KATE2020 Ecotoxicity Prediction System
Technical Document (Ver. 2024/03/25)※ KATE2020 is a system to predict the following ecotoxicity of organic chemicals:
 50% lethal concentration (LC50) in the fish acute toxicity test
 50% effective concentration (EC50) in the Daphnia magna acute immobilization test
 50% effective concentration (EC50) in the algal growth inhibition test No-observed-effect concentration (NOEC) in the fish early-life-stage toxicity test No-observed-effect concentration (NOEC) in the Daphnia magna reproduction test No-observed-effect concentration (NOEC) in the algal growth inhibition test

* Values predicted by KATE2020 cannot be used to satisfy the requirements forreporting ecotoxicity tests under the Law Concerning Examination of Chemical
Substances and Regulation of Manufacturing, etc.
*Please use the predicted values as reference values for ecotoxicological effects of chemical
substances.

Enquires should be directed to the email address indicated below.
Health and Environmental Risk Division, National Institute for Environmental Studies,
KATE Contact Desk

Copyright (C) 2024 Ministry of the Environment, Government of Japan.
All Rights Reserved

KATE2020 Manual Revision History
VersionDate of issue

Revision history

0.99

June 28, 2023

KATE 2020 (Version 3.0) Technical Document

0.99.1March 30, 2023

KATE 2020 (Version 4.0) Technical Document

0.99.2

March 25, 2024

KATE 2020 (Version 5.0) Technical Document

１

Table of Contents
List of abbreviations
1. Introduction
1.1 KATE (KAshinhou Tool for Ecotoxicity) System
1.2 Purpose of KATE 2020 Technical Document
1.3
1.4
1.5
1.6

OECD (Q)SAR Validation Principles
log P
Disclaimer
Acknowledgements

2. Endpoint Definition – OECD (Q)SAR Validation Principle 1
2.1 Endpoints Predicted by KATE2020
2.2 Predicted Value Units
2.3 Dependent Variables
2.4 Training Data Set Endpoints
3. Algorithm – OECD (Q)SAR Validation Principle 2
3.1 Algorithm Outline
3.2 Input of Chemical Substance(s)3.3 Obtaining log P Values
3.4 Extraction of Substructures
3.5 Assginment of Structure Class
3.6 Assignment of QSAR Class
3.7 Calculation of Toxicity Value QSAR Equation
3.8 Prediction Interval Calculation (reference information)
3.9 Similarity Calculation (reference information)
4. Definition of Applicability Domain — OECD (Q)SAR Validation Principle 3
4.1 Applicability Domain
4.2 Applicability Domain Judgement Method
5. Validation – OECD (Q)SAR Validation Principle 4
5.1 Internal Validation
5.2 External Validation
6.

Interpretation of Mechanism — OECD (Q)SAR Validation Principle 5２

Appendix


Substructures
https://kate2.nies.go.jp/nies/substructures.php

Structure Classes
https://kate2.nies.go.jp/nies/structure_classes.php



QSAR Classes
https://kate2.nies.go.jp/nies/qsar_classes.php３List of abbreviations

CDK: Chemistry Development Kit
A collection of free and open-source Java libraries for processing cheminformatics andbioinformatics. The kit can be used to search for the substructures of chemical substancesand calculate their structures and physical properties[1].

EC50: 50% Effective Concentration
The concentration of a substance dissolved in test water expected to produce a certain
effect in 50% of test organisms in a given population under a defined set of conditions.

KATE: KAshinhou Tool for Ecotoxicity
The ecotoxicity QSAR system researched and developed by the Health andEnvironmental Risk Division of the National Institute for Environmental Studies. It is
pronounced as in the feminine name Kate.

KOWWINTM:
A program to estimate log P values of organic compounds included in the EPI SuiteTM(Estimation Programs Interface: a tool intended for use in applications such as to quickly
screen chemicals) developed by the U.S. EPA and its partners[2].

LC50: 50% Lethal Concentration
The concentration of a substance dissolved in test water which, on the basis of laboratory
tests, is expected to kill 50% of a group of test species when administered as a single
exposure.

log P: Logarithm of the octanol/water partition coefficient (Octanol/water partition coefficient)
A common logarithm of the ratio of concentrations of a chemical substance between 1octanol and water in equilibrium. It represents the hydrophobicity of a chemical
substance and does not account for ionization of the chemical substance in question[3].

NOEC: No Observed Effect Concentration
The highest tested concentration (also called maximum no-effect concentration) for which
there is no statistically significant difference of effect (p<0.05) when compared to thecontrol group. This concentration range is just below the LOEC (Least Observed Effect４Concentration) (reference: Japanese Ministry of the Environment, Environmental Risk
Assessment of Chemical Substances Vol. 17, Chapter 1, Reference 2 Glossary [4])OECD: Organisation for Economic Co-operation and Development(Q) SAR: (Quantitative) Structure–Activity Relationships
The relationship between the structural characteristics or the physicochemical constant ofa chemical substance and its biological activities (e.g., toxicity) is called the Structure–
Activity Relationship (SAR), while the quantitative relationship is called the Quantitative
Structure–Activity Relationship (QSAR). Both may be referred to collectively as (Q)SAR.
SAR refers to, for example, an estimation of the toxicity level of a chemical based on thepresence of a specific functional group. A model to quantitatively calculate the toxicity orother properties of a chemical based on the structure is called a QSAR model. (reference:
Japanese Ministry of the Environment, Environmental Risk Assessment of Chemical
Substances Vol. 17, Chapter 1, Reference 2 Glossary [4])SMARTS: SMiles ARbitrary Target Specification
An identifier extended from SMILES to describe substructures.[5]SMILES: Simplified Molecular Input Line Entry System
An identifier of line notation with printable characters to describe the molecular
structures, etc., of chemical compounds.[6]U.S. EPA: United States Environmental Protection Agency

５

1. Introduction
1.1 KATE (KAshinhou Tool for Ecotoxicity) System
KATE (kate.nies.go.jp) is an ecotoxicity QSAR system for which research anddevelopment was commissioned by the Japanese Ministry of the Environment (MoE) to
the Health and Environmental Risk Division of the National Institute for Environmental
Studies. KATE is a system to predict ecotoxicity based on the structure of chemical
substances. The ecotoxicity values predicted by KATE2020 are as follows: 50% lethal concentration (LC50) in acute toxicity test for fish (OECD TG 203)[7] No-observed-effect concentration (NOEC) in fish early-life-stage toxicity test (OECDTG 210) 50% effective concentration (EC50) in the Daphnia magna acute immobilization test
(OECD TG 202)[9] No-observed-effect concentration (NOEC) in the Daphnia magna reproduction test
(OECD TG 211)[10] 50% effective concentration (EC50) and no-observed-effect concentration (NOEC) in thealgal growth inhibition test (OECD TG 201)[11]
Data is input using SMILES notation, which can be obtained by CAS number search, ordrawing using a chemical structure editor, in order to carry out a QSAR prediction by usinga regression equation with log P as the descriptor.
The QSAR model for KATE2020 was constructed using ecotoxicity data obtained fromaquatic toxicity tests conducted by the Japan MoE[12] (fish acute toxicity test,

Daphnia magna acute immobilisation test, fish early-life-stage toxicity test, Daphnia
magna reproduction test, and algal growth inhibition test), together with fish acute toxicity
data from the U.S. EPA fathead minnow database[13].
1.2 Purpose of KATE 2020 Technical Document
This technical document explains the derivation of the KATE2020 QSAR model and its
performance evaluation based on OECD QSAR validation principles[14]. Its target reader
is those who have experience using KATE2020.
Please refer to the KATE2020 Operation Manual(https://kate2.nies.go.jp/nies/doc/KATEmanual_2020-e.pdf) for instructions on using
KATE2020, its development history, and its update history.

