# Rodent Carcinogenicity Dataset

Authors: Natalja Fjodorova; Marjana Novič
Journal: Dataset Papers in Medicine
 (2013)

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## Abstract

The rodent carcinogenicity dataset was compiled from the Carcinogenic Potency Database (CPDBAS) and was applied for the classification of quantitative structure-activity relationship (QSAR) models for the prediction of carcinogenicity based on the counter-propagation artificial neural network (CP ANN) algorithm. The models were developed within EU-funded project CAESAR for regulatory use. The dataset contains the following information:common information about chemicals (ID, chemical name, and their CASRN), molecular structure information (SDF files and SMILES), and carcinogenic (toxicological) properties information: carcinogenic potency (TD50_Rat_mg; carcinogen/noncarcinogen) and structural alert (SA) for carcinogenicity based on mechanistic data. Molecular structure information  was used to get chemometrics information to calculate molecular descriptors (254 MDL and 784 Dragon descriptors), which were further used in predictive QSAR modeling. The dataset presented in the paper can be used in future research in oncology, ecology, or chemicals' risk assessment.


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## Body

## 1. Introduction

Rodent carcinogenicity datasets were used to build models to predict carcinogenicity within EC-funded project CAESAR (Project no. 022674 (SSPI)) [1]. CAESAR project was aimed to develop quantitative structure-activity relationship (QSAR) models for the REACH (Registration, Evaluation, Authorization, and restriction of CHemicals) legislation for five endpoints: bioconcentration factor, skin sensitization, carcinogenicity, mutagenicity, and developmental toxicity. REACH regulation requires the evaluation of the risks resulting from the use of chemicals produced in industry and testing of their toxicity. Carcinogenicity is among the toxicological endpoints that pose the highest public concern. The standard bioassays in rodents used to assess the carcinogenic potency of chemicals are time consuming and costly and require the sacrifice of large number of animals. Cancer bioassays should be reduced according to REACH regulation [2], while the Seventh Amendment to the EU cosmetics directive will ban the bioassay for cosmetic ingredients from 2013 [3].The aim of CAESAR project was to reduce the use of animals as well as the cost associated with toxicity tests.The models predicting carcinogenicity meet the requirements for QSAR models used for regulatory use. Great attention was paid to the quality of data used to build the models; the models were then validated. They are transparent and reproducible and are checked against the OECD principles.The models at the CAESAR's website have been implemented in java and are freely accessible for public use [1].Models for prediction of carcinogenicity using rodent carcinogenicity database were described [4–8]. State of the art and perspectives of predictive models for carcinogenicity are discussed in the paper by Benfenati et al. [9].


## 2. Methodology

The chemicals involved in the study belong to different chemical classes, so-called noncongeneric substances. The aim was to cover chemical space as much as possible. The list of 805 chemicals (see Dataset Item 1 (Table)) was extracted from rodent carcinogenicity study findings for 1481 chemicals taken from Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network, which was built from the Lois Gold Carcinogenic Database (CPDBAS) (http://www.epa.gov/ncct/dsstox/sdf_cpdbas.html) [10].  We have used version 3b of CPDBAS (CPDBAS_v3b_1481_10Apr2006), while in the present moment, it is updated to version 5d, revised on 20 November 2008, and now contains 1547 chemicals.It should be stressed that in order to obtain data suitable for QSAR modeling the initial dataset (1481 chemicals) has been cleaned of all incorrect structures, ambiguous or mixed structures, polymers, inorganic compounds, metallo-organic compounds, salts, and complexes and compounds without well-defined structure. The obtained data and structures of chemicals were cross-checked by at least two partners using the following online databases: ChemFinder [11], ChemIDplus [12], and PubChem Compound [13]. We selected chemicals with available information about carcinogenic potency in rats. Thus, the final dataset of 805 chemicals, with their ID number, chemical name, CASRN, experimental TD50 values for rat, and corresponding binary carcinogenicity classes (P: positive; NP: not positive), are available in Dataset Item 1 (Table). For each substance, it is indicated whether it belongs to training or test set.Rat data only was suggested to be used because a dataset based on data for single species is more consistent and has less variation than a dataset based on two or more species.Additionally, in our latest study we complimented the dataset with the following alerts collected from Toxtree program: GA, genotoxic alert; nGA, non-genotoxic alert; and NA, no carcinogenic alert. Structural alerts (SAs) for carcinogenicity indicating possible mechanism of carcinogenicity were also collected for each chemical and are presented in Dataset Item 2 (Table), and the list of SAs for carcinogenicity is presented in Table1 (for more detailed explanation of terms listed, see http://www.epa.gov/ncct/dsstox/sdf_cpdbas.html).  The Toxtree expert system with the 33 SAs for carcinogenicity was reported in the Benigni/Bossa rulebase for mutagenicity and carcinogenicity [14]. In a broad sense, the set of chemicals characterized by the same SA could compose a family of compounds with the same mechanism of action (see the recent review written by Benigni and Bossa [15]). From our point of view, SA for carcinogenicity is valuable information in mechanistic interpretation of models [8].Table 1
A list of 33 SAs for carcinogenicity extracted from Toxtree with number of chemicals in carcinogenicity dataset.
