It provides an overview of important aspects to consider when predicting properties of 
substances using (Q)SAR models as defined in the REACH Regulation – aspects which ECHA 
also takes into account to evaluate (Q)SAR results. This practical guide also gives useful 
examples for good prediction practices based on widely used and freely available (Q)SAR 
software programs. 

Section 2 of this document gives general information about (Q)SARs and how to use them. 

Section 3 explains the conditions that need to be fulfilled to use (Q)SAR predictions under 
REACH. Registrants are advised to explicitly include these considerations in their registration 
dossiers. 

Section 4 gives practical examples based on freely available and commonly used (Q)SAR 
programs. 

Appendix 1 gives examples (a non-exhaustive list) of (Q)SAR programs available for each of 
the REACH required endpoints. 

Appendix 2 provides links to other guidance documents and tools that give further insights into 
the use of QSARs. 

 
 
 
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2. How to get started with (Q)SARs 

2.1 Definitions 

Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR) 
models – collectively referred to as (Q)SARs – are theoretical models that can be used to 
quantitatively or qualitatively predict the physicochemical, biological (e.g. an (eco)toxicological 
endpoint) and environmental fate properties of compounds from the knowledge of their 
chemical structure.  

A SAR is a qualitative relationship that relates a (sub)structure to the presence or absence of a 
property or activity of interest.  

A QSAR is a mathematical model relating one or more quantitative parameters, which are 
derived from the chemical structure, to a quantitative measure of a property or activity. 

In this document, the chemical for which an endpoint is being estimated by a (Q)SAR model is 
referred to as the target chemical. In other sources, this target chemical may be called a 
‘query compound’ or ‘input structure’. 

2.2 Substance characterisation 

The chemical structure needs to be well defined, following the Guidance on identification and 
naming of substances under REACH. All individual constituents of multi-constituent substances 
should be addressed. The composition of the well-defined substances also has to include 
known impurities (and additives, if any). 

For substances of unknown or variable composition, complex reaction products or biological  
materials (UVCBs), expert judgement is needed to decide whether representative structures 
for the substance can be identified. Stable transformation products should also be identified. A 
suitable structural representation for the chemical (SMILES, mol file, etc.) is usually required. 

2.3 Experimental results 

In general, if reliable and adequate experimental (measured) results are available they should 
prevail over estimated values for the risk assessment and the classification and labelling of the 
substance. 

Therefore, before using (Q)SAR models to predict a specific property of a substance, a critical 
first step is to assemble all of the available information on the substance. There are many 
information sources available for this purpose and which are further explained in the Guidance 
on information requirements and chemical safety assessment - Chapter R.3: Information 
gathering.  

Among those sources, note that the OECD QSAR Toolbox includes one of the largest collections 
of publicly available data, and provides detailed information on how it should be used.  

In addition, most of the (Q)SAR software programs will indicate if their training set1 contains 
experimental results for the target chemical. In this case, the user should prioritise this 
existing experimental data over the predicted data, if there is sufficient indication that the 
experimental data is of good quality. 

1 Dataset used to construct the (Q)SAR model. 

 
 
 
 
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2.4 Conditions for using (Q)SAR results 

Several (Q)SAR models have been integrated in software programs that are straightforward to 
use. However, experience and a thorough understanding of (Q)SARs is needed to verify their 
reliability and adequacy.  

Results of (Q)SARs may be used instead of testing when the conditions set in REACH Annex XI 
(1.3) are met:  

(i) 

a (Q)SAR model where the scientific validity has been established should be used;  

(ii) 

the substance should fall within the applicability domain of the (Q)SAR model;  

(iii) 

the prediction should be fit for the regulatory purpose; and  

(iv) 

the information should be well documented.  

An assessment of the first three points above is expected to be included in the registration 
dossier when substance properties are predicted using (Q)SAR models.  

Section 3 of this practical guide provides detailed information on how to do this assessment. 

2.5 Strategy for using (Q)SAR results 

In general, it is recommended to use (Q)SAR results as part of a weight of evidence 
(WoE) approach or as supporting information. For instance, (Q)SAR predictions can support 
results from tests that have not been performed according to good laboratory practice (GLP) or 
according to accepted guidelines, if those predictions concur with the experimental results. A 
compilation of several predictions with unassignable quality cannot provide an adaptation on 
its own. 

When using (Q)SARs, it is recommended to run all (Q)SAR models available to the 
registrant for the endpoint to be fulfilled, especially when models are independent from each 
other (e.g. the algorithms are based on different descriptors, structural alerts or training sets). 
Agreement among predictions generated from independent and scientifically-valid (Q)SAR 
models increases the confidence in relying on the predictions.  

Predictions that fulfil only some conditions specified in REACH Annex XI (1.3) should be 
disregarded or the reason for providing these predictions should be explained if it is considered 
that there are some benefits to providing these predictions. If the remaining (valid and 
adequate) predictions show small quantitative differences, the most conservative result should 
be chosen for further consideration. If those remaining predictions show significant 
quantitative differences, the registrant must decide if these differences could affect the risk 
assessment (for demonstrating safe use) and/or classification and labelling. 

If the (Q)SAR prediction outcome is a quantitative result, keep in mind that the closer to a 
regulatory threshold the predicted result is, the more accurate the prediction needs 
to be. For instance, if a (Q)SAR model predicts a LC50 (for fish at 96 hours) of 1.2 mg/L then 
this predicted value needs to be fully reliable to ensure that the actual LC50 of the substance is 
not below the CLP regulatory threshold of 1 mg/L.  In contrast, if all (Q)SAR results (and even 
the worst case/over-conservative ones) do not go beyond the regulatory threshold of interest, 
this can support the waiving of the experimental study. 

 
 
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3. How to check a QSAR prediction 

3.1 Is the (Q)SAR model valid? 

As indicated in REACH Annex XI (1.3), the validity of the (Q)SAR model is the first condition to 
be fulfilled to use a (Q)SAR result. To check this, ECHA follows the OECD principles for 
validating (Q)SAR models2. These are five principles dictating that a (Q)SAR model should be 
associated with:  

1.  A defined endpoint: the model must predict the same endpoint that would be 

measured to fulfil the requirements listed in REACH Annexes VII to X. For instance, 
predictions from a model generically predicting “mutagenicity” cannot be accepted as 
such. The model should predict the outcome of a specific test such as “positive”, 
“negative” or “ambiguous” in a bacterial reverse mutation assay (i.e. Ames test 
required in REACH Annex VII, 8.4.1). Another example of an endpoint being too broad 
is a global prediction of a 'repeated dose toxicity lowest observed adverse effect level 
(LOAEL)' from a training set of LOAEL data based on a variety of mode of actions, 
target organs, species or test protocols. This principle links with the adequacy of the 
predictions described later in the document. 

2.  An unambiguous algorithm: the algorithm underlying the model must be available to 
ensure transparency and reproducibility of the calculation. Predictions from a model 
with an algorithm that is not available (to ECHA) to verify its functioning and to 
reproduce the predictions can hardly be accepted. In particular, special precautions are 
needed where non-transparent and difficult to reproduce methods have been used to 
build the (Q)SAR model (e.g. artificial neural networks using many structural 
descriptors). 

3.  A defined domain of applicability: the applicability domain (AD) and the limitations 
of the model have to be described to allow the assessment of the AD for the specific 
prediction (see Section 3.2 of this document). The most common methods for 
describing the AD are to consider the ranges of the individual descriptors and the 
presence of the structural fragments in the training set. Predictions from a model 
without information on the AD cannot be accepted. 

4.  Appropriate measures of goodness-of-fit, robustness and predictivity: this 

principle expresses the need for statistical validation of the model. Statistics on internal 
validation (goodness-of-fit and robustness) and external validation (predictivity) must 
be available. For instance, for regression models, the statistics of the regression model 
could be reported through the correlation coefficient (R2), cross-validated (e.g. from 
leave-one-out procedure) correlation coefficient (Q2) and the standard error of the 
model (s). It can be noted that an R2 below 0.7, a Q2 below 0.5 or an s above 0.3 
should warn the (Q)SAR user of a potential low performance of the (Q)SAR model. The 
external validation should have been done by predicting compounds from an external 
set, i.e. not used for the model development. Statistics on the external validation are 
useful to estimate the uncertainty associated with the predictions.  

