KATE2020
Ecotoxicity Prediction System—Internet Version
Operating Manual (March 25, 2024, version)

KATE2020 is a tool for the prediction of chemical ecotoxicity based on the following data:

50% lethal concentration (LC50) in the fish acute toxicity test

50% effective concentration (EC50) in the Daphnia magna 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 are for reference use only and cannot be used to satisfy therequirements for ecotoxicity test results necessary for notification under Japanese Chemical
Substances Control Law (Act on the Evaluation of Chemical Substances and Regulation of Their
Manufacture, etc.).

For questions, please contact the administrators at
Health and Environmental Risk Division, National Institute for Environmental Studies KATE
Contact Desk

Copyright(C) 2019–2024 Ministry of the Environment, Government of Japan.
All Rights Reserved

1Operating Manual Revision History
Version
0.1
0.8
1.0
1.0.1
1.0.2
2.0
3.0
4.0

Date of issue
March 29, 2018
January 30, 2019
May 23, 2019
June 4, 2019
July 30, 2019
March 30, 2022
March 30, 2022
March 30, 2023
March 30, 2023
March 25, 2024
March 25, 2024

Revision history
Manual for KATE2017 beta version
Provisional version
Official version
Minor update
Explanation of JSME Editor was updated
Manual for KATE2020 version 3.0
KATE2020 version 3.0 released
Manual for KATE2020 version 4.0
KATE2020 version 4.0 released
Manual for KATE2020 version 5.0
KATE2020 version 5.0 released2

Table of Contents

List of Abbreviations ....................................................................................................................... 5
1. Introduction ................................................................................................................................ 6(1) What are KATE and KATE2020?................................................................................................. 6(2) Major Updates from KATE2020 Version 4.0 to Version 5.0 ...................................................... 6(3) Major Updates from KATE2020 Version 3.0 to Version 4.0 ...................................................... 6(4) Major Updates from KATE2020 Version 2.0 to Version 3.0 ...................................................... 7(5) Major Updates from KATE2020 Version 1.1 to Version 2.0 ...................................................... 7(6) Major Updates from KATE2020 Version 1.0 to Version 1.1 ...................................................... 7(7) Major Updates from KATE2017 on NET to KATE2020 Version 1.0 ........................................... 7(8) Development History up to the Release of KATE2017 on NET ................................................. 8(9) About Support Chemicals .......................................................................................................... 8(10) About log P .............................................................................................................................. 8(11) Disclaimer ................................................................................................................................ 9(12) Acknowledgments ................................................................................................................... 9(13) References ............................................................................................................................. 10
2. Overview of KATE2020.............................................................................................................. 11(1) QSAR Prediction Procedures ................................................................................................... 11(2) Assignment of QSAR Equations, Prediction of Toxicity Values, and Judgement of Applicability
Domain..................................................................................................................................... 13
3. Log In ......................................................................................................................................... 17
4. Input of Query Chemical into KATE2020 .................................................................................. 18(1) Acceptable and Unacceptable SMILES Strings ........................................................................ 18(2) Direct Input of a SMILES String ................................................................................................ 18(3) Convert a Drawing of a Chemical Structure into a SMILES String ........................................... 18(4) Convert a CAS Number or Chemical Name into a SMILES String ............................................ 19(5) User-defined Log P Values ....................................................................................................... 20(6) User-defined CAS Number and Chemical Name ..................................................................... 21(7) Skip KOWWINTM Calculation.................................................................................................... 21
5. QSAR Prediction Results ........................................................................................................... 22(1) Summary of the Query Chemical ............................................................................................ 23
3

(2) QSAR Prediction Results .......................................................................................................... 24
6. Details of QSAR Class Information (Verify QSAR Screen) ......................................................... 27(1) Summary of QSAR Class........................................................................................................... 28(2) Graph ....................................................................................................................................... 28(3) Information About the Query Chemical .................................................................................. 31(4) Information About the Regression Equation .......................................................................... 31(5) Chemical Clicked Last .............................................................................................................. 32(6) List of Chemical Structures ...................................................................................................... 33(7) Chemical Data .......................................................................................................................................... 34(8) Structure Class Definition ........................................................................................................................ 35(9) Substructure of the Query Chemical ..................................................................................................... 35

7. Prediction of Multiple Chemical Substances ............................................................................ 37(1) Input File: “SMILES List” ......................................................................................................................... 37(2) Prediction Procedures ............................................................................................................................ 38(3) Additional information ........................................................................................................................... 40
8. Printing the Prediction Results ................................................................................................. 41

4

List of Abbreviations
EC50: 50% effective concentration
The concentration of a chemical dissolved in test water expected to produce a certain
effect in 50% of test organisms.
KATE: KAshinhou Tool for Ecotoxicity
The name of the ecotoxicity QSAR system developed by the Center for Health andEnvironmental Risk Research of the National Institute for Environmental Studies,Japan. It is pronounced as “Kate”.
KOWWINTM
A program for estimating log P values of organic compounds. The program is part of the
EPI (Estimation Programs Interface) SuiteTM, which is a suite of estimation programs
developed by the US EPA for rapid toxicity screening of chemicals.
LC50: 50% lethal concentration
The concentration of a chemical dissolved in test water that causes death in 50% of test
organisms.
Log P: Logarithm of the octanol/water partition coefficient
The logarithm of the ratio of the concentration of a chemical between the solvents 1octanol and water at equilibrium. It is considered an index of the hydrophobicity of achemical substance. Log P values ignore ionized query chemical.
(http://www.eic.or.jp/ecoterm/?act=view&serial=295; description retrieved from EIC Net
on March 1, 2022)
NOEC: No observed effect concentration
The concentration of a chemical causing no statistically or biologically significant
increase in the frequency or intensity of any effect in the tested group compared with thecontrol group. Concentration division just under LOEC (Least Observed EffectConcentration). (http://www.env.go.jp/chemi/report/ierac18/1-ref2.pdf; description
retrieved from the glossary of the Ministry of the Environment of Japan on March 1,
2022)(Q)SAR: (Quantitative) structure–activity relationshipThe relationship between the structural characteristics or the physicochemical constantof a chemical and its biological activities (e.g., toxicity) is called the Structure–Activity
Relationship (SAR), and the quantitative relationship is called the Quantitative
Structure–Activity Relationship (QSAR). When both are referred to, (Q)SAR is used. For
example, SAR refers to 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.
(http://www.env.go.jp/chemi/report/ierac18/1-ref2.pdf; description retrieved from theglossary of the Ministry of the Environment of Japan on March 1, 2022)
SMARTS: SMiles ARbitrary Target Specification
An extension of SMILES that is used to describe molecular substructures (seehttp://www.daylight.com/dayhtml_tutorials/languages/smarts/ for a tutorial on using thenotation; accessed on March 1, 2022)
SMILES: Simplified Molecular-Input Line-Entry System
Line notation that uses ASCII characters to describe the molecular structure of achemical (see http://www.daylight.com/smiles/index.html for a tutorial on using thenotation; accessed on March 1, 2022)
US EPA: United States Environmental Protection Agency