Structural Alert (SA)
Number of Chemicals
SA2: alkyl (C < 5) or benzyl ester of sulphonic or phosphonic acid
4
SA3: N-methylol derivatives
2
SA4: monohaloalkene
5
SA5: S or N mustard
7
SA6: propiolactones or propiosultones
0
SA7: epoxides and aziridines
22
SA8: aliphatic halogens
47
SA9: alkyl nitrite
1
SA10: a, b unsaturated carbonyls
0
SA11: simple aldehyde
4
SA12: quinones
22
SA13: hydrazine
32
SA14: aliphatic azo and azoxy
7
SA15: isocyanate and isothiocyanate groups
4
SA16: alkyl carbamate and thiocarbamate
5
SA17: thiocarbonyl
18
SA18: polycyclic aromatic hydrocarbons
12
SA19: heterocyclic polycyclic aromatic hydrocarbons
5
SA20: (poly)halogenated cycloalkanes
9
SA21: alkyl and aryl N-nitroso groups
107
SA22: azide and triazene groups
3
SA23: aliphatic N-nitro group
1
SA24: a, b unsaturated aliphatic alkoxy group
1
SA25: aromatic nitroso group
4
SA26: aromatic ring N-oxide
1
SA27: nitroaromatic
75
SA28: primary aromatic amine, hydroxyl amine, and its derived esters
52
SA28bis: aromatic mono- and dialkylamine
7
SA28ter: aromatic N-acyl amine
17
SA29: aromatic diazo
4
SA30: coumarins and furocoumarins
8
SA31a: halogenated benzene
16
SA31b: halogenated PAH
7
SA31c: halogenated dibenzodioxins
2To prepare data for modeling, the dataset of 805 chemicals was subdivided into training (644 chemicals) and test (161 chemicals) sets using the subsorting of chemicals according to a hierarchical system of compound classes in relation to functional group (within classes, the compounds were sorted according to halogen substitution, aromaticity, bond orders, ring contents, and number of atoms), and the following procedure aimed to distinguish between connectivity aspects. This sorting of compounds was implemented with the software system ChemProp [16, 17].External validation of models was performed using external validation set of 738 chemicals different from those in our dataset of 805 compounds described earlier [4]. ChemFinder Ultra 10.0 software was used [18].Nowadays, thousands of chemical descriptors such as constitutional, quantum chemical, topological, geometrical, charge related, semiempirical, thermodynamic, and others can be calculated for a given chemical structure [19, 20]. In the present study, the following sets of descriptors for 805 compounds were generated for modeling: 254 MDL descriptors computed using MDL QSAR version 2.2. [21] and 835 Dragon descriptors calculated by DRAGON professional 5.4 software [22].The obtained descriptors include physicochemical, electrotopological E-state, connectivity, and other descriptors. It should be noticed that E-state indices are a combination of electronic, topological, and valence state information [23–25].To develop robust and reliable models, the descriptors’ space should be reduced by extracting the most significant variables correlated with carcinogenicity. TheHybrid Selection Algorithm (HSA)method was used to select among the different molecular descriptors series the best parameters to classify chemicals by their carcinogenic potency. It combines the Genetic Algorithm (GA) concepts and a stepwise regression [26]. In this way, the descriptors’ space was reduced from 254 to 8 MDL descriptors [4]. Thus, we used topological descriptors, including atom-type and group-type, E-State and hydrogen E-state indices, molecular connectivity, Chi indices, and topological polarity to obtain the molecular structure information, which is correlated with carcinogenic potency. Among the 8 MDL descriptors, there are two connectivity indices (dxp9 and nxch6), three constitutional parameters (SdssC_acnt, SdsN_acnt, and SHBint2_acnt), and three electrotopological parameters (SdsCH, Gmin, and SHCsats).Selection of Dragon descriptors was performed using cross-correlation matrix, multicollinearity, and fisher ratio techniques [27, 28].Among statistical approaches such as linear multivariate regressions, GMDH, and fuzzy logic, artificial neural networks (ANNs), particularly the CP ANN, appeared to be one of the most suitable approaches to predict the complex endpoint such as carcinogenicity for noncongeneric datasets of chemicals with the most reproducible results. The main advantage of neural network modeling is that the complex, nonlinear relationships can be modeled without any assumptions about the form of the model. Large datasets can be examined. Neural networks are able to cope with noisy data and are fault tolerant. However, the interpretation of the acquired knowledge is often a challenge [29].The detailed description of CPANN can be found in the literature [30–33]. The models used to predict carcinogenicity using 8 MDL descriptors as well as 12 Dragon descriptors and their characterization have been published [4].


## 3. Dataset Description

The dataset associated with this Dataset Paper consists of 7 items, which are described as follows.1
A list of 805 chemicals from CPDBAS used for carcinogenicity modeling with indication of training and test sets, which were extracted from the original dataset of 1481 chemicals downloaded from Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network (http://www.epa.gov/ncct/dsstox/sdf_cpdbas.html).  The column ID_v5 presents the codes of the chemicals used in CAESAR project (ID of chemicals in database version 5); ID_CPDBAS-Original, the ID number taken from Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network version 3b; Chemical Name,the chemical names taken from DSSTox and double checked from PubChem Compound (NCBI) (http://www.ncbi.nlm.nih.gov/sites/entrez?db=pccompound); CASRN, the registry number of the Chemical Abstract Service taken from DSSTox and double checked from PubChem Compound (NCBI). In the column Carcinogenic Potency Expressed as TD50, TD50 is the dose rate in milligram per kilogram of body weight per day, which, if administered chronically for the standard lifespan of the species, will halve the probability of remaining tumorless throughout that period. The TD50 value reported is the harmonic mean of the most potent TD50 values from each positive experiment in the species. All the values were derived from the Carcinogenic Potency Database (http://potency.berkeley.edu/cpdb.html). In the column Carcinogenic Potency Expressed as P or NP, “P” means positive or active (carcinogens) and “NP” means not positive or inactive (noncarcinogens). In the column Set, “Training” is for the training set and “Test” is for the test (prediction) set.