5.  A mechanistic interpretation, if possible: reasoning on the causal link between the 

descriptors used in the model and the predicted endpoint adds confidence in the 
reliability of the predictions e.g. a SAR model predicting skin sensitisation can be based 
on structural alerts. If reasoning is provided on how the structural alerts are associated 

https://www.oecd.org/en/publications/q-sar-assessment-framework-guidance-for-the-regulatory-assessment-of-

2 
quantitative-structure-activity-relationship-models-and-predictions-second-edition_bbdac345-en/support-
materials.html 

 
 
 
 
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with skin sensitisation (for example, they enclose electrophilic groups able to bind to 
proteins), then confidence in the prediction would increase due to the possibility to 
interpret the outcome of the model. 

In general, a reference to a well-documented model or a (Q)SAR model reporting format 
(QMRF) attached to the registration dossier is recommended. See the Guidance on information 
requirements and chemical safety assessment, Chapter R.6: (Q)SARs and grouping of 
chemicals (R.6.1.9.1) for more details on the QMRF. The JRC’s (Q)SAR Model Database (QMRF 
Inventory) is intended to provide information on (Q)SAR models submitted to the JRC for peer 
review.  

There is no formal adoption process existing or foreseen for (Q)SAR models under REACH. The 
validity, applicability and adequacy of (Q)SAR models is assessed individually with the 
prediction generated for the target chemical. 

NOTE: A valid (Q)SAR model does not necessarily produce a valid prediction. It is necessary to 
assess whether the substance falls within the applicability domain of the (Q)SAR model, that 
the results are adequate for the purposes of classification and labelling and/or risk assessment, 
and that adequate and reliable documentation of the applied method is provided. 

3.2 Does the substance fall within the applicability domain of the 
(Q)SAR model? 

It is important to verify that the target substance falls within the applicability domain (AD) of 
the model. The concept of AD was introduced to assess the probability of a chemical being 
covered by the (Q)SAR training set. Predictions outside the AD are normally not reliable and 
their use is hard to justify. A practical approach to check if a substance falls into the AD is to 
check the following elements:  

1.  Descriptor domain: as indicated in the previous section, the AD of a model can be 

based on the range of the descriptors of the chemicals that are in the training sets (e.g. 
molecular weight, log Kow…). Therefore, if those ranges have been described, users of 
(Q)SAR models should check whether the target chemical falls into these ranges. It is 
expected that the descriptors follow normal distributions and if there are significant 
deviations, these should be explained. Clusters or other anomalies in the distribution of 
data (concerning one or both the dependent and the independent variables) may 
invalidate the model, and the prediction thereof. 

2.  Structural fragment domain: users of (Q)SAR models should check whether the 

(sub)structures (i.e. functional groups or meaningful fragments) of their target chemical 
are represented in the training set. In addition, the (Q)SAR users should also check 
whether their target chemical has more instances of a given fragment than the 
maximum for all training set compounds (e.g. if the target chemical has three hydroxyl 
groups and any compounds in the training do not have more than two hydroxyl groups, 
then the target chemical may be considered outside the AD).  

3.  Mechanistic and metabolic domains, if possible: to check these points is complex 

but still very valuable to support the reliability of the prediction. One approach could be 
to use the OECD QSAR Toolbox. Within this tool, the “profiling methods” can indicate 
whether the chemical shows relevant mechanisms of action that are not covered by the 
(Q)SAR model (i.e. not covered by its algorithm/mechanistic domain) while the 
“Metabolism/Transformations” – also available in the module “Profiling” of the QSAR 
Toolbox – can indicate metabolites/degradation products that should be considered. 
Significant potential differences in absorption, distribution, metabolism and elimination 

 
 
 
 
 
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between the target chemical and the chemicals in the training set might also invalidate 
the prediction from an otherwise valid model. These considerations are not explicitly 
addressed by the tools and might need to be considered separately from the model. 

4.  Analogues in the training set: having close structural analogues in the training set of 
the model increases the reliability of the prediction. Therefore, if the training set is 
available and the software does not do it automatically, the user should search for close 
analogues either manually (for small sets) or with the support of IT tools that provide 
structural search functionalities. 

5.  Accuracy of model predictions for analogues: if there are substances similar to the 
target substance, where the experimental results for the endpoint of interest are known 
(e.g. analogues in the training sets, in the validation sets or from any other source), 
the model can be run to predict these analogues and compare these predictions with 
the experimental results (to see how accurate the model is for these similar 
substances). 

6.  Considerations for specific substances: firstly, and as mentioned in Section 2.2, 

special considerations should be given to UVCBs, multi-constituents, additives, 
impurities, metabolites and degradation products. Secondly, most of the (Q)SAR 
models are developed for organic chemicals and do not address the specificity of some 
types of chemicals such as ionisable substances (e.g. salts, weak acids and bases), 
large molecular weight substances (e.g. polymers), potentially hydrolysable substances 
(e.g. esters, carbamates), surfactants (e.g. hydrocarbon chain with hydrophilic head) 
and isomers (e.g. stereoisomers, tautomers). 

3.3 Is the prediction adequate for the purpose of C&L and/or risk 
assessment? 

For a (Q)SAR prediction to be adequate, it should be not only reliable (i.e. derived from a valid 
(Q)SAR model and within its applicability domain), but also relevant for regulatory decision.  

The adequacy of the prediction for the purposes of classification and labelling (C&L) and/or risk 
assessment is very much endpoint-dependent. Additional information may be needed to assess 
the adequacy of the prediction in the context of a regulatory decision.  

Therefore, the validity (are the five OECD principles on scientific validity of a model fulfilled?), 
applicability (can reliable predictions be expected if the model is applied to the target 
substance?) and relevance (is the information which is needed for the risk assessment and/or 
C&L generated?) need to be assessed for each individual prediction. 

 
 
 
 
 
 
 
 
 
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Figure 1: Criteria for identifying an adequate (Q)SAR model 

C&L and risk assessment are based on well-defined requirements in terms of tests (and 
endpoints), thresholds and uncertainty analysis.  

Therefore, results from (Q)SAR models should be equivalent to results obtained from the 
required experimental test. 

Some examples of inadequacy are listed here: 

• 

(Q)SAR models that are able to fully cover the complexity of higher-tier endpoints do 
not exist yet (e.g. repeated dose toxicity or reproductive toxicity). So far, the use of 
(Q)SARs as standalone information for these endpoints cannot be accepted. For 
instance, repeated dose toxicity tests provide many data points for effects in specific 
tissues (specific target organ toxicity) and it is not only the no observed adverse effect 
level (NOAEL) that matters. Indeed, effect results are needed to trigger other tests such 
as reproductive toxicity or for specific target organ toxicity single exposure/repeated 
exposure (STOT SE/RE) classification. 

• 

If a quantitative outcome is needed (e.g. to derive a derived no-effect level (DNEL) or 
for classification) and the model only gives qualitative predictions (e.g. a negative or 
positive result), then the model is probably not adequate for the purpose. 

•  Uncertainty associated with predictions close to regulatory thresholds needs to be 

examined cautiously. For instance, if the predicted oral rat LD50 does not go beyond 
the threshold for classification but the standard error of the model and/or the error of 
the estimate is larger than this gap, then the prediction is probably not adequate. 

•  As required for an experimental bacterial reverse mutation assay (Ames test), the 

training set of the (Q)SAR model should include experimental results that cover the five 
bacterial strains in the presence and absence of metabolic activation (S9). This 
information has to be included in the documentation of the model and ideally also in the 
prediction report. 

• 

(Q)SAR models for fish toxicity where the experimental results for the chemicals in the 
training set have been performed according to OECD test guideline 204 (14-day 
studies) cannot be used to predict long-term toxicity to fish because the test duration is 
too short. 