６

1.3 OECD QSAR Validation Principles
The technical document describes the KATE2020 QSAR model in terms of the five OECDQSAR validation principles[14].
The QSAR model must satisfy the five OECD QSAR validation principles in order toensure validity and reliability when applying the model to chemical substanceregulations. They are as follows:
1. Endpoint definition
2. Unambiguous algorithms
3. Defined domain of applicability
4. Appropriate assessment of goodness-of-fit, robustness and predictivity
5. If possible, explanation of mechanism
1.4 log P
This system utilizes the log P prediction model KOWWINTM[5] copyrighted by the U.S.
EPA, with its permission, in order to obtain log P values used for predicting thetoxicity of chemical substances.
Users must comply with the KOWWINTM licensing terms described below.
KOWWIN v1.69 (April 2015)
c 2000-2015 U.S. Environmental Protection Agency
KOWWIN is owned by the U.S. Environmental Protection Agency and is protected bycopyright throughout the world.
Permission is granted for individuals to download and use the software on their personaland business computers.
Users may not alter, modify, merge, adapt or prepare derivative works from thesoftware. Users may not remove or obscure copyright, tradename, or proprietary
notices on the program or related documentation.
KOWWIN contained therein is a tradename owned by the U.S. Environmental
Protection Agency.

７

1.5 Disclaimer
The accuracy of results predicted by the KATE system are not guaranteed.
Please utilize this system as a tool to obtain reference information on the degree of
ecotoxicological effects of chemical substances. The Ministry of the Environment
and the National Institute for Environmental Studies do not guarantee thepredicted ecotoxicity values provided by KATE and assume no responsibility
whatsoever for any damages resulting from the use of ecotoxicity values predicted
by KATE.
Further, values predicted by this system cannot currently be used to satisfy therequirements for reporting ecotoxicity tests under the Japanese Law Concerning
Examination of Chemical Substances and Regulation of Manufacturing, etc.
For copyright information and instructions for linking to the site, please visit
the Website Policy page in the KATE website (kate.nies.go.jp/spolicy-e.html).
1.6 Acknowledgements
KATE2020 uses results obtained from the following software and libraries. Here,
we express appreciation to all who developed them.
□ Open Babel[15]□ JSME Molecular Editor[16, 17]□ CDK (Chemistry Development Kit)[1, 18–21]□ KOWWINTM (included in EPI SuiteTM)[2]８2. Endpoint Definition – OECD (Q) SAR Validation Principle 1
Here, we define the endpoints predicted by KATE2020.
2.1 Endpoints Predicted by KATE2020
KATE2020 predicts acute and chronic toxicities using the endpoints (toxicity indicators)
shown in the following table to estimate the ecotoxicities of chemical substances.
Table 2-1: Endpoints predicted by KATE2020.
Predicted Toxicity
Type
Acute/
Organisms
Chronic

Species(Scientific Name)

Fish

Acute

Oryzias latipes,
Pimephales promelas *1

Daphnid

Acute

Daphnia magna

Alga

Acute

Raphidocelis subcapitata *2

Fish

Chronic

Oryzias latipes

Daphnid

Chronic

Daphnia magna

Alga

Chronic

Raphidocelis subcapitata *2

Testing Method
Fish acute toxicity test
(OECD TG 203) [7]Daphnia magna immobilization test
(OECD TG 202) [9]Algal growth inhibition test
(OECD TG 201) [11]Fish early-life-stage toxicity test
(OECD TG 210) [8]Daphnia magna reproduction test
(OECD TG 211) [10]Algal growth inhibition test
(OECD TG 201) [11]Duration

Indicator

96 h

LC50

48 h

EC50

72 h

EC50

Embryonic stage and 30
days after hatching *3

NOEC

21 d

NOEC

72 h

NOEC

*1 If toxicity values are available for both Japanese medaka and fathead minnow,
the Japanese medaka is used. When KATE was initially developed, MoE
Japanese medaka test data alone were insufficient in terms of the number ofsubstances covered to establish a predictive model exhibiting sufficient precision.
Therefore, U.S. EPA fathead minnow test data were also employed anddifferences in predictions arising from interspecies differences in fish species
acute QSAR traceable to Japanese medaka and fathead minnow were
investigated. The findings confirmed that by establishing a QSAR formula using
fathead minnow toxicity values, higher precision could be obtained compared tousing data for Japanese medaka alone. Furthermore, for substances for whichmeasured toxicity values for both Japanese medaka and fathead minnow could
be obtained, the only substance for which the values differed by more than a factorof ten was dimethylamine (Studies on Regulatory based Assessment of Chemicals
under Japanese Chemical Law, Fiscal 2009 Edition).
*2 May have been referred to previously as Selenastrum capricornutum or
Pseudokirchneriella subcapitata, etc.
*3 Test periods for fish species early-life stage tests differ depending on the typeof fish and incubation period. In the case of Japanese medaka used in aquatic
toxicity tests conducted by the MoE, the test period from the embryo stage to 30
days post-hatch is used.

９

2.2 Predicted Value Units
KATE outputs predicted values for toxicity indicators in units of mg/L.
2.3 Dependent Variable
KATE converts the toxicity indicator values from units of mg/L to units of mmol/L anduses the log of the reciprocal, i.e., log (1/toxicity value[mmol/L]) as the dependent
variable.
2.4 Training Data Set Endpoints
The KATE2020 training data set toxicity values are based on the findings of ecotoxicity
tests carried out by the Japan MoE (fish acute toxicity test, Daphnia magna acuteimmobilisation test, fish early-life stage toxicity test, Daphnia magna reproduction test,
algal growth inhibition test), and U.S. EPA fathead minnow database fish species acutetoxicity test results.
Number of Substances
Table 2-2 lists the number of substances in KATE2020 for each predicted toxicity typethat are part of the training data set*1, support chemicals*2 and substances not assignedwith any QSAR class*3 (the number of substances in KATE2020 for all QSAR classes. A
single substance is counted as one even if it is assigned with multiple QSAR classes).
Table 2-2: Number of substances for each predicted toxicity type

Training Set*1
Support Chemical*2
Chemical that is not classified into any
of QSAR classes of KATE2020*3
Total

MOE Data
US EPA Data
MOE Data
US EPA Data
MOE Data
US EPA Data

Fish
361
498
206
1
8
8
1095

Acute
Daphnid
436

Alga
315

Fish
32

Chronic
Daphnid
316

Alga
401

135

214

0

54

115

16

31

0

12

19

1082

487

560

32

382

*1 Substances used to formulate QSAR class regression formulae.
*2 Data with inequality sign (limit tests etc.) or outliers (toxicity test data whose
reliability cannot be verified etc.), and substances for which log P is greater thansix are not included in QSAR class regression formulae. Further, log P values
estimated by KOWWINTM are used for all substances.
*3 Substances that do not correspond to any KATE2020 QSAR class are designatedas “Unclassified class” when predictions are being made.