5

1. Introduction(1) What are KATE and KATE2020?
KATE (KAshinhou Tool for Ecotoxicity) is a quantitative structure–activity relationship(QSAR)–based tool for the prediction of chemical ecotoxicity. The tool was developed by theCenter for Environmental Risk Research of the National Institute for Environmental Studies(currently Health and Environmental Risk Devision, National Institute for Environmental
Studies) under contract with the Japanese Ministry of the Environment1).
The beta version of the tool was released as KATE2017 on NET in 2018, which was then updated
in early 2019. Version 1.0 of KATE2017 on NET was officially released in mid-2019. In early
2020, version 1.0 of KATE2020 was released, followed in early 2021 by version 2.0. KATE2020(version 2.0) operates over the internet and is available at https://kate.nies.go.jp/onnet2020e.html.
Latest version, KATE 2020 version 4.0, uses ecotoxicity test data published by the JapaneseMOE3) as well as fish acute toxicity test data from the US EPA fathead minnow database5) as
chemicals dataset. QSAR equations are updated when additional test results become available.
A query chemical is entered into KATE2020 by using simplified molecular-input line-entry
system (SMILES) notation, which can be obtained from within KATE2020 by entering a CAS
number or by drawing a chemical structure in the provided JavaScript Molecule Editor (JSME
Editor). The SMILES string is then used to perform QSAR prediction based on log P value.
Currently, KATE2020 predictions are based on the following data:2)
 50% lethal concentration in the fish acute toxicity test (OECD TG 203)
 No-observed-effect concentration in the fish early-life-stage toxicity test (OECD TG 210)
 50% effective concentration in the Daphnia magna acute immobilization test (OECD TG 202)
 No-observed-effect concentration in the Daphnia magna reproduction test (OECD TG 211)
 50% effective concentration and no-observed-effect concentration in the algal growth
inhibition test (OECD TG 201)(2) Major Updates from KATE2020 version 4.0 to version 5.0
Improvements of user interfacesi) Input screen and multiple chemicals prediction results screen were improvedii) Download functionality of multiple chemicals prediction results was addediii) Display of structure class definition was improved
Updates to QSAR modelsi) Reviewing toxicity test results for alga acute and chronic, recalculated toxicity of somechemicals based on the latest test guideline, some chemicals (e.g., mixture of isomers) werealtered from training set to support chemicals.
ii) Two aliphatic primary amines and one epoxide, whose toxicity testing was done in FY2022,
were added to training set.
iii) Recalculated statistics of regression equations of QSAR classes(3) Major Updates from KATE2020 version 3.0 to version 4.0
Improvements of user interfacesi) Log in system was revised so that users can use KATE2020 without user ID and password.
ii) Some interfaces (prediction results screen, verify QSAR screen etc.) were modified.
Updates to QSAR modelsi) Reviewing toxicity test results for fish acute, recalculated toxicity of some chemicals based
6

on the latest test guideline, some chemicals were altered from training set to support
chemicals.
ii) Two thiols (fish and Daphnid acute) and one imide (Daphnid acute) compounds, whose
toxicity testing was done in FY2021, were added to training set.
iii) Recalculated statistics of regression equations of QSAR classes
Updates to QSAR class namesi) Scrutinizing the names of all QSAR classes, some of them were fixed.

(4) Major Updates from KATE2020 version 2.0 to version 3.0
Updates to QSAR modelsi) Reviewing toxicity test results for daphnid acute, recalculated toxicity of some chemicals
based on the latest test guideline, some chemicals were altered from training set to support
chemicals.
ii) Recalculated statistics of regression equations of QSAR classes
Updates to QSAR class namesi) Scrutinizing the names of all QSAR classes that satisfy statistic criteria (R2 ≥ 0.7, Q2 ≥ 0.5,
and n ≥ 5), some of them were fixed.

(5) Major Updates from KATE2020 version 1.1 to version 2.0
Updates to QSAR modelsi) One of the criteria for QSAR classes displayed on the prediction results screen by default
was changed from Q2 ≥ 0.6 to Q2 ≥ 0.5.
ii) The algal chronic toxicity QSAR equation “CNOS_X basic aromatic n unreactive” waschanged in accordance with the correction of toxicity data.
Updates to displays and user interfacesi) An individual structure judgement result for each substructure was added.
ii) The ability to display a format for printing was added.
iii) An issue in batch mode where prediction would stop before completion when an error was
encountered was fixed.
iv) Some expressions were corrected.
iv) An issue in batch mode where prediction would stop before completion when an error was
encountered was fixed.
Updates to structure class namesi) Prefixes such as “CN_X” were removed or added.
ii) Typos were fixed.
iii) Unnecessary notations were removed.
iv) Prefix notations were unified.
v) The “reactive/unreactive” notations were corrected.
vi) Predicted toxicity types were corrected.

(6) Major Updates from KATE2020 version 1.0 to version 1.1i) The predicted toxicity value was updated to be displayed in exponential notation (ex. 2.3e7) when the value is greater than 10−5, equal to 106, or less than 10.
ii) The predicted toxicity value was updated to be displayed to two significant figures.
iii) An issue where prediction was not always executed, even when the input information wascorrect, was fixed.
iv) Other slight changes in some expressions were made.
(7) Major Updates from KATE2017 on NET to KATE2020 version 1.0
Updates to QSAR modelsi) The tool for estimating log P was changed from ClogP to KOWWIN™. Some QSAR models
7were modified in accordance with this change.
ii) KOWWIN™ measured value was removed from the priority list for log P value to leave only
two options: 1. user input value and 2. KOWWIN™ estimated value.
iii) Chemical substances with log P > 6.0 were excluded from calculation of the QSAR equation.
iv) Additional training set data and QSAR classes were added, and some were removed.
v) Chemicals that were not used for QSAR calculation (support chemicals with data with log
P > 6.0, data with an inequality sign, and outliers) were updated to be displayed as
information.
Updates to displays and user interfacesi) Data lists of training set data and support chemicals included in the QSAR class were added.
ii) Definition lists of structure class corresponding to QSAR class were added.
iii) A sorting function was added to the chemical list on the “Verify QSAR” screen.
iv) The prediction and confidence intervals were linked to the additional regression line
calculated by excluding training set data.
v) A checkbox to skip KOWWIN™ calculation was added.

(8) Development History up to the Release of KATE2017 on NET
The beta version of the KATE system was released in January 2008 and KATE2009 on theInternet was released in March 2009. In March 2011, KATE2009 was updated to KATE2011
with the addition of chemicals data, updated rules for structure classification, updated structure
judgement, and addition of substructures related to skin sensitization. KATE2011 is stillavailable at https://kate.nies.go.jp/onnet.html (Japanese only).
In March 2018, the beta version of KATE2017 on NET was released. This release was followedby the official release of version 1.0 in January 2019. The main changes between KATE2011
and KATE2017 on NET were updates to the substructure language and search program from
FITS (a combined substructure language and search program) to a combination of SMARTS
notation and the CDK search program, and the addition of predictions for algal and chronic
toxicity, toxicity data with inequality signs, and structure classes. In addition, the QSAR models
received a significant update, the log P calculation module was changed from ClogP (Daylight
Chemical Information Systems, Inc.) to KOWWINTM (US EPA), displays, user interfaces were
updated, and the display language was changed to English.