 
 
 
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• 

(Q)SAR models predicting the biodegradation half-life of a compound cannot be used as 
a standalone replacement of a simulation test as they do not cover the need to identify 
the degradation products (REACH Annex IX, 9.2.3 requirements). 

3.4 How to report a (Q)SAR prediction in IUCLID 

As discussed previously in Sections 2.4, 3.1, 3.2 and 3.3, the following information has to be 
reported in IUCLID: 

• 

Information on the validity of the (Q)SAR model; 

•  Verification that the substance falls within the applicability domain of the (Q)SAR 

model; and 

•  Assessment of the adequacy of the results for the purposes of classification and 

labelling and/or risk assessment. 

These three pieces of information should be compiled according to the (Q)SAR model reporting 
format (QMRF) and the (Q)SAR prediction reporting format (QPRF). 

These formats are respectively available in Chapters R.6.1.9.1 and R.6.1.10.1 of the Guidance 
on information requirements and chemical safety assessment. 

The QMRF is normally provided by the model developer while the QPRF is prediction-specific 
and should be prepared by the registrant using the information in the software report and 
manual. 

The endpoint study record for a (Q)SAR prediction has to be reported in IUCLID as follows. 

Block “Administrative data” 

•  The field “Type of information” states “(Q)SAR”. 

•  The field “Adequacy of study” states whether the estimate is used as a key study, as a 

supporting study or in a weight of evidence approach. 

•  The field “Reliability” states the reliability score, bearing in mind that for (Q)SAR 

predictions it should normally be a maximum of 2. 

 
 
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•  The field “Justification for type of information” can be used to report information 

equivalent to the one listed in the QMRF and QPRF. A free text template to help fill this 

field is available by clicking on the 

 button and selecting “Type ‘QSAR prediction’”. 

•  Otherwise, the field “Attached justification” can be used to attach the QMRF and the 

QPRF. 

Block “Data source” 

•  The field “Title” is used to state the name and version of the program and/or the title of 
the publication. The field “Year” needs to include the year when the software program 
was released or when the (Q)SAR model was published. Additionally, the “Bibliographic 
source” has to give information on the (Q)SAR model. 

•  The field “Data access” provides information on the accessibility of the model. 

Block “Materials and methods” 

Either the field “Guideline” (in table “Test guideline”) or the field “Principles of method if other 
than guideline” should be filled in. 

• 

In the field “Guideline”, the user can select “other guideline” and provide text in the 
adjacent field. This text could, for instance, refer to the REACH Guidance on QSARs R.6 
or to the test guidelines used to generate the data for the training set. 

•  Otherwise, in the field “Principles of method other than guideline”, the user could 

provide further details/references on the (Q)SAR model. 

 
 
 
 
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13 

Block “Test materials” 

•  The field “Test material information” should include a link to the substance for which 

the prediction was made. 

•  The SMILES notation should be reported in the field “Specific details on test material 

used for the study”. 

NOTE: the registered substance may contain more than one constituent and/or impurities. In 
such cases, it may be useful to prepare an individual endpoint study record and a (Q)SAR 
prediction reporting format (QPRF) for each constituent/impurity to be able to address each 
chemical separately (recommended if constituents have different properties and therefore 
different models, assessments, etc. have to be applied). 

Block “Results and discussion” 

•  The (Q)SAR-predicted result should be reported in the structured result fields. This 
allows the user to transfer information automatically from these result fields to the 
chemical safety report (CSR) when the IUCLID report generator is used. The list of 
fields to be filled in the “Results and discussions” block will vary depending on the 
endpoint.  

Therefore, we recommend consulting the manual on “How to prepare registration and 

PPORD dossiers” for instructions on how to fill in the results. 

 
 
 
 
 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1   

• 

If it is not possible to fill in all the structured result fields required to pass the technical 
completeness check, then the fields “Remarks” (at the right end of the table) or the 
field “Any other information on results incl. tables” could be used instead. 

Endpoint study summary 

In addition, it is advisable to create an endpoint study summary when more than one endpoint 
study record is available and to provide the overall assessment on the particular endpoint. This 
would enable the automatic transfer of this information to the CSR when the IUCLID report 
generator is used. 

 
  
 
 
 
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4. Practical examples 

This section describes how to assess the reliability of QSAR predictions. The assessment 
depends on the software and on the target endpoint. The examples used in this section are 
based on computer programs that are widely used and freely available. The fact that these 
programs are used in these examples is not an endorsement from ECHA.  

Usually, the use of QSARs is limited to experts. With these practical examples, the aim is to 
allow less experienced people to use and interpret QSARs for at least some endpoints (like in 
the following examples). 

The programs used in the examples can predict several endpoints. However, only one endpoint 
per program (corresponding to one REACH requirement) has been used for each example. In 
most cases, predictions for different endpoints from the same program are reported (and can 
be assessed) in a similar way. 

The four endpoints predicted in the following examples are log Kow, ready biodegradability, 
short-term toxicity to fish and acute mammalian toxicity. Those endpoints have been selected 
as representatives of REACH Annexes VII or VIII requirements for physicochemical properties, 
environmental fate, ecotoxicological and toxicological information. 

4.1 Log Kow (EPI Suite) 

a)  Introduction 

Partition coefficient n-octanol/water is a REACH requirement for all substances produced or 
imported above one tonne/year (REACH Annex VII). It is commonly expressed as a logarithmic 
value called log Kow or log P. 

Many QSAR models are available to predict log Kow. KOWWIN – which is part of EPI Suite – is 
one of the most commonly used programs. KOWWIN uses a "fragment constant" method to 
predict Kow. Fragment constant methods divide the chemical structure into smaller structural 
fragments (atoms or larger functional groups). Each fragment is associated with a pre-
assigned coefficient value called fragment constants. The predicted log Kow value is obtained 
by summing all the fragment constants appearing in the chemical structure.  

At the time of writing this manual, the current version of EPI Suite™ was version 4.11, which 
has been used to prepare this example. 

Link to the (Q)SAR program: http://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-
program-interface 

 
 
 
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b)  How to check the reliability of the prediction 

Information on 
the QSAR model 
Description of the 
algorithm 

Statistics 
(goodness-of-fit 
and robustness) 
Information on 
the applicability 
domain 
General 
applicability 
domain (chemical 
classes 
covered/not 
covered by the 
model) 

Where to find this 
information 
Available in the KOWWIN 
Help3, in section 
“Methodology”. 
Available in the KOWWIN Help, 
in section “Accuracy & 
Domain”. 
Where to find this 
information 

Available on the front page of 
EPI Suite (“The intended 
application domain is organic 
chemicals. Inorganic and 
organometallic chemicals are 
generally outside the 
domain.”) and in the KOWWIN 
Help, in sections “Ionisation” 
and “Zwitterion 
Considerations”. 

Descriptor domain  Available in the KOWWIN Help, 

Structural 
fragment domain 

in section “Accuracy & 
Domain”.  

The KOWWIN results window 
lists the fragments (and their 
numbers) found in the target 
chemical. 

Appendix D of the KOWWIN 
Help gives the maximum 
number of fragments that 
occur in any individual 
compound of the training set. 

How to use this information 

See Section 3.1 of this practical guide. 

See Section 3.1 of this practical guide. 

How to use this information 

As indicated in Section 3.2 point 6 of this 
practical  guide,  special  attention  should 
be  paid  for  some  types  of  chemicals. 
KOWWIN includes some “corrections” for 
ionisable  and  zwitterionic  substances  to 
refine  the  low  predictivity  for  these 
substances. 

The  user  should  check  that  the  target 
chemical is in the molecular weight range 
of the compounds in the training set (i.e. 
between 18 and 720). 
The  user  should  check  whether  the 
number  of  each  fragment  found  in  the 
target  chemical  (column  “NUM”  in  the 
KOWWIN  results  window)  does  not 
exceed  the  maximum  number  of  this 
fragment    that  occurs  in  any  individual 
compound  of  the  training  set  (column 
“Training  set/Max”  of  Appendix  D  of  the 
KOWWIN Help). 