１０

3. Algorithm – OECD (Q)SAR Validation Principle 2
Here, we describe the KATE2020 algorithm. Section 3.1 presents an outline, while thesubsequent sections go into more detail.
3.1 Algorithm Outline
KATE2020 is a linear regression based QSAR system that predicts the toxicity ofchemical substances (organic compounds).
The bio membrane permeability of chemical substances, their bioaccumulation, andtoxicity are thought to be correlated and based on this assumption, log P is used as a
descriptor.
The Japanese MoE’s ecotoxicological effect test data and the U.S. EPA’s fatheadminnow acute test data are used as training data set. Classification is carried out basedon a decision tree with the feature values of the presence and number of substructurescontained in each substance.
KATE2020 employs 18 major categories and the other categories as shown in Table 3–
1. Each category has a characteristic substructure, while substances are also
categorized into reactive and unreactive classes based on the presence or absence of
substructures considered to be highly reactive and highly toxic. Further conditional
branches (presence of halogens, presence of aromatic atoms, etc.) are used forsegmentation of structure classes (refer to Chapter 3, Section 3.5). QSAR classes (refer
to Chapter 3, Section 3.6) classes are assigned to structure classes and regression
equations are formulated for each QSAR class. Some QSAR classes are classified as
being specifically for toxicity prediction for specific groups of organisms. Substances
possessing substructures with low reactivity that only exhibit narcotic effects are
categorized as the narcotic group class. Substructures exhibiting specific effects such as
skin sensitization[22] are primarily used to evaluate the applicability domain ofstructures (refer to Chapter 4, Section 4.2 A)[23], although some are used forclassification.
Molecular chemical structure data for substances to be predicted are inputted using
the alphanumeric SMILES notation[6], while substructure/s and their number/s are
obtained by using the CDK cheminformatics tool CDK[1]. Substructures are defined
beforehand using SMARTS notation[5], which is an extended notation of SMILES.
Please refer to documentation on the Internet for a list of substructures(https://kate2.nies.go.jp/nies/substructures.php).
QSAR Class assignment is done according to extracted substructures based on the
aforementioned decision tree. Multiple classes may be assigned per predicted toxicity

１１

type (if the query chemical contains substructures that do not match any classes
existing in KATE, the chemicals is assigned to Unclassified class and toxicities are notpredicted). Subsequently, the predicted toxicity value for each class is generated basedon log P estimated by KOWWINTM[2]. While the log P value can be manually entered by
the user, log P values of all training set chemicals are estimated by KOWWINTM.
Table 3–1: KATE2020 Major Classification List
Classification

Substructure

CategoryID

Substructure Name

SMARTS[#6;$([#6](=[#8])([#6])[#8H1])]

3034

carboxylic acid C(=O)O

4760-SO3H, Sulfonic Acid, sulfo-, -sulfonic acid[C,c,O]S(=O)(=O)[O;$([OH1]),$(O[Na,Li,K])]

3046

alcohol COH[#6;$([#6][#8;H]);!X3;v4]

3031

ketone CC(=O)C[#6;$([#6](=[#8])([#6])[#6])]

3036

aldehyde[#6H1;$([#6](=[#8])[#6])]3032

ester CC(=O)OC

3145

acetal

[#6X4;$([#6]([#8])[#8])]

5 ether

3044

ether general[#8H0;!$([#8]C=[O,N,S]);!$([#8]C[#8]);$([#8]([#6])[#6])]

6 phenol3047

phenol cOH[OX2H][cX3;$(c1ccccc1)]

7 amine primary

3100

amine CNH2[#7X3H2;!$([#7][*v6]);!$(N[#6](~[#7,#8,#16]))]3110

amine CNH1

3120

amine CNH0[#7v3X3H0;!$([#7][!#6]);!$([#7][*v6]);!$([#7][#6](~[#7,#8,#16]))]9 aromatic n

4911

aromatic n[n]

10 hydrazine

3210

NN, hydrazine general, not in ring

[Nv3X3R0;$([N][Nv3X3])]

11 nitrile

3104

nitril C#N[N;$([#7X1]#[#6X2])]

12 amide

3123

amide

13 carbamate3041

carbamate general NC(=O,S)O,S[#7v3X3;$([#7][#6R0](=[O,S])[O,S])]

14 nitro

3231

nitro aromatic[N;$(N(c)(=O)=O),$([N+](c)(=O)[O-])]

15 phosphorus

5018

Phosphorus [P][#15]

16 sulfur

5016

Sulfur [S][#16]

17 halogen

4507

halogen[F,Cl,Br,I]

4911

aromatic n

[n]

4912

aromatic o

[o]

4913

aromatic s

[s]

3106

azo N=N[NX2;$(N=N)]

4541

epoxide monocyclic[#8r3;$([#8]1[#6R1][#6R1])]

3108

imino C=N-, guanidine[#7v3X2;$(N=[Cv4X3]);!$(N[N,O,S])]

1 acid2 alcohol
3 aldehyde, ketone

4 ester

8 amine sec, tert

18 heteroatomatic[#6;$([#6](=[#8])([#6])[#8][#6]);!$([#6](=[#8])([#6])[#8][#6]=[O,S,N])][#7v3X3H1;!$([#7][!#6]);!$([#7][*v6]);!$([#7][#6](~[#7,#8,#16]));!$([#
7r5H1](a)a)][#7v3X3;$([#7][C](=O)[!N;!S;!O;!P]),$([#7][CH1](=O));!$(N~[N,S,O]);!$(N(C=[N,O,S])C=O)]

Othersetc
・・・
contains oxygen
contains nitrogencontains oxygen and nitrogencontains phoshorus
contains sulfur
others１２

The toxicity prediction flow is shown below (refer to Figure 3–1).

I. Input of Chemical Structure (SMILES)II.

①B) Extraction of Substructures(using SMARTS）

User

Inside KATE

①A) log P②Extraction of Structure Classes(based on the combination of substructures)

Input Valueor Estimated Value③Assignment of QSAR Class④Calculation of toxicity values using
QSAR equation⑤Judgement of Applicability Domain(Structure Judgement, Log P Judgement )III. Output of Prediction Results※ Following QSAR classes are not displayed (by default)
・Unclassified class
・QSAR class not satisfying R2≥0.7 and Q2≥0.5 and n≥5
Figure 3-1: KATE2020 toxicity prediction flow

Ⅰ．The user inputs the structure of the chemical substance (using SMILES
notation) and the log P value (optional).

Ⅱ．Processes on the QSAR equation assignment, predicted toxicity values, andthe applicability domain judgement.
① For the query chemical (entered chemical substance)A)log P value is estimated (or the input value is used).

B)

substructure/s is/are extracted.

② Structure class*1 is extracted based on the combination of the extracted
substructures.
③ For each predicted toxicity type, a QSAR class*2 corresponding to thestructure class is assigned (multiple classes may be assigned).
④ For each assigned QSAR class, the toxicity value is calculated using the
QSAR equation*3.

１３

⑤ The applicability domain is judged (log P judgement and structure
judgement).
*1 Classification defined by a combination of each substructure number condition
AND/OR (Section 3.5 Structure class extraction)*2 Classification defined based on the structure of the chemical for each predicted
toxicity type.
*3 Model constructed based on training set data included in the QSAR class. Herelog P is used as the descriptor for a simple regression equation.