(9) About Support Chemicals
In KATE2020, the following data are not used for QSAR model construction and are displayed
as “Support Chemicals” for information purposes only:i) data with estimated log P > 6.0ii) data with an inequality sign (such as limit test)iii) mixture of isomers
However, data with an inequality sign that fall within the applicability domain of log P are also
used for the structure judgement.

(10)

About log P
KATE2020 utilizes the log P prediction model KOWWINTM (with permission from the US EPA)
to obtain log P values used for the toxicity prediction6). Users must acknowledge that they agree
to the KOWWINTM Licensing Policy each time at login to KATE2020. The policy is provided
here for reference:
KOWWIN v1.69 (April 2015)8

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 personal andbusiness computers.
Users may not alter, modify, merge, adapt or prepare derivative works from the software.
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.

(11)Disclaimer
KATE2020 is provided as a tool for obtaining information on the potential degree of
ecotoxicological effects of chemicals. The Japanese MOE and the National Institute forEnvironmental Studies give no guarantee about the accuracy of ecotoxicity values provided by
KATE2020 and assume no responsibility whatsoever for any damages resulting from the use of
ecotoxicity values predicted by KATE2020.
In addition, values predicted by KATE2020 cannot be used to satisfy the requirements for
ecotoxicity test results necessary for notification under the Japanese Chemical SubstanceControl Law (Act on the Evaluation of Chemical Substances and Regulation of Their
Manufacture, etc.).
For copyright information and instructions for linking to KATE2020, please visit the policy pageof the KATE website: https://kate.nies.go.jp/spolicy-e.html.

(12) Acknowledgments
KATE2020 uses data obtained from the following software applications and libraries. We
express our sincere appreciation to each of the development teams.
Open Babel (http://openbabel.org/wiki/Category:Installation)□□

JSME Molecular Editor (https://jsme-editor.github.io/)• B Bienfait and P Ertl, JSME: A free molecule editor in JavaScript, J. Cheminform. 5:24
(2013). doi:10.1186/1758-2946-5-24.

□

CDK (Chemistry Development Kit) (https://cdk.github.io/)
E Willighagen et al., The Chemistry Development Kit (CDK) v2.0: Atom typing,
depiction, molecular formulas, and substructure searching, J. Cheminform. 9:33 (2017).
doi:10.1186/s13321-017-0220-4.
• JW May and C Steinbeck, Efficient ring perception for the Chemistry Development Kit,
J. Cheminform. 6:3 (2014). doi:10.1186/1758-2946-6-3.
• C Steinbeck et al., Recent developments of the Chemistry Development Kit (CDK) - anopen-source Java library for chemo- and bioinformatics, Curr. Pharm. Des 12:21112120 (2006). doi:10.2174/138161206777585274.
• C Steinbeck et al., The Chemistry Development Kit (CDK): An open-source Javalibrary for chemo- and bioinformatics, J. Chem. Inf. Comput. Sci. 43:493-500 (2003).
doi:10.1021/ci025584y.
•□

KOWWINTM
9(https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-program-interface)(all URLs accessed March 1, 2023)(13) References
1) https://kate.nies.go.jp (accessed March 30, 2023)
2) http://www.env.go.jp/chemi/sesaku/01.html (accessed March 30, 2023)
3) http://www.env.go.jp/chemi/sesaku/seitai.html (accessed March 30, 2023)
4) https://archive.epa.gov/med/med_archive_03/web/html/fathead_minnow.html(accessed March 1, 2023)
5) A Furuhama, T Toida, N Nishikawa, Y Aoki, Y Yoshioka, and H Shiraishi: Development ofan ecotoxicity QSAR model for the KAshinhou Tool for Ecotoxicity (KATE) system, March2009 version, SAR QSAR Environ. Res., 21 (5), 403 (2010).
6) https://www.epa.gov/tsca-screening-tools/epi-suitetm-estimation-program-interface/(accessed March 1, 2023)

10

2. Overview of KATE2020(1) QSAR prediction Procedures
Figure 2-1 shows a summary of how KATE2020 performs toxicity prediction for a query
chemical.

Figure 2-1. Summary of QSAR prediction in KATE2020
I. The structure of the query chemical is input using SMILES notation.
II. Based on the SMILES string input by the user, KATE2020 calculates QSAR equations,predicts toxicity values, and judges the applicability domain. This is accomplished in five
steps:(i) Extraction of the substructures of the query chemical(ii) Extraction of structure classes*1 based on combination of multiple substructures(iii) Assignment of QSAR class*2 corresponding to the structure class for each type of predicted
toxicity type (one structure class may be assigned to more than one QSAR class for eachtype of predicted toxicity).
(iv) Calculation of toxicity values using the QSAR equation*3 for each assigned QSAR class(v) Judgement of applicability domain (structure judgement and log P judgement)*1 Classification by “AND/OR” combination of substructures*2 Classification based on the chemical structure for each type of predicted toxicity*3 A simple linear regression equation using log P as the descriptor that is created for the
training set data included in the QSAR class.

11III. Output of prediction results(i) When a query chemical is not assigned to a QSAR class, the chemical is assigned to
Unclassified class*4.
(ii) Unclassified class and QSAR classes not satisfying R2≥0.7, Q2≥0.5, and n≥5 are notdisplayed*5 by default.
*4 QSAR class that is not assigned to any QSAR class.
*5 R2, Q2, and n are the coefficient of determination, internal validation index (leave-one-out
method), and the number of training set data respectively, which have been calculated foreach QSAR class in advance.
Figure 2-2 shows a representative prediction flow using 1-pyridin-3-ylethanone as the query
chemical.

Figure 2-2. Representative prediction flow using 1-pyridin-3-ylethanone as the query chemical
12