806
7
0
ID_v5
ID_CPDBAS_Original
Chemical Name
CASRN
Carcinogenic Potency Expressed as TD50
Carcinogenic Potency Expressed as P or NP
Set2
A list of the same 805 chemicals from CPDBAS with additional chemical information for studied compounds and detailed information about carcinogenic potency by results on animal tests (rats and mice). Structural alerts (SAs) for carcinogenicity extracted from Toxtree are included in last columns. The structural diversity of CAESAR dataset of 805 chemicals by the presence of specific structural alerts (SAs) extracted from Toxtree program with the number of chemicals in carcinogenicity dataset is presented in Table1. In Dataset Item 2 (Table), the column ID_v5 presents the ID number used in the model; ID_CPDBAS-Original, the ID number in CPDBAS; STRUCTURE_Formula, the empirical molecular formula; STRUCTURE_MolecularWeight, the molecular weight or molar mass (atomic mass units); TestSubstance_ChemicalName, the common or trade name of chemical; and TestSubstance_CASRN, the Chemical Abstracts Service (CAS) Registry Number of the tested substance. In the column STRUCTURE_ChemicalName_IUPAC, IUPAC (International Union of Pure and Applied Chemistry) refers to the standardized nomenclature of organic chemistry. The column STRUCTURE_SMILES presents the Simplified Molecular Input Line Entry System (SMILES) molecular text code of displayed STRUCTURE. In the columns TD50_Rat_mg, TD50_Rat_mmol, TD50_Mouse_mg, and TD50_Mouse_mmol, TD50 is a standardized quantitative measure of carcinogenic potency (analogous to an LD50) and is computed in the CPDB for each species/sex/tissue/tumor type for each experiment (see http://potency.berkeley.edu/td50harmonicmean.html). In the columns TargetSites_Rat_Male, TargetSites_Rat_Female, TargetSites_Rat_BothSexes, TargetSites_Mouse_Male, TargetSites_Mouse_Female, and TargetSites_Mouse_BothSexes, target sites (e.g., liver, lung, etc.) are reported for each sex-species group with a positive result  in  the  CPDB  (see  http://potency.berkeley.edu/pathology.table.html).  The column NTP_TechnicalReport presents the National Toxicology Program Technical Report number of study; Website URL, the Internet URL website address for chemical-specific data or content; Alert Type, the structural alert (SA) for carcinogenicity, where GA stands for genotoxic alert, nGA stands for non-genotoxic alert, and NA stands for no alert; Alert 1, the structural alert (SA1) for carcinogenicity, the first SA in molecule; Alert 2,the structural alert (SA2) for carcinogenicity, the second SA in molecule.
806
23
4581
ID_v5
ID_CPDBAS_Original
STRUCTURE_Formula
STRUCTURE_MolecularWeight
TestSubstance_ChemicalName
TestSubstance_CASRN
STRUCTURE_ChemicalName_IUPAC
STRUCTURE_SMILES
TD50_Rat_mg
TD50_Rat_mmol
TargetSites_Rat_Male
TargetSites_Rat_Female
TargetSites_Rat_BothSexes
TD50_Mouse_mg
TD50_Mouse_mmol
TargetSites_Mouse_Male
TargetSites_Mouse_Female
TargetSites_Mouse_BothSexes
NTP_TechnicalReport
Website URL
Alert Type
Alert 1
Alert 23
Collection of SDF files for 805 chemicals listed in carcinogenicity dataset. To create QSAR models, we calculated chemical descriptors using chemical structures.4
Values of 254 MDL descriptors for 805 chemicals.
255
256
0
ID_v5
ID_CPDBAS_Original
(1) SsCH3
(2) SdCH2
(3) SssCH2