Ⅲ．Prediction Output
If the query chemical cannot be assigned to any of QSAR classes for a
predicted toxicity type, it is assigned to the unclassified class.
By default, the unclassified class and QSAR classes that do not satisfy
statistical criteria (R2≥0.7, Q2≥0.5 and n≥5)*4 are not shown.

*4 R2, Q2, and n are the coefficient of determination, the internal validation
indicator (Leave-one-out method), and the number of data points in the
training set, respectively, and they are calculated beforehand for each QSAR
class.

As an actual example, Fig. 3–2 shows the prediction flow of the chemical substance 1pyridin-3-ylethanone.

１４

Figure 3–2: KATE2020 QSAR prediction flow (example)※

The actual names of the Ketone, Aromatic ketone, Aromatic n and Unreactive in
Figure 3–2 used in KATE2020 are as follows.

Ketone: COns_X ketone unreactive
Aromatic Ketone: COS_X ketone unreactive aromatic
Aromatic n: CNOS_X aromatic n unreactive
Unreactive: CNO_X unreactive Fish chronic, w/ N,O

１５

In the following, we explain the flow of toxicity predictions in KATE2020.
3.2 Input of Chemical Substance(s)The user either inputs SMILES directly, or converts a drawn structural formula,
the CAS number, or the substance name to SMILES.
3.3 Obtaining log P Values
The log P value for the query chemical used to calculate the predicted toxicity
value is determined by the following order of priority.
1. log P value entered by user (when user enters a value)
2. log P predicted value (when no value is entered by user)
KOWWINTM[2], licensed by the U.S. EPA, is used to estimate the log P value(refer to Chapter 1, Section 1.6).
3.4 Extraction of Substructures
The number of substructures contained in the query chemical is calculated basedon the substructure definition list (Table 3–2). Each row defines a single
substructure in terms of SMARTS notation (Table 3–3).
Table 3–2: Substructure list (partial)
Substructure ID

Substructure Name

3031

Ketone

3032

Ester

3033

Carbonate

SMARTS…
[#6;$([#6](=[#8])([#6])[#6])][#6;$([#6](=[#8])([#6])[#8][#6]);!$([#6](=[#8])([#6])[#8][#6]=[O,S,N])][#6;$([#6](=[#8])([#8][#6])[#8][#6])]…

Refer to the following link for a list of all substructures.
https://kate2.nies.go.jp/nies/substructures.php
The CDK library[1] is used to calculate the number of substructures using SMARTS.

１６

Table 3–3: List of substructures contained in substance to be predicted(SMILES: Case of O=C(C)c1cccnc1)
Substructure ID
3001
3002
3003
3004
3009
3014
3022
3030
3031

Substructure Name
elements other than CX
elements other than CNX
elements other than COX
elements other than CSX
elements other than COSX
elements other than CnosX
Carboncarbonyl C=O
ketone CC(=O)C3059

C=O w/o electron donated o-, p-Nv3X3

4504
4543
4892
4893
4910
4911
5007
5008

>C=O or >C=S (sPilot4)
MF:not C,c,O,F
MF: not CHO (kPilotO)
MF: not CHOP
aromatic
aromatic n
Nitrogen [N,n]Oxygen [O,o]SMARTS[!#6;!#9;!#17;!#35;!#53][!#6;!#7;!#9;!#17;!#35;!#53][!#6;!#8;!#9;!#17;!#35;!#53][!#6;!#16;!#9;!#17;!#35;!#53][!#6;!#8;!#16;!#9;!#17;!#35;!#53][$([!#6;!F;!Cl;!Br;!I;!n;!s;!o]),$([n+])][#6][#6;$([#6](=[#8]))][#6;$([#6](=[#8])([#6])[#6])][C;$(C=O);!$(C(=O)c1c([Nv3X3])cccc1);!$(C(=O)c1ccc([Nv3X3])cc1)][CX3]=[OX1,SX1][!C;!c;!O;!F][!C;!c;!O][!C;!c;!O;!P]
[a]
[n]
[#7]
[#8]

Substructure ID’s beginning with “5” indicate substructures that are also used forstructure judgement (Refer to Chapter 4, Section 4.1 A, Structure Applicability Domain).
3.5 Assignment of Structure Class
Based on the structure class definition (Figure 3-3 shows the structure class definition
for ketone as an example), assign a structure class that matches the query substance. For
example, O=C(C)c1cccnc1 has one or more substructure 3031 from Table 3-3 but does notmatch the conditions for R_00001 (with any of substructures 3036, 3053, 3054, 3055,
3056, 3174, 4515, 4791, 6112) and G1_00010 (with one of the substructures 3034, 4760),
so it is determined to be ketone unreactive. And as it does not have substructure 3011, it
is assigned a structure class of G1_21025. In total, four structure classes are assigned forthis predicted substance, as shown in Table 3-4.

１７

Count
2
1
1
2
1
1
7
1
1
1
1
1
1
1
6
1
1
1

Figure 3–3 Definition of structure class for ketone
Please refer to the following link for the list of structure classes.

https://kate2.nies.go.jp/nies/structure_classes.php
Clicking on a value in the Decision tree column of a structure class definition displays
the definition of the structure class and substructure in question. Figure 3-4 shows anexample of clicking on "R_00001 = false and G1_00010 = false".

Figure 3–4 Definition of structure class R_00001 and G1_00010

１８

Table 3–4: Structure classes matching with query chemical (partial)(in the case of SMILES: O=C (C) c1cccnc1)
Structure class ID

G1_21025
G1_21029
G1_25002
GA_22075

Category

KetoneKetoneHydrocarbon
aromatic n

Structure class nameCOns_X ketone unreactive
COS_X ketone unreactive aromatic
CNO_X unreactive Fish Chronic, w/ N,Oaromatic n reactive Alga

The first letter/s (and in one case a combination of letter and number) in each structureclass ID refer to the following structural types.
・Beginning with A: Acidic structure・Beginning with C: Carbon-containing structure・Beginning with R: Reactive structure・Beginning with U: Unreactive structure・Beginning with GF: Structure set as QSAR class related to fish・Beginning with GD: Structure set as QSAR class related to daphnids
・Beginning with GA: Structure set as QSAR class related to alga・Beginning with GFD: Structure set as QSAR class related to fish and daphnids
・Beginning with G1: Structure set as QSAR class related to one of either fish, daphnids, or alga

3.6 Assignment of QSAR Class
Based on the structural classes contained in the query substance, a corresponding QSAR
class is assigned to each predicted toxicity type (Table 3-5).
* A single substance can be assigned multiple QSAR classes for a particular predicted
toxicity type. The substance is regarded as unclassified if it cannot be categorizedunder any QSAR class.
Please refer to the following link for a list of all QSAR classes.
https://kate2.nies.go.jp/nies/qsar_classes.php

１９Table 3–5: QSAR class list for assigning to substance to be predicted(in case of SMILES: O=C(C)c1cccnc1)
QSAR IDQSAR classpredicted toxicity typestructure class ID

