Organisation for Economic Co-operation and Development

ENV/CBC/MONO(2023)32/ANN1
Unclassified

English - Or. English
21 November 2023

ENVIRONMENT DIRECTORATECHEMICALS AND BIOTECHNOLOGY COMMITTEE

Cancels & replaces the same document of 17 August 2023

ANNEX I – (Q)SAR model reporting format (QMRF) v.2.1
Series on Testing and Assessment
No. 386

The (Q)SAR Assessment Framework document is available under the cote ENV/CBC/MONO(2023)32
An editable format is available athttps://www.oecd.org/chemicalsafety/risk-assessment/qsar-assessment-framework-annex-1-qsarmodel-reporting-format.docx.

JT03532230
OFDE

This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to thedelimitation of international frontiers and boundaries and to the name of any territory, city or area.

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ANNEX I – (Q)SAR MODEL REPORTING FORMAT (QMRF) V.2.1
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ENV/CBC/MONO(2023)32/ANN1  3OECD Environment, Health and Safety Publications
SERIES ON TESTING AND ASSESSMENT
NO. 386

ANNEX I – (Q)SAR model reporting format (QMRF) v.2.1

Environment Directorate
ORGANISATION FOR ECONOMIC COOPERATION AND DEVELOPMENT
Paris 2023

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ANNEX I – (Q)SAR MODEL REPORTING FORMAT (QMRF) V.2.1
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ANNEX I – (Q)SAR MODEL REPORTING FORMAT (QMRF) V.2.1
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Annex I –(Q)SAR model reporting format(QMRF) v.2.1
QMRF v.2.1 is a minor update of the QMRF template, as it only concerns the description of the QMRF
fields. The only exception is Section 10, which has been entirely removed. This section referred to the JRC
QSAR Model Database, which is not updated anymore.
The update is based on the version 2.01.
Element

Explanation

1.

QSAR identifier

1.1.

QSAR identifier (title)

Provide the title of the model. The title should include keywords such as:
endpoint modelled (as detailed as possible and consistent with section 3of the QMRF, recommended), name of the model, name of the modeller,
and name of the software and version coding the model. Examples: “EPI
SuiteTM BIOWIN v4.10 for Biodegradation”; “TOPKAT Rabbit Skin
Irritation (Draize test) for Acyclic compounds (Acids, Amines, Esters)
Severe vs Non/Mild/Moderate skin irritation”.

1.2

Other related models

If applicable, identify any model that is related to the model described in
the present QMRF. Example: TOPKAT Rabbit Skin Irritation (Draize test)
for Acyclics compounds (Acids, Amines, Esters) NEG/MLD v MOD/SEV
Model Nonirritant vs. Mild/Moderate skin irritation” is related to the model
mentioned in 1.1: “TOPKAT Skin Irritation Acyclics compounds (Acids,
Amines, Esters) Severe vs. Non/Mild/Moderate skin irritation MOD v SEVModel.”

1.3.

Software coding the modelSpecify the name and the version of the software that implements themodel. Examples: “BIOWIN v. 4.2 (EPI Suite)”; “TOPKAT v. 6.2”.
If the model is implemented as a web service, please report link to theservice.
If no software implements the model, please state this.

2.

General information

2.0

Abstract

A free text description of the context and background of the (Q)SAR
model. In addition, a more comprehensive explanation should be added
if there is no scientific article about the model or if the presented modelis based on several scientific articles. If the model is adapted from a

1

Triebe, J., Worth, A., Janusch Roi, A. and Coe, A., JRC QSAR Model Database: EURL ECVAM DataBase service
on ALternative Methods to animal experimentation: To promote the development and uptake of alternative andadvanced methods in toxicology and biomedical sciences: User Support & Tutorial, EUR 28713 EN, Publications Officeof the European Union, Luxembourg, 2017, ISBN 978-92-79-71406-1, doi:10.2760/905519, JRC107491.