(4) StCH
(5) SdsCH
(6) SaaCH
(7) SsssCH
(8) SddC
(9) StsC
(10) SdssC
(11) SaasC
(12) SaaaC
(13) SssssC
(14) SsNH2
(15) SssNH2p
(16) SdNH
(17) SssNH
(18) SaaNH
(19) StN
(20) SsssNHp
(21) SdsN
(22) SaaN
(23) SsssN
(24) SdaaN
(25) SssssNp
(26) SsOH
(27) SdO
(28) SssO
(29) SaaO
(30) SsF
(31) SsssP
(32) SdsssP
(33) SsSH
(34) SdS
(35) SssS
(36) SaaS
(37) SdssS
(38) SddssS
(39) SsCl
(40) SsBr
(41) SsI
(42) SsCH3_acnt
(43) SdCH2_acnt
(44) SssCH2_acnt
(45) StCH_acnt
(46) SdsCH_acnt
(47) SaaCH_acnt
(48) SsssCH_acnt
(49) SddC_acnt
(50) StsC_acnt
(51) SdssC_acnt
(52) SaasC_acnt
(53) SaaaC_acnt
(54) SssssC_acnt
(55) SsNH2_acnt
(56) SssNH2p_acnt
(57) SdNH_acnt
(58) SssNH_acnt
(59) SaaNH_acnt
(60) StN_acnt
(61) SsssNHp_acnt
(62) SdsN_acnt
(63) SaaN_acnt
(64) SsssN_acnt
(65) SdaaN_acnt
(66) SssssNp_acnt
(67) SsOH_acnt
(68) SdO_acnt
(69) SssO_acnt
(70) SaaO_acnt
(71) SsF_acnt
(72) SsssP_acnt
(73) SdsssP_acnt
(74) SsSH_acnt
(75) SdS_acnt
(76) SssS_acnt
(77) SaaS_acnt
(78) SdssS_acnt
(79) SddssS_acnt
(80) SsCl_acnt
(81) SsBr_acnt
(82) SsI_acnt
(83) x0
(84) x1
(85) x2
(86) xp3
(87) xp4
(88) xp5
(89) xp6
(90) xp7
(91) xp8
(92) xp9
(93) xp10
(94) xc3
(95) xc4
(96) xpc4
(97) xch3
(98) xch4
(99) xch5
(100) xch6
(101) xch7
(102) xch8
(103) xch9
(104) xch10
(105) dx0
(106) dx1
(107) dx2
(108) dxp3
(109) dxp4
(110) dxp5
(111) dxp6
(112) dxp7
(113) dxp8
(114) dxp9
(115) dxp10
(116) nxp3
(117) nxp4
(118) nxp5
(119) nxp6
(120) nxp7
(121) nxp8
(122) nxp9
(123) nxp10
(124) nxc3
(125) nxc4
(126) nxpc4
(127) nxch3
(128) nxch4
(129) nxch5
(130) nxch6
(131) nxch7
(132) nxch8
(133) nxch9
(134) nxch10
(135) xv0
(136) xv1
(137) xv2
(138) xvp3
(139) xvp4
(140) xvp5
(141) xvp6
(142) xvp7
(143) xvp8
(144) xvp9
(145) xvp10
(146) xvc3
(147) xvc4
(148) xvpc4
(149) xvch3
(150) xvch4
(151) xvch5
(152) xvch6
(153) xvch7
(154) xvch8
(155) xvch9
(156) xvch10
(157) dxv0
(158) dxv1
(159) dxv2
(160) dxvp3
(161) dxvp4
(162) dxvp5
(163) dxvp6
(164) dxvp7
(165) dxvp8
(166) dxvp9
(167) dxvp10
(168) SHsOH_Acnt
(169) SHdNH_Acnt
(170) SHsSH_Acnt
(171) SHsNH2_Acnt
(172) SHssNH_Acnt
(173) SHtCH_Acnt
(174) SHother_Acnt
(175) SHCHnX_Acnt
(176) SHsOH
(177) SHdNH
(178) SHsSH
(179) SHsNH2
(180) SHssNH
(181) SHtCH
(182) SHother
(183) SHCHnX
(184) Hmax
(185) Gmax
(186) Hmin
(187) Gmin
(188) Hmaxpos
(189) SHCsatu
(190) SHCsats
(191) SHtvin
(192) SHvin
(193) SHarom
(194) si
(195) IC
(196) R
(197) idw
(198) idwbar
(199) idc
(200) idcbar
(201) SHBint2
(202) SHBint3
(203) SHBint4
(204) SHBint5
(205) SHBint6
(206) SHBint7
(207) SHBint8
(208) SHBint9
(209) SHBint10
(210) SHBint2_Acnt
(211) SHBint3_Acnt
(212) SHBint4_Acnt
(213) SHBint5_Acnt
(214) SHBint6_Acnt
(215) SHBint7_Acnt
(216) SHBint8_Acnt
(217) SHBint9_Acnt
(218) SHBint10_Acnt
(219) k0
(220) k1
(221) k2
(222) k3
(223) ka1
(224) ka2
(225) ka3
(226) LogP
(227) fw
(228) nvx
(229) nelem
(230) nrings
(231) ncirc
(232) phia
(233) knotp
(234) knotpv
(235) numHBa
(236) numHBd
(237) numwHBd
(238) SHHBd
(239) SHwHBd
(240) SHHBa
(241) Qs
(242) Qv
(243) Qsv
(244) W
(245) Wp
(246) Pf
(247) sumdelI
(248) sumI
(249) TTs(4) Simple
(250) TTd(4) Valence
(251) tets2
(252) totop
(253) Wt
(254) nclass5
A list of 254 MDL descriptors with their signs and definitions.
255
5
0
ID
MDL Number
Descriptors’ Sign
Definition
Class6
Values of 784 Dragon descriptors for 805 chemicals.