12102541

COns_X ketone unreactive

Fish Acute

G1_21025

12102941

COS_X ketone unreactive aromatic fish

Fish Acute

G1_21029

22102541

COns_X ketone unreactive

Daphnid Acute

G1_21025

22102941

COS_X ketone unreactive aromatic

Daphnid Acute

G1_21029

32102541

COS_X ketone unreactive

Alga Acute

G1_21025

32207541

aromatic n reactive Alga

Alga Acute

GA_22075

12500251

CNO_X unreactive Fish Chronic, w/ N,O

Fish Chronic

G1_25002

22102551

COns_X ketone unreactive

Daphnid Acute

G1_21025

32102551

COns_X ketone unreactive

Alga Chronic

G1_21025

32207551

aromatic n reactive Alga

Alga Chronic

GA_22075

The prefix of each QSAR class name in Table 3–5, such as COS_X, shows which elements
may be in substances of this QSAR class. Upper case alphabet signifies both aliphatic andaromatic, while lower case alphabet signifies only aromatic. For example, COS_X meansthat both aliphatic and aromatic carbon, oxygen, sulfur, and halogens can be included,
while CXnos means that aliphatic and aromatic carbon and halogens, and aromatic
nitrogen, oxygen, and sulfur may be included. Therefore, substances containing aliphatic
nitrogen are not classified as QSAR classes beginning with COS_X or CXnos.
QSAR IDs (QSAR class ID) are following the rules below.
・The ID consists of 8 digits.
・The first digit is 1 for fish species, 2 for Daphnia magna, and 3 for algal species.
・The second to sixth digits match the last five digits of the structure class ID.
・The seventh digit is 4 for acute and 5 for chronic.
・The eighth digit indicates the descriptor. In the current release, only 1: log P is used.

Figure 3–5 shows a simplified flow from substructure extraction to QSAR class assignment
when the SMILES of the query chemical is O=C(C)c1cccnc1.

２０

Extraction of substructures (Table 3-3)

Substructure 3031 by 1, substructure 4910 by 6 etc., are extracted.

Assignment of structure class (Figure 3-2)

Matching with 3031>0, R_00001=false, G1_00010=false, 3011=0,
structure class G1_21025 (COns_X ketone unreactive) is assigned.
Likewise, G1_21029, G1_25002, GA_22075 are assigned.

Assignment of QSAR class (Table 3-5)

QSAR class “12102541” is assigned corresponding to the following
combination:
・Predicted toxicity type: Fish Acute・Structure class: G1_21025
Likewise, ”12102941” and “22102541” etc., are assigned

Figure 3–5: Flow from calculating the number of substructures to QSAR class assignment(in case of SMILES: O=C (C) c1cccnc1)
3.7 Calculation of Toxicity Value Using QSAR Equation
For each assigned QSAR class, log P of the query chemical and the precalculated
slope and intercept of the relevant QSAR class*2 (Table 3–6) are entered into thefollowing QSAR equation to calculate log (1/toxicity value[mmol/L]). Then, themolecular weight*1 of the query chemical is employed to convert to units of toxicity
value[mg/L].
log (1/toxicity value[mmol/L]) = slope x log P + intercept… (1)*1 Open Babel [15] is used to calculate the molecular weight of the substance to bepredicted.
*2 The slope and the intercept are obtained by a simple regression of the descriptor
log P for the training data set of the applicable QSAR class and the dependent
variable log (1/toxicity value[mmol/L]). log P values for all training sets use
estimated values by KOWWINTM.

２１

3.8 Calculation of Prediction Interval (reference information)
For each assigned QSAR class, substituting the x (log P) value of the query
chemical and the statistical value t95,n,𝑉 ,𝑥̅ , ∑-1 (Tables 3–6, 3–7) calculated
beforehand for the applicable QSAR class gives a 95% confidence level prediction
interval for log (1/toxicity value[mmol/L]) defined below. Subsequently, using themolecular weight of the query chemical, the lower and upper limits of theprediction interval of Equation (2) are converted into toxicity values [mg/L].
95% predicted interval = [log (1/toxicity value[mmol/L]) −dy,log (1/toxicity value[mmol/L]) +dy]

… (2)

Here,

dy = t95 ×… (3)(1 + 1/𝑛 + 𝐷 / (𝑛 − 1) ) × 𝑉

𝐷 = (x −𝑥̅ ) T∑-1 (x −𝑥̅ )… (4)Table 3–6: Statistical values required for calculation of predicted intervals
Variablen𝑥̅Explanation

FormulaNumber of training set data for the
relevant QSAR class
Average descriptor value for the training𝑥 : log P value of the i-th training set data

set data of the relevant QSAR class
Inverse of the covariance matrix of the

∑-1

∑𝑥 /𝑛

𝑛/∑ (𝑥 − 𝑥̅ )descriptors (Here, the inverse of the𝑥 : log P value of the i-th training set data

variance since it is a simple regression)𝑥̅ : Mean value of log P of training set data
∑ (yi − 𝑦i) 2/ (n − p − 1)

yi : Measured toxicity value of the i-th training set dataVεResidual variance𝑦i : Predicted toxicity value for the i-th training set datap : Number of descriptors (here 1 since it is a single
regression)
t-value at 5% significance level (two-t95

tailed test) for the degrees of freedom of
the QSAR class

Table 3–7: Example of statistical values required for calculating QSAR equation slope, intercept, and prediction interval

QSAR ID

slope

intercept

n

𝑥̅

∑-1

Vε

t95

121002410.847-1.270

30

2.188

0.618

0.178

2.05

12101741

0.784-1.397

25

2.803

0.963

0.033

2.07

２２

3.9 Calculation of Similarity (reference information)
The degree of similarity of the substance to be predicted is calculated using afingerprint* (here, the Tanimoto Coefficient using the PubChem fingerprint [24])for each substance (training data set and support chemicals) assigned to a QSAR
class. Degree of similarity is not required information for predictions but can beused as reference information. The fingerprints for all substances included in
KATE2020 are calculated beforehand.
The degree of similarity T between bitstream X (fingerprint of chemical included
in QSAR class) and bitstream Y (fingerprint of the query chemical) is calculatedusing the formula below.

T = Nc ／ (Nx＋Ny－Nc)

… (5)

Here,

Nx : Bit count of 1 in X
Ny : Bit count of 1 in Y
Nc : Bit count of 1 common to X and Y
The numerical value is between 0 and 1, and the closer the value is to 1, the moresimilar is the substance to be predicted. Figure 3-6 illustrates the similarity calculation*A fixed length bitstream represents chemical substance data, each bit expresses
the presence or absence of a particular feature of the chemical substance:
1 means the chemical substance features the property designated by that bit,while 0 means it does not possess that property.
Example of a fingerprint notation:
1001001000000000110010110000101010000110

Figure 3–6: Image of similarity (Tanimoto coefficient) calculation

２３

The similarity can be verified using the QSAR class detailed view screen (Verify
QSAR screen). Please refer to the KATE2020 Operating Manual† [6. QSAR Class
Information Detailed View (Verify QSAR screen)] for more information regarding
the Verify QSAR screen.
† https://kate2.nies.go.jp/nies/doc/KATEmanual_2020-e.pdf

4. Definition of Applicability Domain – OECD (Q) SAR Validation Principle 3
Here, we explain the applicability domain of the KATE2020 QSAR model and themethod for determining the applicability domain.
4.1 Applicability Domain
The applicability domain of KATE2020 is described in terms of A) structure
applicability domain and B) log P applicability domain.
A)

Applicability domain for structure
The applicability domain for structure is set for each QSAR class andsubstructures are assigned based on the list of substructures for structure
judgement (Table 4–1) for training set data and chemical data with inequality sign(log P judgement for the applicable QSAR class (explained in 4.2 B) is within theapplicability domain only).
Table 4–1: List of substructures for structure judgement for each QSAR class (partial)
QSAR ID