*Actual names of Ketone, Aromatic Ketone, Aromatic n and Unreactive in Figure 2-2 are as
follows.
Ketone: COns_X ketone unreactive
Aromatic Ketone: COS_X ketone unreactive aromatic
Aromatic n: CNOS_X basic aromatic n unreactive
Unreactive: CNO_X unreactive Fish chronic, w/ N,OAssignment of QSAR Equations, Prediction of Toxicity Values, and Judgement of Applicability
DomainKATE2020 performs the following process to construct the QSAR equations and predict toxicity
values based on the structure of the query chemical.
(i) Extraction of substructures
Based on the list of substructure definitions (SMARTS), the number of each substructure
contained in the query chemical is determined. The CDK library is utilized for the
calculation of the number of substructures by SMARTS.
(ii) Extraction of structure classes
Based on the list of structure class definitions (“AND/OR” combination of substructures),all the structure classes that match the structure of the query chemical are extracted.
(iii) Assignment of QSAR class
The system assigns QSAR classes that correspond to the structure class of the query
chemical for each type of predicted toxicity based on the list of QSAR class definitions,where each QSAR class is defined by a type of predicted toxicity and a structure class.
KATE2020 may assign more than one QSAR class for each type of predicted toxicity. If
the query chemical is not classified to any QSAR class, it is assigned to “Unclassified class”.
(iv) Calculation of toxicity values by using QSAR equations
There is a QSAR equation corresponding to each QSAR class, and so the log P value of thequery chemical is substituted into each QSAR equation to calculate the log (1/toxicity
value( (in mmol/L); the system then converts that value into a toxicity value (in mg/L)
using the molecular weight of the query chemical.
(v) Judgement of applicability domain
KATE2020 judges whether a predicted toxicity value of the query chemical is within theapplicability domain. It performs two types of judgment: A) structure judgement, and B)
log P judgement. When the results of both judgement types fall within the applicability
domain, the predicted toxicity value of KATE2020 is considered appropriate.
A) Structure Judgement
KATE2020 judges whether the structure of the query chemical falls within the applicability
domain of the QSAR class classified in (ii) by comparing the “substructures for structure
judgement” *1 (Figure 2-3). There are three possible judgements: in, in (conditionally), and out
of. A QSAR class judged to be “in” or “in (conditionally)” is considered to be within theapplicability domain in terms of structure.
in: Within the applicability domain
All the “substructures for structure judgement” of the query chemical are found in the“substructures for structure judgement” extracted from the training set data*2 in the
QSAR class (pink and orange area in Figure 2-3), or the query chemical contains no“substructures for structure judgement”.
in (conditionally): Conditionally within the applicability domain
The query chemical does not meet the condition of “in”, but all the “substructures forstructure judgement” of the query chemical are found in “substructures for structure13

judgement” extracted from the training set data in the QSAR class or those from the
Narcotic Group class *3 (pink, orange and yellow areas in Figure 2-3).
out of: Out of the applicability domain
The query chemical does not meet the conditions of “in” or “in (conditionally)”; that is, inthe query chemical, there is at least one “substructures for structure judgement” that isin neither the “substructures for structure judgement” extracted from the training set
data of the QSAR class nor those from the Narcotic Group class (grey area in Figure 2-3).
*1 Substructures introduced for the structure judgement in KATE, which contain toxicologicallycharacteristic structures, are also used for structural classification (for details see the
KATE2020 technical guidance document (to be published)).
*2 The “substructures for structure judgement” extracted from the training set data also
includes substructures of chemicals with an inequality sign whose log P judgement is “in”
(within the applicability domain).
*3 Baseline toxicity not based on specific physiological activity (narcotic effect). In KATE2020,
QSAR classes whose toxicity is explained only by a simple narcotic effect are defined for eachtype of predicted toxicity. Examples include aliphatic hydrocarbons, sulfoxide, aliphatic
ethers, aromatic ethers, aliphatic ketones, aromatic ketones, and alcohols. These QSAR
classes are grouped and defined as the Narcotic Group for each type of predicted toxicity.

* The green frame encloses substructures extracted when predicting with the leftmost chemical in“Examples of Query Chemical”. The same applies to the purple and red frames.
* “etc.” indicates multiple substructures have the same ID, so only one example is shown.

Figure 2-3. Example of Structure JudgementB) Log P Judgement
KATE2020 judges whether the log P value falls within the applicability domain, based onwhether the log P value of the query chemical is between the minimum and maximum log P
values of all training set data in the QSAR class concerned.
In KATE2020, all chemical substances with log P > 6, which are highly hydrophobic and have
low prediction accuracy, are judged as being out of the applicability domain. This is a newfeature in KATE2020.
14

in:Within the applicability domain (Figure 2-4).
out of: Out of the applicability domain (Figure 2-5).
out of+: Out of the applicability domain, but the log P value of the query chemical takes avalue between the minimum and maximum log P values of all chemicals (both thetraining set data and the support chemicals) in the QSAR class concerned (Figure
2-6).

Figure 2-4. Example of log P judgement (in)Figure 2-5. Example of log P judgement (out of)15

Figure 2-6. Example of log P judgement (out of+)16

3. Log In
Since the release of KATE2020 ver. 4.0 in March 2023, users no longer need user ID andpassword to login. Accessing KATE2020 login screen (https://kate2.nies.go.jp/nies/index.php) in
figure 3, users only have to ①agree with the term of agreement and then ②click “Start thesession” button. Data entered by the user, such as SMILES, and output results (e.g., predicted
toxicity values and QSAR classes) are stored only in the session, not on the KATE2020 server,
and they are automatically deleted when the session expires. The session will expire when web
browser is closed or user stopped using KATE2020 for one hour. Furthermore, the previous
information is overwritten and deleted every time a prediction is made.

Figure 3. Login screen
17

4. Input of Query Chemical
After login, the input screen is displayed (Figure 4-1). KATE2020 performs predictions based on
the SMILES string of a query chemical. In the center of the screen is a field for the input of a
SMILES string. Above that are fields for entering a CAS number or chemical name for conversion
to a SMILES string. SMILES strings can also be generated from a drawing of a chemical structure
created in the JSME Editor tool within KATE2020.

Figure 4-1. Input screen(1) Acceptable and Unacceptable SMILES Strings
SMILES strings for almost all organic compounds, as well as some inorganic nitrogen compounds(e.g., hydrazine), can be entered into KATE2020. However, the string mustcontain C or N
not include elements other than H, C, N, O, F, Si, P, S, Cl, As, Br, Sn, and I

not include ions (although, ammonium [N+] and [n+] can be entered)

not express a mixture (i.e., a SMILE string that includes “.”)be converted to the protonated form if it includes [Na], [K], [Li], [Na+], [K+], or [Li+](e.g., “c1ccccc1O[Na]” should be entered as “c1ccccc1O”).

Unacceptable SMILES strings will prompt the system to return an error message.

(2) Direct Input of a SMILES String
Enter a SMILES string in the SMILES input field (Figure 4-1), click the “Predict” button (Figure4-2), and the QSAR Prediction Results screen will be displayed. In this example, the SMILES
string for pyridine-3-ylmethanamine has been entered.
18

Figure 4-2. “Predict” button next to the SMILES input box(3) Convert a Drawing of a Chemical Structure into a SMILES String
You can draw a chemical structure using the JavaScript Molecule Editor (JSME Editor) within
KATE2020 and convert the drawing to a SMILES string.
Step 1: Click “JSME Editor” under the SMILES string input box (Figure 4-3) to open the JSME
Editor window (Figure 4-4).