785
785
224
ID_v5
DRA0001 MW
DRA0002 AMW
DRA0003 Sv
DRA0004 Se
DRA0005 Sp
DRA0006 Ss
DRA0007 Mv
DRA0008 Me
DRA0009 Mp
DRA0010 Ms
DRA0011 nAT
DRA0012 nSK
DRA0013 nBT
DRA0014 nBO
DRA0015 nBM
DRA0016 SCBO
DRA0017 ARR
DRA0018 nCIC
DRA0019 nCIR
DRA0020 RBN
DRA0021 RBF
DRA0022 nDB
DRA0023 nTB
DRA0024 nAB
DRA0025 nH
DRA0026 nC
DRA0027 nN
DRA0028 nO
DRA0029 nP
DRA0030 nS
DRA0031 nF
DRA0032 nCL
DRA0033 nBR
DRA0034 nI
DRA0035 nX
DRA0036 nR03
DRA0037 nR04
DRA0038 nR05
DRA0039 nR06
DRA0040 nR07
DRA0041 nR08
DRA0042 nR09
DRA0043 nR10
DRA0044 nR11
DRA0045 nR12
DRA0046 nBnz
DRA0047 ZM1
DRA0048 ZM1V
DRA0049 ZM2
DRA0050 ZM2V
DRA0051 Qindex
DRA0052 SNar
DRA0053 HNar
DRA0054 GNar
DRA0055 Xt
DRA0056 Dz
DRA0057 Ram
DRA0058 Pol
DRA0059 LPRS
DRA0060 VDA
DRA0061 MSD
DRA0062 SMTI
DRA0063 SMTIV
DRA0064 GMTI
DRA0065 GMTIV
DRA0066 Xu
DRA0067 SPI
DRA0068 W
DRA0069 WA
DRA0070 Har
DRA0071 Har2
DRA0072 QW
DRA0073 TI1
DRA0074 TI2
DRA0075 STN
DRA0076 HyDp
DRA0077 RHyDp
DRA0078 w
DRA0079 ww
DRA0080 Rww
DRA0081 D/D
DRA0082 Wap
DRA0083 WhetZ
DRA0084 Whetm
DRA0085 Whetv
DRA0086 Whete
DRA0087 Whetp
DRA0088 J
DRA0089 JhetZ
DRA0090 Jhetm
DRA0091 Jhetv
DRA0092 Jhete
DRA0093 Jhetp
DRA0094 MAXDN
DRA0095 MAXDP
DRA0096 DELS
DRA0097 TIE
DRA0098 S0K
DRA0099 S1K
DRA0100 S2K
DRA0101 S3K
DRA0102 PHI
DRA0103 BLI
DRA0104 PW2
DRA0105 PW3
DRA0106 PW4
DRA0107 PW5
DRA0108 PJI2
DRA0109 CSI
DRA0110 ECC
DRA0111 AECC
DRA0112 DECC
DRA0113 MDDD
DRA0114 UNIP
DRA0115 CENT
DRA0116 VAR
DRA0117 BAC
DRA0118 Lop
DRA0119 ICR
DRA0120 D/Dr03
DRA0121 D/Dr04
DRA0122 D/Dr05
DRA0123 D/Dr06
DRA0124 D/Dr07
DRA0125 D/Dr08
DRA0126 D/Dr09
DRA0127 D/Dr10
DRA0128 D/Dr11
DRA0129 D/Dr12
DRA0130 T(N..N)
DRA0131 T(N..O)
DRA0132 T(N..S)
DRA0133 T(N..P)
DRA0134 T(N..F)
DRA0135 T(N..Cl)
DRA0136 T(N..Br)
DRA0138 T(O..O)
DRA0139 T(O..S)
DRA0140 T(O..P)
DRA0141 T(O..F)
DRA0142 T(O..Cl)
DRA0143 T(O..Br)
DRA0145 T(S..S)
DRA0146 T(S..P)
DRA0148 T(S..Cl)
DRA0150 T(P..Cl)
DRA0152 T(F..F)
DRA0153 T(F..Cl)
DRA0154 T(Cl..Cl)
DRA0156 T(Br..Br)
DRA0157 MWC01
DRA0158 MWC02
DRA0159 MWC03
DRA0160 MWC04
DRA0161 MWC05
DRA0162 MWC06
DRA0163 MWC07
DRA0164 MWC08
DRA0165 MWC09
DRA0166 MWC10
DRA0167 TWC
DRA0169 SRW02
DRA0170 SRW03
DRA0171 SRW04
DRA0172 SRW05
DRA0173 SRW06
DRA0174 SRW07
DRA0175 SRW08
DRA0176 SRW09
DRA0177 SRW10
DRA0178 MPC01
DRA0179 MPC02
DRA0180 MPC03
DRA0181 MPC04
DRA0182 MPC05
DRA0183 MPC06
DRA0184 MPC07
DRA0185 MPC08
DRA0186 MPC09
DRA0187 MPC10
DRA0188 piPC01
DRA0189 piPC02
DRA0190 piPC03
DRA0191 piPC04
DRA0192 piPC05
DRA0193 piPC06
DRA0194 piPC07
DRA0195 piPC08
DRA0196 piPC09
DRA0197 piPC10
DRA0198 TPC
DRA0199 piID
DRA0200 PCR
DRA0201 PCD
DRA0202 CID
DRA0203 BID
DRA0204 X0
DRA0205 X1
DRA0206 X2
DRA0207 X3
DRA0208 X4
DRA0209 X5
DRA0210 X0A
DRA0211 X1A
DRA0212 X2A
DRA0213 X3A
DRA0214 X4A
DRA0215 X5A
DRA0216 X0v
DRA0217 X1v
DRA0218 X2v
DRA0219 X3v
DRA0220 X4v
DRA0221 X5v
DRA0222 X0Av
DRA0223 X1Av
DRA0224 X2Av
DRA0225 X3Av
DRA0226 X4Av
DRA0227 X5Av
DRA0228 X0sol
DRA0229 X1sol
DRA0230 X2sol
DRA0231 X3sol
DRA0232 X4sol
DRA0233 X5sol
DRA0234 XMOD
DRA0235 RDCHI
DRA0236 RDSQ
DRA0237 ISIZ
DRA0238 IAC
DRA0239 AAC
DRA0240 IDE
DRA0241 IDM
DRA0242 IDDE
DRA0243 IDDM
DRA0244 IDET
DRA0245 IDMT
DRA0246 IVDE