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scientific article, or from data obtained from open (or closed) sources, it
must be clearly stated, and the changes made during the adoption of themodel must be described.
Report the date of QMRF drafting (day/month/year). Example: “5November 2023”.

2.1.

Date of QMRF

2.2.

QMRF author(s) and contact details

QMRF author(s) and contact details: Indicate the name and the contact
details of the author(s) of the QMRF (first version of the QMRF).

2.3.

Date of QMRF update(s)2.4.

QMRF update(s)Date of QMRF update(s): Indicate the date (day/month/year) of any
update of the QMRF. The QMRF can be updated for a number of reasons
such as additions of new information (e.g. addition of new validation
studies in section 7) and corrections of information. However, please
note that if the (Q)SAR itself is being updated (i.e. changes in training set
or modelling) this should be regarded as a new model which should havea new QMRF, rather than an update of the old QMRF.
QMRF update(s): Indicate the name and the contact details of theauthor(s) of the QMRF updates (see field 2.3) and list which sections andfields have been modified and why.

2.5.

Model developer(s) and contact details

Model developer(s) and contact details: Indicate the name of model
developer(s)/author(s), and the corresponding contact details; possibly
report the contact details of the corresponding author.

2.6.

Date of model development and/or
publication

Date of model development and/or publication: Report the year of
release/publication of the model described in this QMRF.

2.7.

Reference(s) to main scientific papers
and/or software package

2.8.

Availability of information about the
model

2.9.

Availability of another QMRF for exactly
the same model

Reference(s) to main scientific papers and/or software package: List themain bibliographic references (if any) to original paper(s) explaining themodel development and/or software implementation. Any otherreference such as references to original experimental data and related
models can be reported in field 9.2 “Bibliography”. Please note, that itshould be clearly indicated if the scientific paper(s) refer to an earlier
model version / model than the one addressed in the QMRF.
Availability of information about the model: Indicate whether the model isproprietary or non-proprietary and specify (if possible) what kind of
information about the model cannot be disclosed or are not available(e.g., training and external validation sets, source code, and algorithm).
Example: "The model is non-proprietary: full description of the model
algorithm is available, training and test sets are available assupplementary material of original research article";"The model is non-proprietary: training and test sets are available inmodel repository X;“The model is non- proprietary: but the training and test sets are notavailable”;“The model is proprietary and: the algorithm and the data sets are
confidential”;"The model is proprietary: the algorithm and data sets and model
development are confidential, however the model is implemented in a
public web service";
Availability of another QMRF for exactly the same model: Indicate if you
are aware or suspect that another QMRF is available for the current
model you are describing. If possible, identify this other QMRF.

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Defining the endpoint - OECD
Principle 1: “A DEFINED ENDPOINT"

PRINCIPLE 1: “A DEFINED ENDPOINT". ENDPOINT refers to any
physicochemical,
biological,
orenvironmental
property/activity/effect that can be measured and therefore
modelled. The intent of PRINCIPLE 1 (a (Q)SAR should be
associated with a defined endpoint) is to ensure clarity in theendpoint being predicted by a given model, since a given endpoint
could be determined by different experimental protocols and under
different experimental conditions. It is therefore important to
identify the experimental system and test conditions that is being
modelled by the Q)SAR.

3.1.

Species

3.2.

Endpoint

Species: if applicable, indicate the species for the endpoint being
modelled. Taxon, species, strain, clone, type of organism, cell type, orother (e.g., in chemico)
Example:Fathead minnow (Pimephales promelas)
Endpoint: describe the endpoint that is modelled.
The experimental protocol(s) and test conditions applied for the training
set data together with the subsequent data curation for (Q)SAR
modelling determine the endpoint predicted by the model.

3.3

Comment on endpoint

Comment on the endpoint: Include in this field any other information todefine the endpoint being modelled. Specify the endpoint further if
relevant, e.g. according to test organism such as species, strain, sex,
age or life stage; according to test duration and protocol; according to
the detailed nature of endpoint etc.