Figure 4-3. Link to the “JSME Editor”
Step 2: In the editor window, draw the structure of the query chemical and click the “Submit smiles
to KATE” button. The chemical structure will be converted into the corresponding SMILES string
and the string will be automatically entered in the SMILES input field (Figure 4-5). Click the“Predict” button to display the QSAR Prediction Results screen. Detailed instructions on how to
use the JSME Editor can be found at the developers’ webpage: https://jsme-editor.github.io/(accessed March 1, 2024)

Figure 4-4. JSME Editor

Figure 4-5. Conversion of a structure drawn in JSME Editor into a SMILES string(4) Convert a CAS Number or Chemical Name into a SMILES String
KATE2020 includes tools to convert a CAS number or chemical name into a SMILES string and19vice versa.
Enter the CAS number or chemical name and click the “Get information using Chemical
Identifier Resolver” button (Figure 4-6). The structure associated with the CAS number orchemical name will be converted to a SMILES string, and the string will be automaticallyentered in the SMILES field. In addition, the IUPAC name, SMILES string, and chemical
structure of the query chemical will be presented above the CAS number entry box, and the
IUPAC name will be entered into the Name field (Figure 4-7). Click the “Predict” button todisplay the QSAR Prediction Results screen.

Figure 4-6. CAS input box and “CAS to SMILES, IUPAC Name” button

Figure 4-7. Conversion of a CAS number to a SMILES string(5) User-input Log P Values
If the log P value of the query chemical is known, the value can be entered in the field below the
SMILES string input box (Figure 4-8). The entered log P value will be preferentially used in the
toxicity prediction.

Figure 4-8. Input of a known log P value20(6) User-input CAS Number and IUPAC Name
If a CAS number or IUPAC name is entered in the CAS or Name fields, the information will
displayed on the QSAR Prediction Results screen exactly as it was entered (Figure 4-9).

Figure 4-9. Input of CAS number and IUPAC name for inclusion at the top of the QSAR prediction results(7) Skip KOWWINTM Calculation
If a SMILES string that cannot be used by KOWWINTM for the estimation of log P is entered,an error will be reported during the prediction process. To continue with the prediction, the“Skip KOWWIN Calculation” box should be clicked, and a user-define log P value should beentered (Figure 4-10). If a log P value is not entered, QSAR classes will still be assigned, but nopredicted toxicity values will be calculated. A user-defined log P value can also be entered on
the QSAR Prediction Results screen.

Figure 4-10. Option to skip KOWWINTM calculation

21

5. QSAR Prediction Results
After entering the necessary information about the query chemical in the input page and clicking
the “Predict” button, the QSAR Prediction Results screen is displayed (Figure 5-1). This screen
provides a summary of the query chemical, the QSAR classes to which the query chemical was
assigned for each type of predicted toxicity, the ecotoxicity prediction results for each QSAR class,and the statistics associated with each QSAR class.

Figure 5-1. QSAR Prediction Results screen22(1) Summary of the Query Chemical
The upper part of the QSAR Prediction Results screen shows a summary of the information used
for the prediction (Figure 5-2).
f

b
c
d
e

a

g
h
i
Figure 5-2. Summary of the query chemicala Chemical structure of the query chemical.
b CAS number (displayed only when entered by the user).
Note: The CAS number is verified by means of the check digit only (the final digit in the
CAS number), not the whole CAS number, and the number is tagged with “(incorrect)” if
the check digit is incorrect . This may identify if an incorrect number has been input, but
not whether the SMILES string matches the CAS number.
c Name of chemical substance (displayed only when entered by the user).
d SMILES string of the query chemical.
e Molecular weight of the query chemical calculated by Open Babelf Log P value entered by the user.
g Log P value estimated by KOWWINTM.
h Measured log P value in the KOWWINTM database.
Note: If there is more than one log P value for the query chemical in the KOWWINTM
database, all values in the database will be displayed.
i “Re-calculate” button: Clicking updates the QSAR prediction results with the log P value
entered in the “User Input Value” field.

23

(2) QSAR Prediction Result
The middle section of the QSAR Prediction Results screen provides the QSAR class names of
the query chemical, type of predicted toxicity, predicted toxicity results (in green), 95%prediction interval, log P value used for the query chemical, applicability domain judgement,
and statistics related to the QSAR classes (Figure 5-3). The results are presented as a tablewith a series of checkboxes that can be used to filter the results.
a
c

b

d

e

f

g

h

i

j

k

l

m

n

o

p q

r s

Figure 5-3. QSAR Prediction Results screen
Checkboxes
a “Include” checkboxes indicate the type of predicted toxicity results to show. All the boxes
are checked by default.
b “Exclude” checkboxes: If a QSAR class meets any of the conditions stipulated for R2(coefficient of determination), Q2 (internal validation index), or n (number of training setdata), the results are not displayed. Which QSAR classes are shown can be specified byunchecking the boxes or changing the values. By default, all the boxes are checked, andthe following values are entered: R2 < 0.7, Q2 < 0.5, or n < 5. QSAR classes with either
value less than these limits are not displayed.
※When the leftmost checkbox is checked, all three checkboxes on the right side are checked.
For example, checking the checkbox in the parentheses to the right of “Exclude” will check R2,
Q2, and n checkboxes at the same time.
When no QSAR class is displayed for all types of predicted toxicity, “No applicable results” isdisplayed (Figure 5-4).

Figure 5-4. QSAR Prediction Results (No QSAR Class Shown)c “Update” button: Clicking updates the QSAR results in line with any changes that havebeen made to the checkboxes.

24Batch Printing

d Check boxes for inclusion in the final print format. By default, checkboxes are selected
when the QSAR class meets the criteria R2≥0.7 and Q2≥0.5 and n≥5, the log P judgement
is “in”, and the structure judgement is “in” or “in (conditionally)”.
e Button for displaying the final print format for review.
QSAR class names and links
f QSAR class names. Click the name to go to the Verify QSAR screen.
Type of predicted toxicity
g Organism (fish, daphnid, or alga)h Acute or chronic
The following combinations are available for e and f:
Type of predicted
toxicity
Testing method
Acute/
Organism
chronic
Fish

Acute

Daphnid

Acute

Alga

Acute

Fish

Chronic

Daphnid

Chronic

Alga

Chronic

Test duration

Indicator

96 h

LC50

48 h

EC50

72 h

EC50

Embryonic stage and
30 d after hatching*

NOEC

21 d

NOEC

72 h

NOEC

Fish acute toxicity test

(OECD TG 203)
Daphnia magna immobilization test

(OECD TG 202)
Algal growth inhibition test

(OECD TG 201)
Fish early-life-stage toxicity test

(OECD TG 210)
Daphnia magna reproduction test

(OECD TG 211)
Algal growth inhibition test

(OECD TG 201)

*Although the test duration of the fish early-life-stage test differs by species and the number of days before
hatching, it is set as “embryonic stage and 30 days after hatching” for Oryzias latipes used in the ecotoxicity
tests conducted by the Japanese Ministry of the Environment.

Predicted values
i Predicted toxicity value
j 95% prediction interval of the predicted toxicity valuelog P
k Type of log P used for the query chemical, which is determined in the following priority
order:
1. User Input: log P value entered by the user
2. Estimated: log P value estimated by KOWWINTM

lLog P value of the query chemical

Judgement of applicability domainm Log P judgement result.
n [Minimum value, maximum value] of the descriptor log P of the training set data in the
QSAR class (applicability domain in terms of log P).
o Structure judgement result.