Notes on specific substructures: 
-  For  some  substructures,  KOWWIN 
reports  correction  factors.  In  this  case, 
the user should do the same verification 
described  above  for  the  numbers  of 
fragments. 
- For some substructures, the coefficient 
has been estimated (if this is the case, it 
will be reported as a note in the KOWWIN 
results  window).  It  should  be  kept  in 
mind 
this  estimation  brings 
additional  uncertainty  to  the  overall 
prediction. 

that 

3 KOWWIN Help can be accessed by clicking on the tab “Help” at the top of the KOWWIN window. 

 
 
 
 
 
 
 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

17 

How to use this information 

The user should check whether there are 
close  analogues  in  the  training  and 
validation  sets  (either  manually  in  the 
Excel  file  or  automatically  with  the  SDF 
file).  If  there  are  close  analogues,  the 
user  should  check  how  well  they  have 
been predicted by KOWWIN. 

Training set and 
validation set 

Where to find this 
information 
The training and validation sets 
can be downloaded through 
links given at the bottom of 
the section “Accuracy & 
Domain” of the KOWWIN Help. 

The first link provides an Excel 
file with chemical names, 
experimental and estimated 
values of log Kow: 
http://esc.syrres.com/interkow
/KowwinData.htm. 

The second link provides an 
SDF file with the same 
information as the Excel file 
plus structural information4: 
http://esc.syrres.com/interkow
/EpiSuiteData_ISIS_SDF.htm  

c)  Additional information on EPI Suite 

•  The EPI Suite Help files do not have exactly the same table of contents between the 

various estimation programs (e.g. between KOWWIN, BIOWIN…). Therefore, the user 
should go through each specific Help file to identify the information needed to check the 
reliability of the prediction (as done for KOWWIN in the table above). 

•  Several programs of EPI Suite display the experimental result of the target chemical if it 
is part of the training or validation sets. For instance, KOWWIN contains a database of 
approximately 13 500 experimental log Kow and if an experimental log Kow value is 
available for the target chemical it will be displayed in the “Results” window (see green 
rectangle in the figure below). In this case, it is recommended to report this 
experimental data and the EPI Suite estimate in two different endpoint study records. 

4  Many  tools  are  available  to  read SDF  files.  These  tools  allow  users  to  visualise  the chemicals,  to search for closest 
structural analogues or to search for specific substructures. Some of these tools are freely available (e.g. Knime or the 
OECD QSAR Toolbox). 

 
 
 
 
 
 
 
 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1   

4.2 Ready biodegradability (VEGA) 

a)  Introduction 

Ready biodegradability is a REACH requirement for all substances produced or imported above 
one tonne/year (REACH Annex VII). The main outcome of a ready biodegradability test is the 
classification of the chemical either as “readily biodegradable” or as “not readily 
biodegradable”. 

The VEGA platform contains several QSAR models for various endpoints. One of these models 
predicts ready biodegradability (model developed by the Istituto di Ricerche Farmacologiche 
Mario Negri). This model is based on structural alerts.  

Four sets of substructures (i.e. fragments) are included in this model and those sets are 
classified as “non-readily biodegradable”, “possible non-readily biodegradable”, “readily 
biodegradable” and “possible readily biodegradable”. A target chemical is always considered 
non-biodegradable if at least one fragment related to “non-readily biodegradability” is found. 

At the time of writing this manual, the current version of VegaNIC is 1.1.0, which has been 
used to prepare this example. 

Link to the (Q)SAR program: https://www.vegahub.eu/ 

b)  How to check the reliability of the prediction 

Information on the 
(Q)SAR model 
Description of the 
algorithm 

Statistics (goodness-
of-fit and Robustness) 

Where to find this 
information 
Available in the “Guide 
to Ready 
Biodegradability 
Model”5 (in Sections 
1.2, 1.4 and 1.5). 
Available in the “Guide 
to Ready 
Biodegradability 
Model” (in Section 
1.6). 

How to use this information 

See Section 3.1 of this practical guide. 

See Section 3.1 of this practical guide. 

5 This guide can be downloaded from the VEGA program by clicking on the tab “SELECT”, then on the tab “Environ”, then 

on the question mark icon 

 adjacent to the “Ready Biodegradability model (IRFMN)”. 

 
 
 
 
 
 
 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

19 

Information on the 
applicability 
domain 
General applicability 
domain (chemical 
classes covered/not 
covered by the 
model) 

Where to find this 
information 

Partly available in the 
Vega prediction 
report. 

Descriptor domain 

Not applicable as the 
model is based only 
on 
fragments/structural 
alerts 

Structural fragment 
domain 

Available in the “Guide 
to Ready 
Biodegradability 
Model” and in the 
Vega prediction 
report. 

How to use this information 

If less than three “golden stars” are 
displayed in Section 1 of the Vega report, 
this indicates that at least one issue has 
been detected for the prediction and that, 
therefore, the prediction might not be 
reliable. In this case, the user should 
investigate the issues thoroughly. Note 
that the issues are further detailed in 
Section 3.2 of the VEGA report. 

In addition, as indicated in Section 3.2 
point 6 of this practical guide, special 
attention should be paid to some types of 
chemicals. 
The training set of the model is based on 
tests performed according to the OECD 
301C guideline. Some substances led to 
unreliable results using this guideline (e.g. 
low water soluble, volatile or absorptive 
substances). Therefore, if the target 
substance has low water solubility, a high 
vapour pressure or a high log Koc, then the 
user should keep in mind that the 
prediction might be erroneous. 

In addition, the user should verify if there 
are close analogues in the training set 
(especially if the target substance has a 
large molecular weight). 
If the target chemical does not contain any 
of the fragments listed in Sections 1.4 and 
1.5 of the model’s guide, then no 
prediction is given by the tool. 

In addition, in Section 3.2 of the Vega 
Prediction report, if the “Atom Centred 
Fragments similarity check” gives an ACF 
index <1, this would indicate that there is 
at least one atom centred fragment of the 
target chemical which has not been found 
in the compounds of the training set (or 
which is rarely present). In this case, the 
user should determine if these missing/rare 
fragments (listed in Section 4.1 of the 
report, if any) could have an impact on 
biodegradation.  

 
 
 
 
 
 
 
20 

Practical Guide - How to use and report (Q)SARs 3.1.1   

Training set and 
validation set 

Where to find this 
information 
Available in the file 
called “Training set 
(plain text with 
SMILES)”6. 

How to use this information 

This file contains the SMILES of the training 
set  compounds  and  of 
test  set 
compounds. 

the 

The  VEGA  report  displays  the  most  similar 
compounds found in the training set and in 
the test set in Section 3.1. The user should 
check 
these 
compounds are closely similar to the target 
chemical and if their experimental outcome 
agrees with the predicted one. 

this  section  whether 

in 

c)  Additional information on VEGA 

If there is an experimental result for the target chemical in the training set or in the test set, 
this data will be displayed in the VEGA report (in Section 1 of the report). In this case, the user 
should look for further information about this test – e.g. by searching for this experimental test 
within the QSAR Toolbox – and report the details of this test in an endpoint study record only 
dedicated to this experimental study. 

6 This file can be downloaded from the VEGA program by clicking on the tab “SELECT”, then on the tab “Environ”, then 

on the question mark icon 

 adjacent to the “Ready Biodegradability model (IRFMN)”. 

 
 
 
 
 
 
 
 
 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

21 

4.3 Short-term toxicity to fish (ECOSAR) 

a)  Introduction 

Short-term toxicity testing on fish is a REACH requirement for all substances produced or 
imported above 10 tonnes/year (REACH Annex VIII). The endpoint to be derived is the LC50, 
which is the concentration lethal to 50 % of the fish. 

The ecological structure activity relationships (ECOSAR) class program is a collection of QSAR 
models estimating aquatic toxicity, including short-term toxicity to fish. Most of the ECOSAR 
models are based on the relationships between log Kow and toxicity (LC50 or EC50) and take 
into account different structural classes. 