3.4.

Endpoint units

3.5.

Dependent variableEndpoint units: Specify the units of the endpoint measured. For
categorical endpoints, provide details on the scale and eventual
conversions.
Dependent variable: Specify the relationship between the dependent
variable being modelled and the endpoint measured since the twoquantities may be different. Example: For modelling purposes all rate
constants (i.e. Nitrate radical degradation rate constant (kNO3) weretransformed to logarithmic units and multiplied by -1 to obtain positive
values. The dependent variable is: -log(kNO3).

3.6.

Experimental protocol

Experimental protocol: Make any useful reference to a specificexperimental protocol(s) (for example OECD Test Guideline number)
followed in the collection and evaluation of the experimental data sets.

3.7.

Endpoint data quality and variability

Endpoint data quality and variability: provide available information about
the experimental test data quality selection and evaluation and include a
description of the data quality used to develop the model. This includes
provision of information about in terms of the known variability of the test
data, i.e. repeatability (variability over time) and reproducibility (variability
between laboratories) and sources of error (confounding factors which
may influence testing results) etc..
Please also as far as possible provide information about test chemical
purity. Ideally, (Q)SARs should be based on experimental tests
performed with test chemical of high purity to assure good correlation
between structures and effect.
Test chemical purity should preferably be provided for the individual

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substances used in the training and validation sets.
The data curation procedure and its effect on data quality should also be
described here.

4

Defining the algorithm - OECD
Principle 2 : “AN UNAMBIGUOUS
ALGORITHM”

PRINCIPLE 2: “AN UNAMBIGUOUS ALGORITHM”. The (Q)SAR
estimate of an endpoint is the result of applying an ALGORITHM toa set of structural parameters which describe the chemical
structure. The intent of PRINCIPLE 2 (a (Q)SAR should be
associated with an unambiguous algorithm) is to ensuretransparency in the model algorithm that generates predictions ofan endpoint from information on chemical structure and/or
physicochemical properties. In this context, algorithm refers to any
mathematical equation, decision rule or output approach.

4.1.

Type of modelType of model: Describe the type of model, e.g., equation based,fragment/alert based (expert rule-based and/or statistical based), etc..

4.2.

Explicit algorithm

4.3.

Descriptors in the model

Explicit algorithm: Report the algorithm (only the algorithm) for
generating predictions from the descriptors and/or structural
fragments/alerts; more text information about the algorithm can bereported in the following fields of this section or as supporting information(see field 9.3). If the algorithm is too long and complicated, include in thisfield a reference to a scientific paper or another document where thealgorithm and/or general modelling approach is described in detail. If
possible, the algorithm should be made available in a machine-readable
manner (e.g., PMML or some other model describing format). This
material can be attached as supporting information or made available inan open access repository. This material can be attached as supporting
information.
If the algorithm cannot be disclosed or fully disclosed due toconfidentiality, it should be explicitly stated.
Descriptors in the model: Identify the number and the name or identifierof the descriptors included in the model. In this context, descriptors referto e.g. physicochemical parameters, structural fragments etc.

4.4.

Descriptor selection

Descriptor selection: Indicate the number and the type (name) of
descriptors /decision rules initially screened, and explain the method
used to select the descriptors and develop the model from them.

4.5.

Algorithm and descriptor generation

Algorithm and descriptor generation: Explain the approach used to derive
the algorithm and the method (approach) used to generate each
descriptor.

4.6.

Software name and version for descriptor
generation

Software name and version for descriptor and algorithm generation:
Specify the name and the version of the software used to generate thedescriptors and the algorithm. If relevant, report the specific settingschosen in the software to generate a descriptor or the algorithm.

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4.7.

Chemicals/Descriptors ratio

5

Defining the applicability domain OECD Principle 3: “A DEFINEDDOMAIN OF APPLICABILITY”

5.1.