DRA0247 IVDM
DRA0248 HVcpx
DRA0249 HDcpx
DRA0250 Uindex
DRA0251 Vindex
DRA0252 Xindex
DRA0253 Yindex
DRA0256 SIC0
DRA0257 CIC0
DRA0258 BIC0
DRA0259 IC1
DRA0260 TIC1
DRA0261 SIC1
DRA0262 CIC1
DRA0263 BIC1
DRA0264 IC2
DRA0265 TIC2
DRA0266 SIC2
DRA0267 CIC2
DRA0268 BIC2
DRA0284 ATS1m
DRA0285 ATS2m
DRA0286 ATS3m
DRA0287 ATS4m
DRA0288 ATS5m
DRA0289 ATS6m
DRA0290 ATS7m
DRA0291 ATS8m
DRA0292 ATS1v
DRA0293 ATS2v
DRA0294 ATS3v
DRA0295 ATS4v
DRA0296 ATS5v
DRA0297 ATS6v
DRA0298 ATS7v
DRA0299 ATS8v
DRA0300 ATS1e
DRA0301 ATS2e
DRA0302 ATS3e
DRA0303 ATS4e
DRA0304 ATS5e
DRA0305 ATS6e
DRA0306 ATS7e
DRA0307 ATS8e
DRA0308 ATS1p
DRA0309 ATS2p
DRA0310 ATS3p
DRA0311 ATS4p
DRA0312 ATS5p
DRA0313 ATS6p
DRA0314 ATS7p
DRA0315 ATS8p
DRA0316 MATS1m
DRA0317 MATS2m
DRA0318 MATS3m
DRA0319 MATS4m
DRA0320 MATS5m
DRA0321 MATS6m
DRA0322 MATS7m
DRA0323 MATS8m
DRA0324 MATS1v
DRA0325 MATS2v
DRA0326 MATS3v
DRA0327 MATS4v
DRA0328 MATS5v
DRA0329 MATS6v
DRA0330 MATS7v
DRA0331 MATS8v
DRA0332 MATS1e
DRA0333 MATS2e
DRA0334 MATS3e
DRA0335 MATS4e
DRA0336 MATS5e
DRA0337 MATS6e
DRA0338 MATS7e
DRA0339 MATS8e
DRA0340 MATS1p
DRA0341 MATS2p
DRA0342 MATS3p
DRA0343 MATS4p
DRA0344 MATS5p
DRA0345 MATS6p
DRA0346 MATS7p
DRA0347 MATS8p
DRA0348 GATS1m
DRA0349 GATS2m
DRA0350 GATS3m
DRA0351 GATS4m
DRA0352 GATS5m
DRA0353 GATS6m
DRA0354 GATS7m
DRA0355 GATS8m
DRA0356 GATS1v
DRA0357 GATS2v
DRA0358 GATS3v
DRA0359 GATS4v
DRA0360 GATS5v
DRA0361 GATS6v
DRA0362 GATS7v
DRA0363 GATS8v
DRA0364 GATS1e
DRA0365 GATS2e
DRA0366 GATS3e
DRA0367 GATS4e
DRA0368 GATS5e
DRA0369 GATS6e
DRA0370 GATS7e
DRA0371 GATS8e
DRA0372 GATS1p
DRA0373 GATS2p
DRA0374 GATS3p
DRA0375 GATS4p
DRA0376 GATS5p
DRA0377 GATS6p
DRA0378 GATS7p
DRA0379 GATS8p
DRA0380 EPS0
DRA0381 EPS1
DRA0382 EEig01x
DRA0383 EEig02x
DRA0384 EEig03x
DRA0385 EEig04x
DRA0386 EEig05x
DRA0387 EEig06x
DRA0388 EEig07x
DRA0389 EEig08x
DRA0390 EEig09x
DRA0391 EEig10x
DRA0392 EEig11x
DRA0393 EEig12x
DRA0394 EEig13x
DRA0395 EEig14x
DRA0396 EEig15x
DRA0397 EEig01d
DRA0398 EEig02d
DRA0399 EEig03d
DRA0400 EEig04d
DRA0401 EEig05d
DRA0402 EEig06d
DRA0403 EEig07d
DRA0404 EEig08d
DRA0405 EEig09d
DRA0406 EEig10d
DRA0407 EEig11d
DRA0408 EEig12d
DRA0409 EEig13d
DRA0410 EEig14d
DRA0411 EEig15d
DRA0412 EEig01r
DRA0413 EEig02r
DRA0414 EEig03r
DRA0415 EEig04r
DRA0416 EEig05r
DRA0417 EEig06r
DRA0418 EEig07r
DRA0419 EEig08r
DRA0420 EEig09r
DRA0421 EEig10r
DRA0422 EEig11r
DRA0423 EEig12r
DRA0424 EEig13r
DRA0425 EEig14r
DRA0426 EEig15r
DRA0427 ESpm02u
DRA0428 ESpm03u
DRA0429 ESpm04u
DRA0430 ESpm05u
DRA0431 ESpm06u
DRA0432 ESpm07u
DRA0433 ESpm08u
DRA0434 ESpm09u
DRA0435 ESpm10u
DRA0436 ESpm11u
DRA0437 ESpm12u
DRA0438 ESpm13u
DRA0439 ESpm14u
DRA0440 ESpm15u
DRA0441 ESpm01x
DRA0442 ESpm02x
DRA0443 ESpm03x
DRA0444 ESpm04x
DRA0445 ESpm05x
DRA0446 ESpm06x
DRA0447 ESpm07x
DRA0448 ESpm08x
DRA0449 ESpm09x
DRA0450 ESpm10x
DRA0451 ESpm11x
DRA0452 ESpm12x
DRA0453 ESpm13x
DRA0454 ESpm14x
DRA0455 ESpm15x
DRA0456 ESpm01d
DRA0457 ESpm02d
DRA0458 ESpm03d
DRA0459 ESpm04d
DRA0460 ESpm05d
DRA0461 ESpm06d
DRA0462 ESpm07d
DRA0463 ESpm08d
DRA0464 ESpm09d
DRA0465 ESpm10d
DRA0466 ESpm11d
DRA0467 ESpm12d
DRA0468 ESpm13d
DRA0469 ESpm14d
DRA0470 ESpm15d
DRA0471 ESpm01r