NOTE: Regarding EPI Suite and ECOSAR, ECOSAR is developed and maintained as a 
standalone program. Even if the latest version of ECOSAR (v.1.11) has been included in the 
latest version of EPI Suite (v.4.11), it is still recommended to use the ECOSAR standalone 
program because it will inform the user if an experimental value is available for the target 
chemical while the ECOSAR integrated in EPI Suite does not have this functionality. 

Link to the (Q)SAR program: http://www.epa.gov/tsca-screening-tools/ecological-structure-
activity-relationships-ecosar-predictive-model 

b)  How to check the reliability of the prediction 

Information on the 
(Q)SAR model 
Description of the 
algorithm 

Statistics (goodness-
of-fit and 
robustness) 

this 

find 

to 
Where 
information 
Available in the ECOSAR Help7, 
in  section  “Interpreting  QSAR 
Class  Ref  Docs”  and  in  each 
“QSAR  Equation  Document”  of 
the various chemical classes. 
Available  in  the  ECOSAR  Help, 
equation 
“QSAR 
each 
in 
document”  of 
the  various 
chemical classes. 

How to use this information 

See  Section  3.1  of  this  practical 
guide. 

(i.e. 

correlation 

See  Section  3.1  of  this  practical 
guide. For instance, the user should 
check that: 
R2 
- 
or 
determination coefficient) is not too 
low (e.g. not below 0.7), 
-  The  data  points  are  not  too  few. 
This 
important  as 
several ECOSAR classes contain only 
one  or  few  data  points  which  can 
lead to unreliable predictions. 
- The linear regression is not built on 
scattered data points. 

is  especially 

7 ECOSAR Help can be accessed by clicking on the tab “Help” at the top of the ECOSAR window. 

 
 
 
 
 
 
 
 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1   

Where to find this 
information 

How to use this information? 

Information on the  
applicability 
domain 
General applicability 
domain (chemical 
classes covered/not 
covered by the 
model) 

Available  in  the  ECOSAR  Help, 
in  sections  “Chemicals  that 
profiled”, 
should 
“Surfactants”  and 
“Special 
ECOSAR classes/dyes”. 

not 

be 

The  user  should  check  whether  its 
chemical is one of those “that should 
not be profiled” with ECOSAR or if it 
should  be  predicted  using  a  special 
ECOSAR class. 

In addition, it should be kept in mind 
that most of the ECOSAR models are 
based  on  the  relationships  between 
log Kow and toxicity (LC50 or EC50) 
addressing  the  uptake  of  chemicals 
through the aqueous phase.  

For  very  hydrophobic  or  very 
sorptive  substances,  the  uptake 
from food can also be an important 
exposure pathway.  

Moreover,  other  properties  of  the 
substance can trigger specific modes 
of action (e.g. the substance is likely 
to  be  more  reactive  if  it  is  protein 
binding).  

some 

chemical 
excess 

Finally, it should also be kept in mind 
that 
classes 
toxicity 
expressing 
(compared  to  the  baseline  toxicity 
model)  have  not  yet  been  included 
in the ECOSAR program. 
The  user  should  check  that  the 
molecular weight (MW) of the target 
chemical does not exceed 1 000. The 
MW  of  the  target  chemical 
is 
indicated  in  the  ECOSAR  results 
window. 

If  the  log  Kow  of  the  compound 
exceeds  the  maximum  log  Kow  of 
the  class-specific  model  (e.g.  for 
Fish 96 hour LC50, the maximum log 
Kow  varies  between  class  models 
from 2.6 to 8.2, often being 5) or if 
the  predicted  effect  (e.g.  Fish  96 
hour  LC50)  exceeds  the  water 
solubility of the compound, then the 
prediction  might  be  unreliable  and 
long-term  toxicity  data  might  be 
more 
Detailed 
instructions  on  how  to  check  these 
points is given in section c) below. 

appropriate. 

Descriptor domain 

Available  in  the  ECOSAR  Help, 
equation 
in 
“QSAR 
each 
document”  of 
the  various 
chemical  classes  and  in  the 
ECOSAR results window. 

 
 
 
 
 
 
 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

23 

Structural fragment 
domain 

Can  be  determined  from  each 
“QSAR  equation  document”  of 
the  various  chemical  classes 
(except  for  the  “dyes”  and 
“surfactants” special classes). 

Each  “QSAR  equation  document” 
gives the training set of the specific 
class  model.  In  these  training  sets, 
the  CAS  numbers  and  the  chemical 
names  are  provided  (if  not  claimed 
confidential (CBI)).  

This requires manual work since the 
training sets are not in a structurally 
searchable format.  

Software programs8 are available to 
help  the  user  derive  the  chemical 
structures if needed. In addition, the 
whole  structural  domain  of  the 
model cannot be determined if there 
are  CBI  compounds  in  the  training 
set. 
How to use this information 

The user should check whether there 
are  close  analogues  in  the  training 
set of the specific class model (based 
on  the  CAS  numbers  or  on  the 
chemical names provided).  

If  there  are  close  analogues,  the 
user should compare their predicted 
and experimental values. 

Training set and 
validation set 

Where to find this 
information 
The training sets of all  models 
(except  the  special  classes  for 
dyes  and  surfactants)  are 
available 
“QSAR 
equation  document”  of  each 
chemical class.  

from 

the 

However, 
the 
some 
compounds  in  the  training  set 
have  been  kept  confidential 
(CBI). 

of 

Validation  (test)  sets  do  not 
exist in ECOSAR.9 

c)  Additional information on ECOSAR 

• 

In ECOSAR, all predictions (except for surfactants and dyes) are based on log Kow. By 
default, ECOSAR estimates Kow using KOWWIN. However, if users have a reliable 
measured log Kow they should insert it in the data entry screen (see screenshot below). 
This value will be taken into account by the model and will reduce the uncertainty of the 
prediction. 

8 Several software programs allow users to derive the chemical structure from the CAS number or from the chemical 
name. Some of these programs are freely available (e.g. Chemspider or the OECD QSAR Toolbox). 
9 However, there are several peer-reviewed publications assessing the external performance of ECOSAR. 

 
 
 
 
 
 
 
 
 
 
 
24 

Practical Guide - How to use and report (Q)SARs 3.1.1   

• 

If there is an experimental result for the target chemical in the training set then this data 
will  be  displayed  in  the  ECOSAR  results  window  (see  “Available  Measured  Data  from 
ECOSAR Training Set” in the figure below). In this case, it is recommended to report this 
experimental data and the ECOSAR estimate in two different endpoint study records. 

• 

If the compound has been allocated to a specific class (e.g. “Aldehydes, mono” class), 
then the effect level of this class should be taken into account and not only the one 
from the “Neutral organics” class (i.e. baseline toxicity potential). In general, if the 
program identifies several classes, it is recommended to use the most conservative 
effect level from any of these classes (and to consider the potential synergistic toxicity 
effect of these various classes). 

•  Each of the “QSAR equation documents” is class-specific and some contain more 
information than others under the titles “APPLICATION” and “LIMITATIONS” (see 
examples below). Therefore, users should carefully read the “QSAR equation document” 
of the specific class/endpoint for which they want to perform a prediction. 

 
 
 
 
 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

25 

•  As indicated in the table from section b) (see descriptor domain), if the log Kow of the 

compound exceeds the maximum log Kow of the class-specific model or if the predicted 
effect value exceeds the water solubility of the compound, then the prediction might be 
unreliable (see ECOSAR results window below). 

•  Ecotoxicity chronic values (ChV) can be predicted with ECOSAR. However, users should 

pay attention to the following points: 

o 

o 

o 

The (Q)SAR models available for predicting these chronic values are often built on 
small to very small training sets. 

The chronic value (ChV) is defined as the geometric mean of the no observed effect 
concentration (NOEC) and the lowest observed effect concentration (LOEC). 
However, under REACH, NOECs are the effect concentrations used to assess long-
term toxicity data to aquatic organisms. A proxy to derive the NOEC is to divide the 
ChV by √2. 