Description of the applicability domain of
the model

Description of the applicability domain of the model: Define / describe theresponse and chemical structure and/or descriptor space in which themodel makes predictions with a given reliability determined in thestatistical validation. Describe the defined applicability domain in termsof the following, as relevant:a) fixed or probabilistic boundaries;b) structural features, a descriptor and/or a response space;c) in the case of (Q)SARs which applies substructures as descriptors,
describe possible defined limits on applicability (inclusion and/or
exclusion rules regarding the chemical classes to which the model isapplicable);d) in the case of (Q)SARs applying substructures as descriptors,
describe possible rules on the modularity effects of the substructure’s
molecular environment;e) possible defined inclusion and/or exclusion rules for the descriptor
variable ranges for which the (Q)SAR is applicable;f) possible defined inclusion and/or exclusion rules that define theresponse variable ranges for which the QSAR is applicable;g) possible defined (graphical) expression of how the descriptor valuesof the chemicals in the training set are distributed in relation to theendpoint values predicted by the model.

5.2.

Method used to assess the applicability
domain

Method used to assess the applicability domain: Describe the method
used to assess the applicability domain of the model.

5.3.

Software name and version
applicability domain assessment

Software name and version for applicability domain assessment: Specify
the name and the version of the software used to apply the applicability
domain method, where applicable. If relevant, report the specific settingschosen in the software to apply the method.

for

Chemicals/ Descriptors ratio: Report the following ratio: number ofchemicals (chemicals from the training set) to number of descriptors, if
applicable (if not, explain why). For some type of models, it may also be
important to report the number of other parameters in the model (e.g.,number of hidden neurons in artificial neural network models).
PRINCIPLE 3: “A DEFINED DOMAIN OF APPLICABILITY”.
APPLICABILITY DOMAIN refers to the response and chemical
structure space in which the model makes predictions with a given
reliability. Ideally the applicability domain should express the
structural, physicochemical and response space of the model. The
CHEMICAL STRUCTURE (x variable) space can be expressed byinformation on physicochemical properties and/or structural
fragments. The RESPONSE (y variable) can be any
physicochemical, biological or environmental effect that is being
predicted. According to PRINCIPLE 3 a (Q)SAR should be
associated with a defined domain of applicability. Section 5 can be
repeated (e.g., 5.a, 5.b, 5.c, etc) as many times as necessary if morethan one method has been used to assess the applicability domain.

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5.4.

Limits of applicability

Limits of applicability: Describe for example the inclusion and/or
exclusion rules (fixed or probabilistic boundaries, structural features,
descriptor space, response space) that define the applicability domain.

6

Defining
goodness-of-fit
androbustness (internal validation) –OECD Principle 4: “APPROPRIATE
MEASURES OF GOODNESS-OF-FIT,ROBUSTENESS AND PREDICTIVITY”

PRINCIPLE 4: “APPROPRIATE MEASURES OF GOODNESS-OF-FIT,ROBUSTENESS AND PREDICTIVITY”. PRINCIPLE 4 expresses theneed to perform validation to establish the performance of themodel. GOODNESS-OF-FIT and ROBUSTNESS refer to the internal
model performance.

6.1.

Availability of the training set

Availability of the training set: Indicate whether the training set issomehow available (e.g., published in a paper, embedded in the software
implementing the model, stored in a database) and appended to thecurrent QMRF as supporting information (field 9.3). If it is not available,
explain why. Example: “It is available and attached” “It is available but
not attached”; “It is not available because the data set is proprietary”;“The data set could not be retrieved”.

6.2.

Available information for the training set

Available information for the training set: Indicate whether the following
information for the training set is reported as supporting information (seefield 9.3): a) Chemical names (common names and/or IUPAC names);b) CAS numbers; c) SMILES; d) InChI codes; e) MOL files; f) Structural
formula; g) If the dataset contains nanomaterials; h) test chemical purity
for individual substances; i) Any other structural information.