25

Statistics
p R2 (coefficient of determination) of the QSAR equationq Q2 (internal validation index by the leave-one-out method) of the QSAR equation (fordetails, see the KATE2020 technical document※).
r RMSE (root mean square error) of the QSAR equations Number of training set data used for the QSAR equation (support chemicals are notincluded). The value in parenthesis is the number of support chemical data points.
※https://kate2.nies.go.jp/nies/doc/KATE_TechnicalDocument-e.pdf

26

6. Details of QSAR Class Information
In the QSAR prediction results, clicking on a link in the “QSAR Class Name” column will open a
new window showing detailed information of the QSAR class (Figures 6-1 to 6-3). On this screen,a graph of the regression equation for the QSAR class, detailed information on the training set
data and support chemicals, definition of the structure class, and substructures of the query
chemical are shown. The page can be considered to consist of nine sections, each of which is
described below.

(1)

(3)

(2)

(4)
(5)

Figure 6-1. Verify QSAR screen (top)(6)

Figure 6-2. Verify QSAR screen (middle)

27

(7)
(8)

(9)

Figure 6-3. Verify QSAR screen (bottom)(1) Summary of QSAR ClassThe dark blue band at the top of the page provides summary information (Figure 6-4).
abcFigure 6-4. QSAR class basic informationabType of predicted toxicity for the QSAR class
Structure class ID corresponding to the QSAR class (see “(8) structure class definition”)

cName of the QSAR class(2) Graph
The graph of the regression equation consists of four parts (Figure 6-5).
a

b

c
d
Figure 6-5. Graph of the regression equation (toxicity vs. log P)
28

aField to select the horizontal axis
Change from “log P” (default) to “Predicted Variable” and click the “Update” button to
draw a graph of “measured toxicity value vs. predicted toxicity value”, where toxicity value
is presented as log(1/{LC50, EC50, or NOEC} in mmol/L). Click the “Default” button to
return to the default display.

b

Graph and legend
The graph of the regression equation is displayed on the left with the legend on the right.
The following symbols are used in the legend:
×: Query chemical+: Training set
Black line: Regression line
Orange curve: 95% confidence interval of the regression equation
Green curve: 95% prediction interval of log(1/{LC50, EC50, or NOEC})------Support Data-----＊: Plot for a chemical with log P > 6.0▽△: Plot for a chemical with an inequality sign◇: Plot for an outlier------When a training set (shown as “+” in the graph) is deleted by clicking on the symbolin the graph-----□: Deleted data
Red line: Regression line calculated without the deleted data------Bottom-left of the graph-----First line
Regression equation
Second line “R2” Coefficient of determination of the regression equation“N” Number of training set data (support chemical data are not included)c

Information for the query chemical
1st line: Predicted toxicity value (in mg/L]
2nd line: 95% prediction interval of predicted toxicity value (in mg/L]
3rd line: Log P judgment and structure judgment resultsdGraph display buttons
First line
Shift L: Shift view left R: Shift view right
Dn: Shift view down Up: Shift view up
Second line
Zoom −: Zoom out whole graph +: Zoom in whole graph
X−: Zoom out the X axis+: Zoom in the X axis
Y−: Zoom out the Y axis+: Zoom in the Y axis
29Selection of individual chemicals
The “Chemical list” dropdown gives a list of training set data. When a point (+) on the graph(Figure 6-6) is clicked or one of the structures on the list is clicked (Figure 6-7), the chemical
is excluded from the calculation, and an updated regression line is shown in red (Figure 6-6).
The prediction interval (green curves) and confidence interval (orange curves) are also
recalculated without the selected chemical. Multiple chemicals can be removed from thecalculation.
When one or more training set data are removed from the calculation, the number ofchemicals removed is displayed in the upper left of the graph (Figure 6-6) and, in addition tothe original QSAR equation, the modified QSAR equation is displayed after an arrow (Figure6-6). Updated information on R2 and N is also displayed.
When a training set is removed, the + symbol for the chemical is changed to a
(Figure 66), and the structure is highlighted by a violet frame in the training set data list (Figure 6-7).
Click the point again to deselect.

Figure 6-6. Selection of training set (graph)

Figure 6-7. Selection of training set (Training set data dropdown)
30(3) Information About the Query Chemical
The chemical structure and basic information about the query chemical are displayed to theright of the graph legend (Figure 6-8).

Figure 6-8. Query chemical information
SMILES: SMILES string of the query chemical
Chemical Name (User Input): Chemical name entered by the user
CAS RN (User Input): CAS number entered by the user
Molecular Weight: Molecular weight of the query chemicallog P: Log P value of the query chemical. “(User Input)” is appended for a user-definedvalue and “(Estimated)” is appended for a value estimated by KOWWINTM.
(4) Information About the Regression Equation
Information about the regression equation (QSAR equation) is displayed below the graph(Figure 6-9).

Figure 6-9. Information about the regression equation
Equation: Regression equation (QSAR equation)
Number of Chemicals used for Regression: Number of training set data used for the
regression calculation
Number of Support Chemicals: Number of support chemicals identified
Applicable Range of log P: Minimum and maximum log P values for the training set data

R2: Coefficient of determination of the QSAR equation
Q2: Internal validation index by the leave-one-out method of the QSAR equation (fordetails, see the KATE2020 technical guidance document (to be published))
RMSE: Root mean square error of the QSAR equation31

(5) Chemical Clicked Last
The “Chemical Clicked Last” section provides information about the last clicked chemical within
the Training set data or Support Chemicals list. In its default state (i.e., when no chemical has
been clicked), the page reports “Not Clicked” (Figure 6-10).

Figure 6-10. Chemical Clicked Last section: before a chemical has been clicked
When a chemical is clicked, the following information is displayed (Figure 6-11):Chemical structureSMILES string
CAS number

IUPAC name
Coordinates on the regression graphSquare of residual
Molecular weightMeasured toxicity value information ({LC50, EC50, or NOEC}; species; reference)Note (information about toxicity test etc., if applicable)

Figure 6-11. Chemical Clicked Last section: after a chemical has been clicked32

(6) List of Chemical Structures
The “Chemical list” dropdown provides lists containing the structural formulas of the training
set and support chemicals within the QSAR class (Figure 6-12).
a
b
c
d

e
Figure 6-12. “Chemical List” dropdown
Each of these lists can be sorted using the dropdown menus and “Update” button at the top of
the page (Figure 6-12a). The available options are sort by CAS number, X coordinate (log P), Y
coordinate (log (1/{LC50, EC50, or NOEC})), square of residual, and similarity, and each can bearranged in ascending or descending order. By default, the chemicals are arranged in ascendingorder of X coordinate (log P). Here, X coordinate, not X axis, is used in case “Horizontal Axis” (see “a”
in (2)) is selected as “log P”; that way, even if the chemicals are sorted by X coordinate, when thegraph of “Measured toxicity value vs. Predicted toxicity value” is displayed they are still sorted by log
P value.
Clicking the “Training set data” dropdown reveals a list containing the structural formulas of the
training set data (Figure 6-12 b). The values displayed directly below the chemical structures (Figure
6-12 c) are their similarity with the query chemical (Tanimoto coefficient using PubChem
fingerprints; for details, see the KATE2020 guidance document [to be published]); the coefficient falls
between 0 and 1, and a structure with higher similarity to the query chemical has a value closer to
1. The values displayed in parentheses (Figure 6-12 d) are the X coordinate (i.e., log P value) and Y
coordinate (log(1/{LC50, EC50, or NOEC})) of the chemical substance in the default graph.
Clicking the “Support Chemicals” dropdown (Figure 6-12 e) reveals three additional lists showing
the structures of the support chemicals divided into those with data containing an inequality sign,
outlier chemicals, and chemicals with a log P > 6 (Figure 6-13).