List of Substructures for structure judgement…

12100241

5004,5007,5008,5016,5022,5023,5028,5081,5500

22100741

5074, 5075

32100541

5020,5021,5067,5155…

Please refer to the following link for the substructure list used in structure
judgement.
kate.nies.go.jp/data/Substructures_for_judgement.html

２４B) Applicability domain for log P
The following log P ranges (Table 4–2) for each QSAR class are calculated
beforehand in order to judge log P (explained in 4.2 B).
・ log P range 1: log P minimum value and maximum value of training set data
included in the applicable QSAR class (required for the judgement of “in” and “out of”)
・ log P range 2: log P minimum value and maximum value of training set data andchemical data with inequality sign included in the applicable QSAR class (required
for the judgement of ”out of+)The log P applicability domain is given based on the aforementioned log P range 1.
Table 4–2: log P range for each QSAR classQSAR ID

log P Range 1

log P Range 2…
121002410.09 ~ 4.84

0.09 ~ 4.84

22100741

3.77 ~ 6.17

3.77 ~ 6.89

32100541

1.46 ~ 3.48

1.46 ~ 4.63…
4.2 Applicability Domain Judgement Method
KATE2020 judges whether the predicted toxicity value of the query chemical is within
the range where it can be applied. Judgement by A) structure and by B) log P areconducted and if both are within the applicability domains, the predicted toxicity value isjudged to be within the applicability domain.
A)

Structural Judgement
KATE2020 compares substructures used for structure judgement to judge
whether the structure of a substance to be predicted is within the applicability
domain of the relevant QSAR class (Figure 4–1). There are three possible results
as explained below. Structures judged to be within the applicability domain of aparticular QSAR class are classified as either “in” or “in (conditionally).”

・ in: Within applicability domain
When all substructures for structure judgement contained in the query
chemical are included in the substructure list (refer to Table 4–1) for２５

structure judgement of substances*1 for the applicable QSAR class (pinkarea of Figure 4–1), or when the query chemical possesses none of thesubstructures for structure judgement.

・ in (conditionally): Conditionally within applicability domain
While not satisfying the conditions for “in,” all substructures for structure
judgement for the substance to be predicted are either included in thesubstructure list for structure judgement for the applicable QSAR class or
included in the substructure list for structure judgement *1 of Narcotic
Group*2 (pink and yellow areas of Figure 4–1).

・ out of : Outside applicability domain
Cases where the conditions for neither “in” nor “in (conditionally)” aresatisfied. In other words, when the query chemical possesses substructures
for structure judgement that are neither included in the substructure list
for structure judgement for the applicable QSAR class, nor included in the
Narcotic Group*2 class (structures in the gray area of Figure 4–1).
*1 Here, data with inequality signs whose log P judgement is “in” (within theapplicability domain ) are included.
*2 Low reactivity, and baseline toxicity (narcotic effect) not based on a specificbioactivity. QSAR classes containing chemicals such as aliphatic hydrocarbons,
sulfoxides, aliphatic and aromatic esters, aliphatic and aromatic ketones, andalcohols, whose toxicity can be explained by simple narcotic effect, exist for eachpredicted toxicity type. Narcotic group class is defined to include such QSAR
classes.

２６

Figure 4–1: Structural judgement example

２７B) log P Judgement
KATE2020 judges whether the log P value of the query chemical lies between thelog P minimum value and maximum value (the range shown in Table 4–2 for theapplicable QSAR class) for all training data set (support chemicals data is notincluded) for the applicable QSAR class in order to judge whether the value lies
within the applicability domain. Note that all substances with log P>6.0* are outside
the applicability domain for KATE2020 (a modification for the KATE2020 version).
*The threshold value of 6.0 set here was selected based on a comprehensive
consideration of the following: the cut-off value for predicted acute toxicity in
ECOSAR is 5.0 (some set at 6.4); the cut-off value for predicted chronic toxicity
value is set at 8.0; the upper limit of linearity between log P and log BCF (forexample, in Dimitrov et al. SAR QSAR Environ Res., 13, 177-184, 2010, the upper
limit is 6.1–6.5); and the upper limit of the applicability domain for measurementof log P for highly hydrophobic (log P>4) substances using the HPLC method(prescribed by OECD Test Guideline 117) is 6.
・ in: Within applicability domain (refer to Figure 4–2).
・ out of : Outside applicability domain. Excluding the case of “out of+ ” explained in thefollowing (refer to Figure 4–3).
・ out of+ : Outside applicability domain. However, the log P value of the query chemicallies between the log P minimum value and maximum value (log P range 2 for each QSAR
class row in Table 4–2) for all substances including the training data set and support
chemicals for the applicable QSAR class (refer to Figure 4–4).

Figure 4–2: log P judgement example (in)２８Figure 4–3: log P judgement example (out of)

Figure 4–4: log P judgement example (out of+)２９

5. Validation – OECD (Q) SAR Validation Principle 4
5.1 Internal Validation
Here, the goodness-of-fit (to what extent the model explains responses in training set
data, in other words, variances of predicted toxicity values) and robustness (the stabilityof predictions if one or more data is removed from a training set) of KATE2020 QSAR
models are assessed.
5.1.1 Indicators used for internal validation
Internal validation uses the indicators listed in Table 5–1.
Table 5–1: Internal validation indicators
indicator

Explanation
An index of goodness of fit (coefficient of determination), taking a value between 0 and 1,
the closer to 1, the better the fit.

R2

R2 =1－

∑

(

)

∑

(

)

yi : Measured toxicity value of the i-th training set data𝑦 : Predicted toxicity value of the i-th training set data𝑦 : Average of measured toxicity values of training set datan : Number of training set data
Robustness index (leave-one-out method); the closer it is to 1, the more robust it is, and it
can be negative.

Q2 = 1－

∑
∑

(

)

,

(

)

Q2
yi : Measured toxicity value of the i-th training set data𝑦 , : Predicted toxicity values from regression equations constructed with the remaining
training set data, excluding the i-th training set data𝑦 : Average of measured toxicity values of training set datan : Number of training set data
5.1.2 Internal validation results
Table 5–2 shows the internal validation results for QSAR classes that meetstatistical criteria (R2 ≧ 0.7, Q2 ≧ 0.5 and n ≧ 5) [25–30].