6.3.

Data for each descriptor variable for the
training set

Data for each descriptor variable for the training set: Indicate whether thedescriptor values of the training set are available and are attached as
supporting information (see field 9.3).

6.4.

Data for the dependent variable for the
training set

Data for the dependent variable (response) for the training set: Indicate
whether dependent variable values of the training set are available andattached as supporting information (see field 9.3).

6.5.

Other information about the training set

6.6.

Pre-processing of data before modelling

Regardless of whether the training set is made available, otherinformation about the training set: Indicate any other relevant information
about the training set (e.g. number and type of compounds in the training
set (e.g. for models predicting positive and negative results the numberof positives and the number of negatives in the training set)). Also
indicate the rationale on the selection of the different compounds of the
training set here.
Pre-processing of data before modelling: Indicate whether raw data havebeen processed before modelling (e.g. averaging of replicate values); if
yes, report whether both raw data and processed data are given.

6.7.

Statistics for goodness-of-fit

6.8.

Robustness - Statistics obtained byleave-one-out cross-validation

Statistics for goodness-of-fit: Report here goodness-of-fit statistics: in thecase of models for continuous endpoints, report at least r2, r2 adjusted,
RMSE; in the case of models for categorical endpoints, report at leastsensitivity, specificity, true positives (TP), true negatives (TN), false
negatives (FN), false positives (FP)Robustness – Statistics obtained by leave-one-out cross-validation:
Report here the corresponding statistics.

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6.9.

Robustness - Statistics obtained byleave-many-out cross-validation

Robustness – Statistics obtained by leave-many-out cross-validation:
Report here the corresponding statistics, the strategy for splitting thedata set (e.g. random, stratified), the percentage of left out compounds
and the number of cross-validations.

6.10.

Robustness - Statistics obtained by Yscrambling

Robustness – Statistics obtained by Y-scrambling: Report here thecorresponding statistics and the number of iterations.

6.11.

Robustness - Statistics obtained bybootstrap

Robustness – Statistics obtained by bootstrap: Report here thecorresponding statistics and the number of iterations.

6.12.

Robustness - Statistics obtained by othermethods

Robustness – Statistics obtained by other methods: Report here thecorresponding statistics.

7

Defining
predictivity(external
validation) – OECD Principle 4:“APPROPRIATE MEASURES OFGOODNESS-OF-FIT, ROBUSTENESSAND PREDICTIVITY”

PRINCIPLE 4: “APPROPRIATE MEASURES OF GOODNESS-OF-FIT,ROBUSTENESS AND PREDICTIVITY”. PRINCIPLE 4 expresses theneed to perform validation to establish the performance of themodel. PREDICTIVITY refers to the external model validation.
Section 7 can be repeated (e.g., 7.a, 7.b, 7.c, etc) as many times asnecessary if more validation studies need to be reported in the
QMRF.

7.1.

Availability of the external validation set

Availability of the external validation set: Indicate whether an external
validation set is available and appended to the current QMRF as
supporting information (field 9.3). If it is not available, explain why.

7.2.

Available information for the external
validation set

7.3.

Data for each descriptor variable for theexternal validation set

Available information for the external validation set: Indicate whether thefollowing information for the external validation set is reported as
supporting information (see field 9.3): a) Chemical names (common
names and/or IUPAC names); b) CAS numbers; c) SMILES; d) InChI
codes; e) MOL files; f) Structural formula; g) If the dataset contains
nanomaterials; h) test chemical purity for individual substances; i) Any
other structural information.
Data for each descriptor variable for the external validation set: Indicate
whether descriptor values of the external validation set are somehow
available and attached as supporting information (see field 9.3).

7.4.

Data for the dependent variable for theexternal validation set

Data for the dependent variable for the external validation set: Indicate
whether dependent variable values of the external validation set are
somehow available and attached as supporting information (see field
9.3).