Figure 6-13. “Support Chemicals” dropdown
33

(7) Chemical Data
The “Chemical Data” dropdown provides the chemical details of the training set data (Figure 6-14)
and support chemicals in the class. Both lists are formatted as shown below.

a
c

b

d

e

f

g

h

i

j

k

Figure 6-14. “Chemical Data” dropdown
a
b
c
d
e
f
g
h
i

j
k

l

CAS No.: CAS number
Chemical Name: Chemical name used in KATE2020
SMILES: SMILES string used in KATE2020
Structure Formula: Structural formula
Similarity: Similarity between the chemical in the list and the query chemical
Molecular Weight: Molecular weight
Estimated log P: Log P value estimated using KOWWINTM
EC50*: Toxicity value (in mg/L) based on the results of ecotoxicity testslog(1/EC50* [mmol/L]):*For h and i, the LC50, EC50, or NOEC corresponding to the type of predicted toxicity of
the QSAR class is automatically displayed.
Species: Species for which the original toxicity data were determined
Reference: Source of the original toxicity data. The year indicates the year the test was
implemented. “MOE” means the data were obtained from the following webpage:
http://www.env.go.jp/chemi/report/ierac18/2-1-2-1.pdf (results of ecotoxicity tests
conducted by the Japanese MOE [in Japanese]; accessed March 1, 2023). “USEPA” means
the data were fish acute toxicity test results obtained from the US EPA fathead minnow
database as a training set data dataset.
Note: Other information about the chemical substance

The order in which the data are displayed is determined by the sorting filter at the top of thepage (see “a” in (6) ).

34

(8) Structure Class Definition
Clicking the “Definition of Structure Class” dropdown reveals information about the structureclass corresponding to the QSAR class (Figure 6-15). The assigned structure class is highlighted
in yellow, with the structure class ID in the "Structure ID" column, the name of the structureclass or substructure in the "Description" column The "Decision Tree" column contains thedefinition of the structure class or substructure.

Figure 6-15. “Structure Class Definition” information(9) Substructures of the Query Chemical
The “Substructures of the Query Chemical” section contains two dropdowns. Clicking the lower“Substructures used for the Judgement and the Classification” dropdown reveals a list of thesubstructures used in both the structural classification and the structural judgement (Figure 616).

f

a

b

c

d

e

Figure 6-16. “Substructures used for the Judgement and the Classification” dropdownaJudgement: This is a new function in KATE2020 version 2.0. Structure judgment result
for the substructure. This provides detailed information about the structure judgment of
the QSAR class, and when the structure judgment of the QSAR class is “out of” or “in(conditionally)”, you can see which of the substructures contained in the query chemical
are the cause of the judgment result.
Three judgments are possible:
in: The substructure is found in the “substructures for structure judgement” extracted
from the training set data in the QSAR class.
in (conditionally): The substructure does not meet the condition of “in”, but thesubstructure is found in “substructures for structure judgement” extracted from thetraining set data in the Narcotic Group class.
35

out of: The substructure does not meet the conditions of “in” or “in (conditionally)”; thatis, the substructure is found in neither the “substructures for structure judgement”
extracted from the training set data of the QSAR class nor those from the Narcotic
group class.
b

c
d
e
f

FragID: ID of the substructure. This is a four-digit number and was arbitrarily set for
convenience during the development of KATE. At present, the FragIDs used in this table
all start with the number 5 (for details, see the KATE2020 technical guidance document(to be published)).
Substructure Name: Name of each substructure (please note that the names may change
in the future).
Count: The number of substructures in the query chemical corresponding to SMARTS
SMARTS: Definition of the substructure in SMARTS notation
Show/Hide SMARTS Button: Click to hide or display the “SMARTS” column.

Clicking the “+Substructures used only for Structural Classification” dropdown reveals a list of
the substructures used only for the structural classification (Figure 6-17).

g

h

i

j

Figure 6-17. “Substructures of a Query Chemical” dropdown (used only for structural classification)
g

h
i
j

FragID: ID of the substructure. This is a four-digit number and was arbitrarily set for
convenience during the development of KATE. At present, the FragIDs used in the table
all start with a number 3, 4, 6, or 7 (for details, see the KATE2020 technical guidance
document (to be published)).
Substructure Name: Name of each substructure (please note that the names may change
in the future).
Count: The number of substructures in the query chemical.
SMARTS: Definition of the substructure in SMARTS notation.
36

7. Prediction of Multiple Chemical Substances
Files containing multiple SMILES strings can be input to perform sequential prediction for
multiple chemical substances.

(1) Input File: “SMILES List”
KATE utilizes a “SMILES list”, an input file containing a series of SMILES strings, tosequentially predict the ecotoxicity of multiple query chemicals. In KATE2020, thespecifications of the “SMILES list” are different from those in KATE2017.
In the SMILE list file, enter one or more headers in the first line (case-insensitive) and theinformation on each query chemical in the subsequent lines in text form.
As headers, you can enter “SMILES” (required) as well as “ID” (ID defined by user), “LogP” (logP value entered by user), “NAME” (name of chemical substance), and “CAS” (CAS number).
Separate columns with tabs.
Below are two examples of SMILES lists:
Example 1
SMILES
CCCCOC(=O)CS
CC(=C)CSCC1(CC2(C)CC3(Br)C1)CC(Br)(C2)C3
CCCCCCCCCBr
CCCCCCCCC(Br)CBr
CCCCCCCCCCBr*In the example above, the first line specifies the column name “SMILES”, and thesubsequent lines specify “SMILES” strings of query chemicals.
Example 2
NAME
name1
name2
name3

LogP
SMILES
0.8
CCCCOC(=O)
CC(=C)CS
1.3
NCc1cccnc1

CAS3731-52-0

id
A10
A50
F20

*The order of columns is arbitrary.
*Columns should be separated by tabs (denotes a tab character). The number oftabs should be the same in each line.
*Any of the columns can be omitted except the “SMILES” column.
In KATE2020, to see how to format the SMILES list, click “SMILES list” above the “Select”
button (Figure 7-1).

Figure 7-1. Link to details about formatting a SMILES list37(2) Prediction Procedures
Step 1. Click the “Select” button on the Input screen (Figure 7-2).