DRA0472 ESpm02r
DRA0473 ESpm03r
DRA0474 ESpm04r
DRA0475 ESpm05r
DRA0476 ESpm06r
DRA0477 ESpm07r
DRA0478 ESpm08r
DRA0479 ESpm09r
DRA0480 ESpm10r
DRA0481 ESpm11r
DRA0482 ESpm12r
DRA0483 ESpm13r
DRA0484 ESpm14r
DRA0485 ESpm15r
DRA0486 BEHm1
DRA0487 BEHm2
DRA0488 BEHm3
DRA0489 BEHm4
DRA0490 BEHm5
DRA0491 BEHm6
DRA0492 BEHm7
DRA0493 BEHm8
DRA0494 BELm1
DRA0495 BELm2
DRA0496 BELm3
DRA0497 BELm4
DRA0498 BELm5
DRA0499 BELm6
DRA0500 BELm7
DRA0501 BELm8
DRA0502 BEHv1
DRA0503 BEHv2
DRA0504 BEHv3
DRA0505 BEHv4
DRA0506 BEHv5
DRA0507 BEHv6
DRA0508 BEHv7
DRA0509 BEHv8
DRA0510 BELv1
DRA0511 BELv2
DRA0512 BELv3
DRA0513 BELv4
DRA0514 BELv5
DRA0515 BELv6
DRA0516 BELv7
DRA0517 BELv8
DRA0518 BEHe1
DRA0519 BEHe2
DRA0520 BEHe3
DRA0521 BEHe4
DRA0522 BEHe5
DRA0523 BEHe6
DRA0524 BEHe7
DRA0525 BEHe8
DRA0526 BELe1
DRA0527 BELe2
DRA0528 BELe3
DRA0529 BELe4
DRA0530 BELe5
DRA0531 BELe6
DRA0532 BELe7
DRA0533 BELe8
DRA0534 BEHp1
DRA0535 BEHp2
DRA0536 BEHp3
DRA0537 BEHp4
DRA0538 BEHp5
DRA0539 BEHp6
DRA0540 BEHp7
DRA0541 BEHp8
DRA0542 BELp1
DRA0543 BELp2
DRA0544 BELp3
DRA0545 BELp4
DRA0546 BELp5
DRA0547 BELp6
DRA0548 BELp7
DRA0549 BELp8
DRA0550 GGI1
DRA0551 GGI2
DRA0552 GGI3
DRA0553 GGI4
DRA0554 GGI5
DRA0555 GGI6
DRA0556 GGI7
DRA0557 GGI8
DRA0558 GGI9
DRA0559 GGI10
DRA0560 JGI1
DRA0561 JGI2
DRA0562 JGI3
DRA0563 JGI4
DRA0564 JGI5
DRA0565 JGI6
DRA0566 JGI7
DRA0567 JGI8
DRA0568 JGI9
DRA0569 JGI10
DRA0570 JGT
DRA0571 LP1
DRA0572 Eig1Z
DRA0574 Eig1v
DRA0575 Eig1e
DRA0576 Eig1p
DRA0577 SEigZ
DRA0578 SEigm
DRA0579 SEigv
DRA0580 SEige
DRA0581 SEigp
DRA0582 AEigZ
DRA0583 AEigm
DRA0584 AEigv
DRA0585 AEige
DRA0586 AEigp
DRA0587 VEA1
DRA0588 VEA2
DRA0589 VRA1
DRA0590 VRA2
DRA0591 VED1
DRA0592 VED2
DRA0593 VRD1
DRA0594 VRD2
DRA0595 VEZ1
DRA0596 VEZ2
DRA0597 VRZ1
DRA0598 VRZ2
DRA0601 VRm1
DRA0602 VRm2
DRA0603 VEv1
DRA0604 VEv2
DRA0605 VRv1
DRA0607 VEe1
DRA0608 VEe2
DRA0609 VRe1
DRA0610 VRe2
DRA0611 VEp1
DRA0612 VEp2
DRA0613 VRp1
DRA0615 nCp
DRA0616 nCs
DRA0617 nCt
DRA0618 nCq
DRA0619 nCrs
DRA0620 nCrt
DRA0621 nCrq
DRA0622 nCar
DRA0623 nCbH
DRA0624 nCb-
DRA0625 nCconj
DRA0626 nR=Cp
DRA0627 nR=Cs
DRA0628 nR=Ct
DRA0629 nR#CH/X
DRA0630 nR#C-
DRA0633 nRNCS
DRA0634 nRCOOH
DRA0635 nArCOOH
DRA0636 nRCOOR
DRA0637 nArCOOR
DRA0638 nRCONH2
DRA0639 nArCONH2
DRA0640 nRCONHR
DRA0641 nArCONHR
DRA0642 nRCONR2
DRA0644 nROCON
DRA0645 nArOCON
DRA0646 nRCHO
DRA0647 nArCHO
DRA0648 nRCO
DRA0649 nArCO
DRA0650 nCONN
DRA0652 nN=C-N
DRA0653 nC(=N)N2
DRA0654 nRC=N
DRA0655 nArC=N
DRA0656 nRCNO
DRA0657 nRNH2
DRA0658 nArNH2
DRA0659 nRNHR
DRA0660 nArNHR
DRA0661 nRNR2
DRA0662 nArNR2
DRA0663 nN-N
DRA0664 nN=N
DRA0665 nRCN
DRA0667 nN+
DRA0668 nNq
DRA0669 nArNHO
DRA0670 nRNNOx
DRA0671 nArNNOx
DRA0672 nRNO
DRA0673 nArNO
DRA0674 nRNO2
DRA0675 nArNO2
DRA0676 nN(CO)2
DRA0677 nC=N-N
DRA0678 nROH
DRA0679 nArOH
DRA0680 nOHp
DRA0681 nOHs
DRA0682 nOHt
DRA0683 nROR
DRA0684 nArOR
DRA0685 nO(C=O)2
DRA0687 nRSR
DRA0688 nRSSR
DRA0689 nSO
DRA0690 nS(=O)2
DRA0693 nSO3
DRA0694 nSO2N
DRA0695 nPO4
DRA0697 nCH2RX
DRA0698 nCHR2X
DRA0700 nR=CHX
DRA0701 nR=CRX
DRA0703 nCHRX2
DRA0704 nR=CX2
DRA0705 nCRX3
DRA0706 nArX
DRA0707 nCXr
DRA0708 nCXr=
DRA0709 nCconjX
DRA0710 nAziridines
DRA0711 nOxiranes