7.5.

Other information about the external
validation set

Other information about the external validation set: Indicate any otherrelevant information about the validation set. Example: “External
validation set with 56 compounds appended”.

7.6.

Experimental design of test set

Experimental design of test set: Indicate any experimental design forgetting the validation set (e.g. by randomly setting aside chemicals
before modelling, by literature search after modelling, by prospective
experimental testing after modelling, etc.).

7.7.

Predictivity - Statistics obtained byexternal validation

Predictivity – Statistics obtained by external validation: Report here thecorresponding statistics. In the case of classification models, include
false positive and negative rates.

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7.8.

Predictivity - Assessment of the external
validation set

Predictivity – Assessment of the external validation set: Discuss whether
the external validation set is sufficiently large and representative of theapplicability domain. Describe for example the descriptor and response
range or space for the validation test set as compared with that for the
training set. Here the descriptor values of the chemicals predicted by themodel (training set) should be compared with the descriptor value rangeof the test set. In addition, the distribution of the response values of thechemicals in the training set should be compared to the distribution of
the response values of the test set. Predictivity of certain (Q)SARs canbe measured by a cross-validation procedure qualifying it to be a “n-foldexternal validation procedure” or “external cross-validation”

7.9.

Comments on the external validation of
the model

Comments on the external validation of the model: Add any other usefulcomments about the external validation procedure.

8

Providing a mechanistic interpretation- OECD Principle 5: “A MECHANISTICINTERPRETATION, IF POSSIBLE”

PRINCIPLE 5: “A MECHANISTIC INTERPRETATION, IF POSSIBLE”.
According to PRINCIPLE 5, a (Q)SAR should be associated with amechanistic interpretation, if possible.

8.1.

Mechanistic basis of the model

Mechanistic basis of the model: Provide information on the mechanistic
basis of the model (if possible). In the case of (Q)SARs using structuralfeatures as descriptors, you may want to describe (if possible) the
molecular features that underlie the properties of the molecules
containing the substructure (e.g. a description of how sub-structuralfeatures could act as nucleophiles or electrophiles, or form part or all ofa receptor-binding region). In the case of (Q)SARs using numericdescriptors, you may give (if possible) a physicochemical interpretationof the descriptors used (consistent with a known mechanism of biologicalaction). If it is not possible to provide a mechanistic interpretation, try to
explain why.

8.2.

A priori or a posteriori mechanistic
interpretation

A priori or a posteriori mechanistic interpretation: Indicate whether themechanistic basis of the model was determined a priori (i.e. before
modelling, by ensuring that the initial set of training structures and/or
descriptors were selected to fit pre-defined known mechanism of action)
or a posteriori (i.e. after modelling, by interpretation of the final set oftraining structures and or descriptors).

8.3.

Other information about the mechanistic
interpretation

Other information about the mechanistic interpretation: Report any otheruseful information about the (purported) mechanistic interpretation
described in the previous fields (8.1 and 8.2) such as any reference
supporting the mechanistic basis.

9

Miscellaneous information

9.1.

Comments

Comments: Add here other relevant and useful comments (e.g. otherrelated models, known applications of the model) that may facilitate
regulatory considerations on the model described. Include if relevant
experience obtained by use of model prediction for various types ofregulatory decisions (incl. references as appropriate).

9.2.

Bibliography

Bibliography: Report useful references other than those directly
associated with the model development (references describing themodel development are reported in field 2.7).

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9.3

Supporting information

Supporting information: Indicate whether supporting information is
attached (e.g. external documents) to this QMRF and specify its content
and possibly its utility. This should cover structures in the training, set,and validation sets, response variable value), descriptor values, whether
the training and test sets are submitted in defined file formats (txt, csv,
SDF, etc.), model (e.g., pmml), predictions for training and validation sets
and other documents, as relevant.

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