Acute-to-chronic ratios (ACRs) are used by ECOSAR when measured data are lacking 
within a class. Such predictions are flagged with an exclamation mark (!) in the 
ECOSAR results window (see screenshot below) and should be considered with 
caution. 

 
 
 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1   

4.4 Acute toxicity to rats (T.E.S.T.) 

a)  Introduction 

Acute toxicity by oral route is a REACH requirement for all substances produced or imported 
above one tonne/year (REACH Annex VII). The preferred test species according to the OECD 
test guidelines is the rat, and the endpoint to be derived is the LD50 (generally expressed in 
mg/kg body weight). 

Most of the software programs that predict acute oral toxicity are commercial except T.E.S.T. 
(Toxicity Estimation Software Tool), which is made freely available by the US EPA. This tool 
offers four different methods to predict acute oral toxicity to rats: hierarchical, FDA, nearest 
neighbour and consensus methods.  

The consensus method predicts the toxicity simply by taking an average of the predicted 
toxicities from the three other methods (hierarchical, FDA and nearest neighbour methods).  

This consensus method should be the preferred one to use as it achieved the best results for 
prediction accuracy and leverage compared to the three other methods. To check the reliability 
of the predictions coming from this consensus method, the user should assess the reliability of 
the three other methods. Therefore, the information given in the following table addresses all 
of these four methods. 

At the time of writing this manual, the current version of T.E.S.T. is 4.1, which has been used 
to prepare this example. 

Link to the (Q)SAR program: https://www.epa.gov/comptox-tools/toxicity-estimation-
software-tool-test 

b)  How to check the reliability of the prediction 

Information on 
the  (Q)SAR 
model 
Description of the 
algorithm 

Where 
to 
information 

find 

this 

How to use this information 

Available in the  T.E.S.T. User’s 
Guide10, in Sections 1.2 and 2.2 
called “QSAR methodologies”. 

The four methods are transparently 
described in Section 2.2 of the User’s 
Guide. It should be noted that none of 
them involve mechanistic 
interpretations. 

Concerning the hierarchical and FDA 
methods, they are based on clustering 
and genetic algorithms that lead to 
equations (and descriptors) that vary 
depending on the target chemical. 

Concerning the nearest neighbour 
method, the predicted toxicity is the 
average of the toxicities of the three 
most similar chemicals (structural 
analogues) in the training set. 

10 This User’s Guide can be accessed by clicking on the tab “Help” at the top right of the T.E.S.T. window. 

 
 
 
 
 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

27 

Statistics 
(goodness-of-fit 
and robustness) 

Available in the  T.E.S.T. User’s 
Guide, in Sections 2.2.1, 2.2.2, 
2.3.1 and 4.4.1. 

In Section 4.4.1 of the User’s Guide, it is 
written  that  R2  is  less  than  0.6  for  the 
hierarchical,  FDA  and  nearest  neighbour 
methods and that R2−R0
R2
for all of the four methods.  

 is more than 0.1 

2

Information on 
the  applicability 
domain 
General 
applicability 
domain (chemical 
classes 
covered/not 
covered by the 
model) 
Descriptor domain 

conditions 

Therefore,  those  methods  do  not  satisfy 
the 
for  an  acceptable 
predictive  power  as  indicated  in  Section 
2.3.1 of the T.E.S.T. User’s Guide. 

In addition, it is stated in Section 4.4.1 
of the User’s Guide that “The prediction 
statistics for this endpoint were not as 
good as those for the other endpoints. 
This is not surprising since this endpoint 
has a higher degree of experimental 
uncertainty and has been shown to be 
more difficult to model than other 
endpoints”. 

Where 
to 
information 

find 

this 

How to use this information 

Available in the T.E.S.T. User’s 
Guide, in Section 3.4. 

Information can be found in the 
T.E.S.T. User’s Guide (Sections 
2.1 and 2.2.1, 2.2.2). 

For instance, in Section 3.4 of the User’s 
Guide,  it  says  that  “salts,  undefined 
isomeric mixtures, polymers, or mixtures 
were removed [from the training set]”.  

Therefore,  those  types  of  substances 
should not be predicted with T.E.S.T. 
For the hierarchical and FDA methods, 
797 descriptors can be used in the 
model equation depending on the target 
chemical.  

These methods give a prediction only if 
the target chemical is within the ranges 
of descriptor values for the chemicals in 
the cluster (for the descriptors 
appearing in the cluster model). 

Concerning the nearest neighbour 
method, the descriptor differences 
between the target chemical and its 
structural analogues are not accounted 
for by T.E.S.T. 

To increase the confidence in the 
predictions given by these three 
methods, the user could potentially 
check that the target chemical is within 
the ranges of log Kow and molecular 
weight of the compounds in the cluster 
(or of the three nearest neighbours). 

 
 
 
 
 
 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1   

Structural 
fragment domain 

Information related to that can 
be found in the  T.E.S.T. User’s 
Guide 
(Sections  2.2.1  and 
2.2.2). 

The hierarchical and FDA methods give a 
prediction only if the compounds in the 
cluster have at least one example of 
each of the fragments contained in the 
target chemical. 

Concerning the nearest neighbour 
method, the structural differences 
between the target chemical and its 
structural analogues are not accounted 
for by T.E.S.T.  

Therefore, users should check this 
visually from the T.E.S.T. report. 
How to use this information 

In the results report of each method, for 
the  predictions  performed  on 
the 
analogues  in  the  validation  and  training 
sets, the user should check: 
•  whether  these  predicted  values  are 
close to the experimental values; and 
•  whether  the  mean  absolute  error 
(MAE) for these analogues is not more 
than  the  MAE  of  the  entire  set  (this 
would  be  displayed  as  a  red  cell 
instead of a green cell). 

Training set and 
validation set 

this 

find 

Where 
to 
information 
The results report of each of the 
four  methods  displays 
the 
predictions for the most similar 
chemicals  in  the  validation  set 
(i.e.  prediction  set  or  external 
test  set) 
the 
predictions for the most similar 
chemicals in the training set. 

followed  by 

can 
as  SDF 

In  addition,  the  training  and 
be 
validation 
sets 
downloaded 
files 
through  the  link  given  at  the 
bottom of this web page: 
https://www.epa.gov/comptox
-tools/toxicity-estimation-
software-tool-test 

c)  Additional information on T.E.S.T. 

• 

If there is an experimental result for the target chemical in the training set or in the 
validation set (i.e. prediction set or external test set), then this data will be displayed in 
the T.E.S.T. results report (see “aNote” in the screenshot below). The user can check 
this experimental value by clicking on the name of the source (in the example below by 
clicking on “ChemidPlus”). In this case, it is recommended to report this experimental 
data and the T.E.S.T. estimate in two different endpoint study records. 

•  Some methods (hierarchical, FDA and group contribution methods) give the “Prediction 
interval” (i.e. 90 % confidence interval) in the results report. When using these, the 

 
 
 
 
 
 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

29 

user should check that this interval does not overlap with a regulatory threshold (e.g. 
CLP, persistent, bioaccumulative and toxic (PBT) or risk assessment thresholds). 