Figure 7-2. Select Button for “Prediction of Multiple Chemicals”
Step 2. Select the input SMILES list and click the “Open” button (Figure 7-3).

Figure 7-3. Selection of SMILES list
Step 3. Click the “Predict” button to start the prediction (Figure 7-4).

Figure 7-4. “Predict” Button for using a SMILES list
After the calculation, the prediction results are displayed (Figure 7-5).

38

Figure 7-5. Results obtained from using a SMILES list

Click on the QSAR class name in the QSAR Class Name column to go to the Verify QSAR screen.
You can also download the prediction results as a tab-delimited file by clicking on 'Download
results as tsv' in the top right-hand corner of the screen. The contents of the file are shown in
Figure 7-6.
ID CAS RN

Chemical

SMILES

Molecular

QSAR Class Name

Organism

Predicted 95% Prediction

Chronic

Toxicitylog P Range

Structure

R2

Q2

44.1 12120341 C_X hydrocarbon unreactive aliphatic w/o X

Fish

Acute

36 [4.7, 280]

1.81 Estimated out of[2.58, 4.98]

in

0.73

0.68

1

CCC

44.1 12899941 narcotic group Fish Acute

Fish

Acute

66 [9.2, 470]

1.81 Estimated in[-0.63, 5.88] in

0.87

0.87

0.43 154(31)1CCC44.1 22899941 narcotic group Daphnid Acute

DaphnidAcute

11 [1.5, 87]

1.81 Estimated in[1.08, 5.88]

0.71

0.7

0.43 83(22)1CCC44.1 32899941 narcotic group Alga Acute

Alga

Acute

62 [7.7, 510]

1.81 Estimated in[1.08, 5.26]

in

0.76

0.74

0.43 52(46)1

CCC44.1 12100151 C_X hydrocarbon unreactive

Fish

Chronic

1.1 [0.077, 14]

1.81 Estimated in[1.52, 5.52]

in

0.78

0.68

0.43 11(0)1CCC44.1 12500151 Cnos_X unreactive Fish Chronic

Fish

Chronic

0.99 [0.073, 13]

1.81 Estimated in[1.52, 5.52]

in

0.76

0.68

0.43 12(0)1CCC44.1 12899851 narcotic group Fish Chronic

Fish

Chronic

1.1 [0.093, 13]

1.81 Estimated in[1.52, 5.81]

in

0.82

0.75

0.41 12(0)1CCC44.1 22899851 narcotic group Daphnid Chronic

Daphnid

Chronic

1 [0.084, 12]

1.81 Estimated in[-1.20, 5.88] in

0.7

0.68

0.53 74(13)2CCN45.08 12200841 amine primary unreactive NH2=1 aliphatic

Fish

Acute

240 [17, 3500]-0.15 Estimated in[-1.61, 5.25] in

0.84

0.81

0.54 26(2)3CCO

46.07 10600041 CO_X primary alcohol

Fish

Acute

3700 [390, 34000]-0.14 Estimated in[-1.75, 5.26] in

0.92

0.9

0.44 22(15)3CCO

46.07 12102041 CO_X alcohol unreactive w/o EO Fish

Fish

Acute

6200 [690, 54000]-0.14 Estimated in[-0.63, 5.81] in

0.89

0.88

0.45 46(13)3CCO

46.07 12899941 narcotic group Fish Acute

Fish

Acute

3600 [490, 27000]-0.14 Estimated in[-0.63, 5.88] in

0.87

0.87

0.43 154(31)3CCO

46.07 20600041 CO_X primary alcohol

DaphnidAcute1300 [150, 12000]-0.14 Estimated out of+[2.31, 5.26]

in

0.95

0.76

0.17 6(17)3CCO

46.07 22102241 CO_X alcohol unreactive w/o EO Daphnid

DaphnidAcute

750 [25, 22000]-0.14 Estimated out of+[0.78, 5.81]

in

0.78

0.72

0.55 14(13)3CCO

46.07 22899941 narcotic group Daphnid Acute

DaphnidAcute

260 [30, 2200]-0.14 Estimated out of+[1.08, 5.88]

in

0.71

0.7

0.43 83(22)3CCO

46.07 30600041 CO_X primary alcohol

Alga

Acute

28000 [260, 2.9e+6]-0.14 Estimated out of+[2.31, 5.26]

in

0.91

0.79

0.36 6(16)3CCO

46.07 32102041 CO_X alcohol unreactive w/o halogen, acid, EO

Alga

Acute

12000 [380, 360000]-0.14 Estimated out of+[1.08, 5.26]

in

0.95

0.9

0.35 6(14)3CCO

46.07 32899941 narcotic group Alga Acute

Alga

Acute

5100 [480, 55000]-0.14 Estimated out of+[1.08, 5.26]

in

0.76

0.74

0.43 52(46)3CCO

46.07 12899851 narcotic group Fish Chronic

Fish

Chronic

30 [1.4, 630]

-0.14 Estimated out of[1.52, 5.81]

in

0.82

0.75

0.41 12(0)3CCO

46.07 22102251 CO_X alcohol unreactive w/o EO Daphnid

Daphnid

Chronic

24 [1.4, 410]

-0.14 Estimated in[-1.20, 5.81] in

0.82

0.75

0.52 14(8)3CCO

46.07 22899851 narcotic group Daphnid Chronic

Daphnid

Chronic

16 [1.2, 210]

3

CCO

46.07 32102051 CO_X alcohol unreactive w/o halogen, acid, EO

Alga

Chronic

1100 [24, 54000]

Intervallog P Value log P Type

log P

CCC

WeightQSAR ID

Acute or

1

Name

Judgement

in

RMSE

n

0.36 21(4)-0.14 Estimated in[-1.20, 5.88] in

0.7

0.68

0.53 74(13)-0.14 Estimated out of+[0.69, 5.26]

0.87

0.81

0.59 10(9)Figure 7-6.tsv file of prediction results of multiple chemicals

39

Judgementin(3) Additional information• If there is a line in the SMILES list that contains an error, that line will be skipped,
and an error message will be displayed in the corresponding row of the prediction
results.
• The maximum number of chemical substances the system can predict at once is 100.
• The time needed for prediction depends on the structures of the chemical substances
contained in the SMILES list. It usually takes no longer than 30 min to finish thecalculation. If it is taking longer, there may be a problem with the calculation. To
address the issue, try to reduce the number of chemical substances in the SMILES
list or enter a single chemical substance in order to check if the system is operating
normally. If you find an issue with a particular SMILE, please contact the KATE
Contact Desk (KATE@nies.go.jp).

40

8. Printing the Prediction Results
In the prediction results screen, click the button

to format the prediction

results for printing. The resulting screen will contain the prediction results for all the QSAR
classes that were selected on the main results screen (Figure 8).

(1)
(2)

(3)

Figure 8. Print Format screen

(1)
(2)
(3)

Title, Date and time when the prediction results were output (Japan Standard Time)QSAR results (Checks are unchangeable)
Results for individual QSAR classes

41