３０

Table 5–2: List of QSAR classes meeting statistical criteria(sorted by n value for each organism and acute/chronic)
Organics

Acute/Chronic

Fish

Acute

Fish

QSAR Class

R2

Q2

n

narcotic group Fish Acute

0.87

0.87

154

Acute

CNOS_X halogen unreactive

0.76

0.75

95

Fish

Acute

phenol unreactive unhindered

0.88

0.87

58

Fish

Acute

phenol unreactive unhindered w/o bisphenol, HRAC Ea

0.87

0.86

57

Fish

Acute

CO_X alcohol unreactive w/o EO Fish

0.89

0.88

46

Fish

Acute

CO_X ether unreactive

0.87

0.86

44

Fish

Acute

COns_X ketone unreactive

0.84

0.82

41

Fish

Acute

CNOS_X aromatic n unreactive excl. triazine Fish

0.76

0.74

39

Fish

Acute

phenol unreactive unhindered w/o X

0.89

0.88

37

Fish

Acute

CNO_X ester unreactive

0.72

0.68

37

Fish

Acute

Cnos_X heteroaromatic unreactive

0.84

0.81

30

Fish

Acute

CNO_X nitro mono unreactive

0.74

0.70

28

Fish

Acute

COns_X ketone unreactive aliphatic

0.88

0.86

27

Fish

Acute

0.84

0.81

26

Fish

Acute

0.87

0.86

24

Fish

Acute

amine primary unreactive NH2=1 aliphatic
C_X hydrocarbon unreactive aliphatic w/ X, excl.
Halomethane
C_X hydrocarbon unreactive aromatic fused R=0 w/o X

0.85

0.79

24

Fish

Acute

amine primary unreactive aromatic w/ NO2,SO

0.82

0.79

24

Fish

Acute

CO_X primary alcohol

0.92

0.9

22

Fish

Acute

CNOS_X amine sec,tert w/o n

0.91

0.89

21

Fish

Acute

Cnos_X heteroaromatic unreactive Fish, Daphnid

0.82

0.78

21

Fish

Acute

CNO_X amide unreactive

0.80

0.76

21

Fish

Acute

C_X hydrocarbon unreactive aliphatic w/o X

0.73

0.68

21

Fish

Acute

CNO_X aldehyde normal aromatic

0.85

0.81

19

Fish

Acute

CNOS_X amine aromatic w/ aliphatic carbon

0.77

0.66

19

Fish

Acute

CNO_X amine sec,tert unreactive aliphatic

0.91

0.86

16

Fish

Acute

CN_X amine sec,tert unreactive aliphatic

0.73

0.64

16

Fish

Acute

CN_X nitrile unreactive

0.87

0.84

15

Fish

Acute

COS_X ketone unreactive aromatic

0.90

0.85

14

Fish

Acute

Fish

0.83

0.76

14

Acute

CNOS_X amine primary reactive w/o ortho,paraOH,NH2
amine primary unreactive NH2 >1

0.82

0.75

14

Fish

Acute

CN_X amine sec,tert unreactive aromatic

0.84

0.78

12

Fish

Acute

CNOS_X acid unreactive

0.77

0.63

12

３１

Organics

Acute/Chronic

Fish

Acute

Fish

QSAR Class

R2

Q2

n

Cnos_X heteroaromatic reactive Fish

0.72

0.59

12

Acute

n+, N+

0.75

0.61

11

Fish

Acute

CNOS_X aromatic n reactive excl. nitrile

0.73

0.53

11

Fish

Acute

phenol reactive w/o ortho,para-OH,NH2, w/o nitro

0.75

0.64

10

Fish

Acute

urea unreactive

0.95

0.87

9

Fish

Acute

amide reactive excl. C=O,S,N

0.92

0.85

8

Fish

Acute

CNOS_X carbamate unreactive Fish

0.9

0.68

8

Fish

Acute

ester reactive methacrylate

0.76

0.54

8

Fish

Acute

CS_X sulfide unreactive

0.70

0.59

8

Fish

Acute

CN_X nitrile unreactive aliphatic

0.97

0.95

7

Fish

Acute

COS_X methacrylate

0.87

0.66

7

Fish

Acute

COS_X thiol

0.96

0.92

6

Fish

Acute

0.94

0.87

6

Fish

Acute

0.79

0.55

6

Fish

Acute

CNOSP_X phosphorus unreactive
CNOS_X N-hetero unreactive w/o amine, aldoxime,
carbamate
CO_X alcohol unreactive w/ EO

0.98

0.97

5

Fish

Acute

C_X hydrocarbon unreactive halomethane

0.94

0.85

5

Fish

Acute

CNOS_X amine tert unreactive w/ C=O

0.88

0.61

5

Fish

Acute

phenol unreactive bisphenol

0.87

0.63

5

DaphnidAcutenarcotic group Daphnid Acute

0.71

0.70

83

Daphnid

Acute

CNOS_X halogen unreactive

0.86

0.84

44

DaphnidAcutephenol unreactive unhindered

0.82

0.78

28

DaphnidAcutephenol unreactive unhindered w/o bisphenol, HRAC Ea

0.8

0.76

27

DaphnidAcutephenol unreactive unhindered w/o X

0.87

0.83

19

Daphnid

Acute

CNOS_X aromatic n unreactive Daphnid

0.85

0.82

17

DaphnidAcute

C_X hydrocarbon unreactive aromatic fused R=0 w/o X

0.8

0.74

17

Daphnid

Acute

CO_X ether unreactive

0.83

0.74

15

DaphnidAcute

C_X hydrocarbon unreactive aliphatic w/ X

0.82

0.77

14

Daphnid

Acute

CO_X alcohol unreactive w/o EO Daphnid

0.78

0.72

14

DaphnidAcuteamine primary unreactive NH2 >1

0.71

0.60

12

Daphnid

Acute

Cnos_X heteroaromatic unreactive

0.94

0.90

11

DaphnidAcutephenol unreactive hindered

0.76

0.64

11

Daphnid

Acute

CNO_X amine sec mono w/o n Daphnid

0.75

0.58

9

Daphnid

Acute

CNO_X ester unreactive Daphnid

0.93

0.86

8

Daphnid

Acute

CNO_X nitro mono unreactive Daphnid

0.85

0.74

8

３２

Organics

Acute/Chronic

Daphnid

Acute

Daphnid

QSAR Class

R2

Q2

n

C_X hydrocarbon unreactive aliphatic w/ X, excl.
gem,vic-Cl,TCE

0.98

0.97

7

Acute

Cnos_X heteroaromatic unreactive Fish, Daphnid

0.96

0.9

7

Daphnid

Acute

CN_X amine sec,tert unreactive aromatic

0.97

0.92

6

Daphnid

Acute

CO_X primary alcohol

0.95

0.76

6

Daphnid

Acute

CN_X amine sec,tert unreactive aliphatic

0.89

0.64

6

Daphnid

Acute

CNO_X amide unreactive Daphnid

0.88

0.70

6

DaphnidAcuten+, N+

0.87

0.77

6

Daphnid

Acute

CNOS_X N-hetero unreactive w/o amine, aldoxime,
carbamate

0.78

0.63

6

DaphnidAcuteester reactive methacrylate

0.82

0.60

5

Daphnid

Acute

CNOS_X amine sec,tert multi-functional

0.79

0.53

5

Daphnid

Acute

CNO_X imide unreactive

0.78

0.59

5

Alga

Acute

narcotic group Alga Acute

0.76

0.74

52

Alga

Acute

phenol unreactive unhindered w/o bisphenol, HRAC Ea

0.8

0.77

26

Alga

Acute

aromatic n reactive Alga

0.78

0.72

10

Alga

Acute

CO_X ether unreactive excl. HRAC Ea Alga

0.92

0.82

9

Alga

Acute

CNOS_X sulfur reactive excl. disulfide Alga

0.86

0.54

8

Alga

Acute

CO_X alcohol unreactive w/o halogen, acid, EO

0.95

0.9

6

Alga

Acute

CNO_X ester unreactive Alga

0.94

0.85

6

Alga

Acute

CO_X primary alcohol

0.91

0.79

6

Alga

Acute

0.88

0.52

6

Alga

Acute

0.97

0.91

5

Alga

Acute

COS_X thiol
C_X hydrocarbon unreactive aliphatic w/ X, excl.
Halomethane
Cnos_X heteroaromatic excl. pyridine Alga