 
 
30 

Practical Guide - How to use and report (Q)SARs 3.1.1   

Appendix 1. QSAR models related to REACH endpoints 

The QSAR computer programs listed in this appendix are widely known and are given to inform 
REACH registrants on the QSAR models availability for each of the REACH endpoints. However, 
it constitutes neither an exhaustive list of available programs nor a list of regulatory validated 
QSAR models. So far, most of the toxicological and ecotoxicological information required under 
REACH can rarely be fulfilled with QSAR predictions alone. 

a.  Physicochemical endpoints 

Endpoint 

Software tool 

Models/Modules 

Melting/freezing 
point 
Boiling point 

Relative density 
Vapour pressure 

Surface tension 
Water solubility 

Partition 
coefficient (log 
Kow) 

Flash point 
Dissociation 
constant 

Viscosity 

EPI Suite (US EPA) 

MPBPVP 

EPI Suite (US EPA) 
T.E.S.T. (US EPA) 
ACD/Percepta 
(ACD/Labs) 
T.E.S.T. (US EPA) 
EPI Suite (US EPA) 
T.E.S.T. (US EPA) 
ACD/Percepta 
(ACD/Labs) 
T.E.S.T. (US EPA) 
EPI Suite (US EPA) 
T.E.S.T. (US EPA) 
ACD/Percepta 
(ACD/Labs) 
ADMET Predictor 
(Simulations Plus) 
Discovery Studio 
(Accelrys) 
EPI Suite (US EPA) 
VEGA (IRFMN) 
ACD/Percepta 
(ACD/Labs) 
ADMET Predictor 
(Simulations Plus) 
JChem (ChemAxon) 
T.E.S.T. (US EPA) 
Danish QSAR Database 
(DTU) 
ACD/Percepta 
(ACD/Labs) 
ADMET Predictor 
(Simulations Plus) 
JChem (ChemAxon) 
T.E.S.T. (US EPA) 

MPBPVP 
Normal boiling point 
Boiling Point/Vapor Pressure 
Module 
Density 
MPBPVP 
Vapor pressure at 25°C 
Boiling Point/Vapor Pressure 
Module 
Surface tension at 25°C 
WSKOW and WATERNT 
Water solubility at 25°C 
Aqueous Solubility Module 

Physicochemical and 
Biopharmaceutical Module 
ADMET Descriptors 

KOWWIN 
LogP Models 
LogP Module 

Physicochemical and 
Biopharmaceutical Module 
LogP/logD predictor 
Flash point 
pKa from ACD/Labs 

pKa Module 

Physicochemical and 
Biopharmaceutical Module 
pKa predictor 
Viscosity at 25°C 

Free or 
Commercial 
Free 

Free 
Free 
Commercial 

Free 
Free 
Free 
Commercial 

Free 
Free 
Free 
Commercial 

Commercial 

Commercial 

Free 
Free 
Commercial 

Commercial 

Commercial 
Free 
Free 

Commercial 

Commercial 

Commercial 
Free 

 
 
 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

31 

b.  Environmental fate and pathways endpoints 

Endpoint11 

Software tool 

Models/Modules 

Hydrolysis 
Ready biodegradability  Danish QSAR 

EPI Suite (US EPA) 

Database (DTU) 
EPI Suite (US EPA) 
VEGA (IRFMN) 
CATALOGIC (LMC) 
Discovery Studio 
(Accelrys) 
Meta-PC (MultiCASE)  Aerobic Microbial 

HYDROWIN 
Not Ready Biodegradability 
model from DTU 
BIOWIN and BioHCwin 
IRFMN model 
Several OECD 301 models 
Aerobic Biodegradability 
model 

Bioaccumulation in 
aquatic species 

Adsorption/desorption 
screening 

EPI Suite (US EPA) 
T.E.S.T. (US EPA) 
VEGA (IRFMN) 

CASE Ultra 
(MultiCASE) 
CATALOGIC (LMC) 
EPI Suite (US EPA) 

Biodegradation expert rules 
BCFBAF 
Bioaccumulation factor 
CAESAR, Meylan and 
KNN/Read-Across models 
EcoTox model bundle 

Two BCF base-line models 
KOCWIN 

Commercial 
Free 

Free or 
Commercial 
Free 
Free 

Free 
Free 
Commercial 
Commercial 

Commercial 

Free 
Free 
Free 

Commercial 

11 The REACH requirement “Simulation testing in water, soil or sediment and identification of degradation products” is 
not listed in this table because – to our knowledge – there are no QSAR tools/models available for this endpoint.  

 
 
 
 
 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1   

c.  Ecotoxicological endpoints 

Endpoint12 

Software tool 

Models/Modules 

Short-term toxicity to fish  Danish QSAR 

Database (DTU) 
ECOSAR (US EPA) 
T.E.S.T. (US EPA) 

VEGA (IRFMN) 

Fathead minnow 96h LC50 
from DTU 
Fish, 96-hr, LC50 
Fathead minnow LC50 (96 
hr) 
SarPy/IRFMN classification 
and KNN/Read-Across model 
Toxicity module 

Free or 
Commercial 
Free 

Free 
Free 

Free 

Commercial 

Commercial 

Commercial 

EcoTox model bundle 

Fathead Minnow LC50 

ADMET Predictor 
(Simulations Plus) 
CASE Ultra 
(MultiCASE) 
Discovery Studio 
(Accelrys) 
ECOSAR (US EPA) 
Danish QSAR 
Database (DTU) 
Free 
ECOSAR (US EPA)  Daphnid, 48-hr, LC50 
T.E.S.T. (US EPA)  Daphnia magna LC50 (48 hr)  Free 
Free 
VEGA (DEMETRA)  Daphnia Magna LC50 (48 h) 
Commercial 
ADMET Predictor 
(Simulations Plus) 
Discovery Studio 
(Accelrys) 
ECOSAR (US EPA)  Daphnid, ChV11 

Fish, ChV13 
Daphnia magna 48h EC50 
from DTU 

Toxicity module 

Daphnia EC50 

Commercial 

Free 
Free 

Free 

Danish QSAR 
Database (DTU) 
ECOSAR (US EPA)  Green Algae, 96-hr, EC50 
ECOSAR (US EPA)  Earthworm, 14-day, LC50 

Pseudokirchneriella s. 72h 
EC50 from DTU 

Free 

Free 
Free 

Long-term toxicity to fish 
Short-term toxicity to 
aquatic invertebrates 
(daphnia) 

Long-term toxicity to 
aquatic invertebrates 
(daphnia) 
Toxicity to aquatic plants 
(algae) 

Short-term toxicity to 
terrestrial invertebrates 

12  The  following  REACH  requirements  are  not  listed  in  this  table  because  –  to  our  knowledge  –  there  are  no  QSAR 
tools/models available for these endpoints:Toxicity to aquatic micro-organisms (activated sludge respiration inhibition 
testing), 
Long-term toxicity to sediment organisms, 
Long-term toxicity to terrestrial invertebrates, 
Short-term toxicity to terrestrial plants, 
Long-term toxicity to terrestrial plants, 
Toxicity to terrestrial micro-organisms, and 
Long-term toxicity to birds. 
13 See section 4.3 c) of this practical guide for further information on these chronic values. 

 
 
 
 
 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

33 

d.  Toxicological endpoints 

Endpoint 

Software tool 

Models/Modules 

Acute toxicity 

Skin irritation 
or skin 
corrosion 

Eye irritation 

Skin 
sensitisation 

Repeated dose 
toxicity 

Models for acute toxicity in 
rodents from ACD/Labs 
Oral rat LD50 
Acute Toxicity Module 

Danish QSAR Database 
(DTU) 
T.E.S.T. (US EPA) 
ACD/Percepta 
(ACD/Labs) 
ADMET Predictor 
(Simulations Plus) 
CASE Ultra (MultiCASE)  AcuteTox model bundle 
Discovery Studio 
(Accelrys) 
Danish QSAR Database 
(DTU) 
OECD QSAR Toolbox 

Rat oral LD50  and rat inhalation 
toxicity LC50 
Skin irritation model 

Toxicity module 

Skin irritation/corrosion Inclusion 
(and Exclusion) rules by BfR 
Skin irritation / skin corrosion 
Irritation Module 

ToxTree (JRC) 
ACD/Percepta 
(ACD/Labs) 
CASE Ultra (MultiCASE)  SkinEye Toxicity model bundle 
Derek (Lhasa) 
Discovery Studio 
(Accelrys) 
OECD QSAR Toolbox 

Irritation (of the skin) alerts 
Skin irritancy 

Eye irritation/corrosion Inclusion 
(and Exclusion) rules by BfR 
Eye irritation and corrosion 
Irritation Module 