DRA0713 nPyrrolidines
DRA0714 nOxolanes
DRA0715 nPyrroles
DRA0716 nPyrazoles
DRA0717 nImidazoles
DRA0718 nFuranes
DRA0719 nThiophenes
DRA0721 nThiazoles
DRA0722 nTriazoles
DRA0723 nPyridines
DRA0724 nPyrimidines
DRA0725 nPyrazines
DRA0726 n135-Triazines
DRA0727 nHDon
DRA0728 nHAcc
DRA0729 C-001
DRA0730 C-002
DRA0731 C-003
DRA0732 C-004
DRA0733 C-005
DRA0734 C-006
DRA0735 C-007
DRA0736 C-008
DRA0737 C-009
DRA0738 C-010
DRA0739 C-011
DRA0740 C-012
DRA0741 C-013
DRA0742 C-014
DRA0743 C-015
DRA0744 C-016
DRA0746 C-018
DRA0747 C-019
DRA0748 C-020
DRA0749 C-021
DRA0750 C-022
DRA0752 C-024
DRA0753 C-025
DRA0754 C-026
DRA0755 C-027
DRA0756 C-028
DRA0757 C-029
DRA0758 C-030
DRA0759 C-031
DRA0760 C-032
DRA0761 C-033
DRA0762 C-034
DRA0763 C-035
DRA0764 C-036
DRA0765 C-037
DRA0766 C-038
DRA0767 C-039
DRA0768 C-040
DRA0769 C-041
DRA0770 C-042
DRA0771 C-043
DRA0772 C-044
DRA0773 H-046
DRA0774 H-047
DRA0775 H-048
DRA0776 H-049
DRA0777 H-050
DRA0778 H-051
DRA0779 H-052
DRA0780 H-053
DRA0781 H-054
DRA0782 O-056
DRA0783 O-057
DRA0784 O-058
DRA0785 O-059
DRA0786 O-060
DRA0787 O-061
DRA0788 O-062
DRA0791 N-067
DRA0792 N-068
DRA0793 N-069
DRA0794 N-070
DRA0795 N-071
DRA0796 N-072
DRA0797 N-073
DRA0798 N-074
DRA0799 N-075
DRA0800 N-076
DRA0801 N-077
DRA0802 N-078
DRA0803 N-079
DRA0804 F-081
DRA0805 F-082
DRA0806 F-083
DRA0807 F-084
DRA0808 F-085
DRA0809 Cl-086
DRA0810 Cl-087
DRA0811 Cl-088
DRA0812 Cl-089
DRA0813 Cl-090
DRA0814 Br-091
DRA0815 Br-093
DRA0816 Br-094
DRA0821 S-107
DRA0822 S-108
DRA0823 S-109
DRA0824 S-110
DRA0825 P-117
DRA0827 Ui
DRA0828 Hy
DRA0829 AMR
DRA0830 TPSA(NO)
DRA0831 TPSA(Tot)
DRA0832 MLOGP
DRA0833 MLOGP2
DRA0834 ALOGP
DRA0835 ALOGP27
A list of 784 Dragon descriptors with their signs and definitions.
836
4
0
Internal Code
Symbol
Definition
Class


## 4. Concluding Remarks

The CPDB rodent carcinogenic database was used for the development of models for the categorization of carcinogenic potency. Initial preprocessing of data and selection of data with carcinogenic potency for rats give us consistent data suitable for QSAR modeling with carcinogenic potency response closer to human. The MDL and Dragon software programs were applied for calculating the molecular descriptors. The topological structure descriptors provided sound bases for classifying molecular structures.The CP ANN model for prediction of carcinogenicity demonstrated good prediction statistics on the test set of 161 compounds with sensitivity of 75% and specificity of 61%–69% in addition to accuracy of 69%–73%.  A diverse external validation set of 738 compounds confirmed the robustness of our models regarding a large applicability domain, yielding the accuracy 60.0%–61.4%, sensitivity 61.8%–64.0%, and specificity 58.4%–58.9%.The carcinogenicity models presented in the study [4] can be used as a support in risk assessment, for instance, in setting priorities among chemicals for further testing. The dataset and additional information presented in the paper can be used in the QSAR modeling, in future research in oncology, and in risk assessment of chemicals.



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*Source: 361615-2013-01-17.xml*