0.83

0.52

5

Fish

Chronic

narcotic group Fish Chronic

0.82

0.75

12

Fish

Chronic

Cnos_X unreactive Fish Chronic

0.76

0.68

12

Fish

Chronic

C_X hydrocarbon unreactive

0.78

0.68

11

Daphnid

Chronic

narcotic group Daphnid Chronic

0.70

0.68

74

Daphnid

Chronic

C_X hydrocarbon unreactive aromatic fused R=0 w/o X

0.87

0.83

15

Daphnid

Chronic

CNO_X amine sec,tert unreactive w/ N-Oxide,Nitroso

0.81

0.74

15

Daphnid

Chronic

CO_X alcohol unreactive w/o EO Daphnid

0.82

0.75

14

Daphnid

Chronic

CO_X ether unreactive

0.88

0.76

10

Daphnid

Chronic

CNO_X ester unreactive Daphnid

0.84

0.73

8

Daphnid

Chronic

CNO_X amide unreactive Daphnid

0.83

0.74

8

Daphnid

Chronic

Cnos_X heteroaromatic unreactive

0.83

0.64

7

Daphnid

Chronic

Cnos_X heteroaromatic unreactive Fish, Daphnid

0.83

0.64

7

３３

Organics

Acute/Chronic

Daphnid

Chronic

Daphnid

QSAR Class

R2

Q2

n

C_X hydrocarbon unreactive aliphatic w/ X, excl.
gem,vic-Cl,TCE

0.98

0.97

6

Chronic

COns_X ketone unreactive

0.92

0.59

5

Alga

Chronic

phenol unreactive unhindered w/o X, bisphenol, HRAC
Ea

0.72

0.65

18

Alga

Chronic

CO_X ether unreactive excl. HRAC Ea Alga

0.89

0.86

15

Alga

Chronic

CNO_X nitro mono unreactive

0.78

0.53

13

Alga

Chronic

aromatic n reactive Alga

0.77

0.70

11

Alga

Chronic

CO_X alcohol unreactive w/o halogen, acid, EO

0.87

0.81

10

Alga

Chronic

amine primary unreactive NH2>1, Nv3 <3

0.78

0.70

10

Alga

Chronic

ester unreactive w/o acid

0.88

0.79

9

Alga

Chronic

CNOS_X sulfur reactive excl. disulfide Alga

0.82

0.66

9

Alga

Chronic

CNOSP_X phosphorus all

0.74

0.64

9

Alga

Chronic

CNO_X ester unreactive Alga

0.90

0.79

8

Alga

Chronic

COS_X thiol

0.85

0.07

7

Alga

Chronic

CNO_X amine sec,tert unreactive aliphatic

0.90

0.79

6

Alga

Chronic

CNOSP_X phosphorus unreactive

0.95

0.86

5

Alga

Chronic

CNOS_X N-hetero unreactive w/o amine, aldoxime,
carbamate

0.80

0.57

5

３４

5.2 External Validation
Here, we assess KATE2020 QSAR model external predictivity performance (how well
can new data that was not used in model development be predicted).
5.2.1 Data (test set) used in external validation
Toxicity values listed in OECD SIDS (Screening Information DataSet) were used as
the test set for external validation. Data deviating from the standard test duration of±24 hours and data with reliability (Klimisch code) of 3 or 4 were excluded, while test
results for species not included in KATE2020 (e.g., Danio rerio and Desmodesmus

subspicatus) were adopted. Further, if multiple test results existed for a single
substance, the data of highest reliability was adopted. if multiple test results with thehighest reliability existed, the geometric mean value of those data was taken.
Table 5–3 shows the number of test set substances for each predicted toxicity type andthe data points where predicted values were obtained for KATE2020 classes. All
predicted values are included, including where multiple predicted values were obtained
for a single substance.
Table 5–3: Test set data points
Acute

Chronic

Fish

Daphnid

Alga

Fish

Daphnid

Alga

Number of Chemicals

178

196

141

3

46

100

Number of Predicted Values

199

207

93

1

55

76

３５

5.2.2Results
Of the data of Table 5–3, Table 5–4 shows the number and ratio of data points within theapplicability domain (including “in (conditionally)” structure judgement) where the ratio
of predicted value to measured value is less than one order of magnitude (1/10th to 10
times) and ess than two order of magnitude (1/100th to 100 times) for all QSAR classes.
The data is also present in graph form in Figure 5–1. In the figure, inside the blue dottedline is the range of one order of magnitude, and the red single-pointed line the two orderof magnitude.
Table 5–4: Number and ratio of data points where ratio of predicted to measured value isless than one order and two order of magnitude for all QSAR classes
Acute
Daphnid

Fish
Data falling within 1order of magnitude
Data falling within 2order of magnitude
All Data

Alga

Chronic
Daphnid

Fish

Alga

Number

Ratio

Number

Ratio

Number

Ratio

Number

Ratio

Number

Ratio

Number

Ratio

176

88%

147

71%

66

71%

0

0%

39

71%

51

67%

196

98%

199

96%

89

96%

0

0%

53

96%

70

92%

199

207

93

1

55

Figure 5–1: Predicted versus measured values for all QSAR classes

３６76

The number and ratio of data points assigned to QSAR classes that satisfy statisticalstandards (Table 5–2) that are within the applicability domain (log P judgement andstructure judgement are both “in,” including “in (conditionally)” structure judgement)
where the ratio of predicted value to measured value is less than one order of magnitude
and two order of magnitude for QSAR classes that satisfy statistical criteria are shown in
Table 5–5 and graphically represented in Figure 5–2.

Table 5–5: Number and ratio of data points where ratio of predicted to measured value is less than
one order and two order of magnitude for QSAR classes satisfying statistical criteria
Acute
Daphnid

Fish
Data falling within 1order of magnitude
Data falling within 2order of magnitude
All Data

Alga

Chronic
Daphnid

Fish

Alga

Number

Ratio

Number

Ratio

Number

Ratio

Number

Ratio

Number

Ratio

Number

Ratio

136

90%

77

73%

25

69%

0

NA

10

77%

16

59%

148

98%

102

96%

35

97%

0

NA

13

100%

24

89%

151

106

36

0

13

Figure 5–2: Predicted values versus measured values for QSAR classes satisfying statistical criteria

３７27

6. Interpretation of mechanism – OECD (Q) SAR Validation Principle 5
A linear relationship between the logarithms of hydrophilicity (membrane
permeability) and toxicity has been reported[31], and KATE2020 constructs QSAR
equations based on log P as an explanatory variable and toxicity value (effective
concentration) as a dependent variable. Each QSAR class in KATE consists of chemical
substances that possess characteristic structures, and each structure class is defined by
the parameter for number of substructures (Chapter 3, Section 3.5). Substances
possessing substructures considered to be highly reactive are classified as reactive
structure classes and are believed to exhibit toxicity through their nonspecific highreactivity. Substances without such substructure are considered to have low reactivity
and are classified as unreactive structure classes[32]. Some structure classes contain
substructures that act via specific mechanisms. Please refer to the following forstructure classes and substructure list.
Structure class list: https://kate2.nies.go.jp/nies/Structure_Classes.php
Substructure class list: https://kate2.nies.go.jp/nies/Substructures.php

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４１