ToxTree (JRC) 
ACD/Percepta 
(ACD/Labs) 
CASE Ultra (MultiCASE)  SkinEye Toxicity model bundle 
Derek (Lhasa) 
Discovery Studio 
(Accelrys) 
Danish QSAR Database 
(DTU) 
OECD QSAR Toolbox 

Irritation (of the eye) alerts 
Ocular irritancy 

Allergic Contact Dermatitis model 

Protein binding alerts for skin 
sensitisation by OASIS 
Skin sensitisation reactivity 
domains 
CAESAR model 
Irritation Module 

VEGA (IRFMN) 
ACD/Percepta 
(ACD/Labs) 
CASE Ultra (MultiCASE)  SkinEye Toxicity model bundle 
Derek (Lhasa) 
Discovery Studio 
(Accelrys) 
TIMES (LMC) 

Skin sensitisation 
Skin sensitization  

Skin sensitization with 
autoxidation 
Toxicity module 

ADMET Predictor 
(Simulations Plus) 
CASE Ultra (MultiCASE)  Several model bundles associated 

Derek (Lhasa) 

with repeated dose toxicity 
Several endpoints associated 
with repeated dose toxicity 

ToxTree (JRC) 

Free or 
Commercial 
Free 

Free 
Commercial 

Commercial 

Commercial 
Commercial 

Free 

Free 

Free 
Commercial 

Commercial 
Commercial 
Commercial 

Free 

Free 
Commercial 

Commercial 
Commercial 
Commercial 

Free 

Free 

Free 

Free 
Commercial 

Commercial 
Commercial 
Commercial 

Commercial 

Commercial 

Commercial 

Commercial 

 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1   

Endpoint 

Software tool 

Models/Modules 

Discovery Studio 
(Accelrys) 
Leadscope 

In vitro gene 
mutation in 
bacteria (Ames 
test) 

Danish QSAR Database 
(DTU) 
OECD QSAR Toolbox 

T.E.S.T. (US EPA) 
ToxTree (JRC) 

VEGA (IRFMN) 

Rat Chronic (Oral) LOAEL  

Several models associated with 
repeated dose toxicity 
Models for Ames test 

Several profilers (alerts) 
associated with this endpoint 
Mutagenicity 
In vitro mutagenicity (Ames test) 
alerts by ISS 
CAESAR, SarPy/IRFMN, ISS and 
KNN/Read-Across models 
Genotoxicity Module 

ACD/Percepta 
(ACD/Labs) 
CASE Ultra (MultiCASE)  Bacterial mutagenicity model 

bundle 
Mutagenicity in vitro 
Ames Mutagenicity  

Free or 
Commercial 
Commercial 

Commercial 

Free 

Free 

Free 
Free 

Free 

Commercial 

Commercial 

Commercial 
Commercial 

Derek (Lhasa) 
Discovery Studio 
(Accelrys) 
Leadscope 

TIMES (LMC) 
Danish QSAR Database 
(DTU) 
OECD QSAR Toolbox 

ToxTree (JRC) 

Mutagenicity 
(other 
endpoints than 
in vitro gene 
mutation in 
bacteria) 

CASE Ultra (MultiCASE)  EcoTox model bundle 
Derek (Lhasa) 
Leadscope 
TIMES (LMC) 

Reproductive 
toxicity 

Danish QSAR Database 
(DTU) 

Genetox Expert Alerts Suite and 
Non-human Genetic Toxicity Suite 
Ames mutagenicity 
Models for genotoxicity endpoints  Free 

Commercial 

Commercial 

Several profilers (alerts) 
associated with mutagenicity 
Several decision trees associated 
with mutagenicity 

Free 

Free 

Commercial 
Commercial 
Chromosome damage in vitro 
Non-human Genetic Toxicity Suite  Commercial 
Commercial 
Several models associated 
with mutagenicity 
Models for Endocrine endpoints 
and model for Teratogenic 
Potential in Humans 
CAESAR and PG models 
Toxicity module 

Free 
Commercial 

Free 

VEGA (IRFMN) 
ADMET Predictor 
(Simulations Plus) 
CASE Ultra (MultiCASE)  Several model bundles associated 

Commercial 

Derek (Lhasa) 

Discovery Studio 
(Accelrys) 
Leadscope 

TIMES (LMC) 

with reproductive and 
developmental toxicity 
Several endpoints associated 
with reproductive toxicity 
Developmental Toxicity Potential   Commercial 

Commercial 

Several models associated with 
reproductive and developmental 
toxicity 
Androgen, AHR and Estrogen 
(receptor) binding affinity models 

Commercial 

Commercial 

 
 
Practical Guide - How to use and report (Q)SARs 3.1.1 

35 

e.  Information on the Danish (Q)SAR Database 

A new version of the Danish (Q)SAR database has been released in November 2015 and is 
publicly available at the following link: http://qsar.food.dtu.dk/.  

This database contains (Q)SAR predictions for physicochemical properties, ecotoxicity, 
environmental fate, ADME and toxicity of more than 600 000 chemical structures. 

When possible, models from the Technical University of Denmark and some commercial 
models have been modelled in the three software systems Leadscope, CASE Ultra (MultiCASE) 
and SciQSAR. Some model predictions from ACD/Labs and the US EPA (EPI Suite and ECOSAR) 
have also been integrated in the database. 

However, it should be noted that the database does not provide the possibility to refine the 
predictions as some of the source software programs do (e.g. ECOSAR). In addition, the 
database does not provide as much details on the results as the source software programs and 
it is not updated on a regular basis. Therefore, whenever possible, the predictions given by the 
database should be compared to the results obtained from the source software programs 
themselves. 

A REACH registrant who would like to report in its IUCLID registration dossier a prediction 
coming from the Danish (Q)SAR database should also check that the (Q)SAR model is valid – 
by comparing the points given in Section 3.1 of this practical guide with the information given 
in the model’s QMRF – and should attach a QPRF (for each prediction) to the IUCLID endpoint 
study record. 

 
 
 
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Practical Guide - How to use and report (Q)SARs 3.1.1   

Appendix 2. Further guidance documents and links 

a.  Guidance documents providing information about (Q)SARs 

Various chapters of the Guidance on information requirements and chemical safety assessment 
provide useful information: 

Chapter R.4: Evaluation of available information, with a summary on how to use non-testing 
data obtained by applying (Q)SARs (chapter R.4.3.2.1 (Q)SAR data) 

Chapter R.6: (Q)SARs and grouping of chemicals contains a dedicated part on 
computational methodologies (chapter R.6.1 Guidance on (Q)SARs)  

Chapter R.7: Endpoint specific Guidance offers relevant tools and approaches for the 
endpoints of interest, and specific for each endpoint. 

Chapter R.8: Characterisation of dose [concentration]-response for human health: identifies 
for human health, the available (Q)SARs which may be suitable mostly for hazard 
identification, in particular in a weight of evidence approach.  

Chapter R.10: Characterisation of dose [concentration]-response for environment presents 
several (Q)SAR tools which may be used to determine the Predicted No-Effect 
Concentrations (PNECs) (chapter R.10.2.2.2 (Q)SAR and grouping approaches). 

Chapter R.11: PBT Assessment refers to information on the use of non-testing degradation 
and bioaccumulation data for persistent, bioaccumulative and toxic (PBT) chemicals.  

b.  Other useful links 

OECD (Q)SAR Toolbox: http://www.qsartoolbox.org/ 

OECD Global Portal (eChemPortal): https://www.echemportal.org/echemportal/ 

Manual ‘How to prepare registration and PPORD dossiers’: http://echa.europa.eu/manuals  

‘Practical guide on how to use alternatives to animal testing to fulfil your information 
requirements for REACH registration’: http://echa.europa.eu/practical-guides 

‘Practical guide for SME managers and REACH coordinators on how How to fulfil your 
information requirements at tonnages 1-10 tpa and 10-100 tpa’: http://echa.europa.eu/ 
practical-guides 

EUROPEAN CHEMICALS AGENCY 
ANNANKATU 18, P.O. BOX 400, 
FI-00121 HELSINKI, FINLAND 
ECHA.EUROPA.EU