Comparative Assessment of QSAR Models for Aquatic Toxicity

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• Internal validation:2The model evaluated by leave-one-out internal cross-validation ( QLOO) shows a moderatepredictive power and according to the bootstrap it is not predictive at all.2QLOO2QLOO= explained variance in prediction;bootstrapping;Prediction2Qboostrap(5000 iterations)2Qext SDEP68.28 9.30 60.73 0.5052Qboostrap= explained variance in prediction by2Qext= external explained variance; SDEP = Standard Deviation Error inPredictions performed for the 9 evaluation chemicals, together with their leverage andstandardized error in prediction are collected in the Table VI. Two chemicals in the test setare out of the applicability domain of the model, since they are identified as Y-outliers:pentachloronaphthalene (128) and 1-formyl-2-fluorobenzene (130).5.4.2 External validation on SIDS test dataThe QSAR model was used to make predictions of SIDS test data.• Model descriptor applicability domainThe domain of applicability with respect to descriptor ranges was evaluated by analyzing thedistribution of the SIDS chemicals in the principal component space. The score plot of thefirst two PCs clearly highlights that not all SIDS chemicals are covered by the model trainingchemicals.7.5Score plot PC1 vs PC2Cum E.V.= 28.2%S148T82T85StatusSidsTraining5.0T121S16PC22.50.0-2.5-5.0T120 S143T98T57S55S161 S131S28S126S118 S50S13 S14S17T119T55 T54 S157S90S144T65T118 S22S170S176S129 S158S20 T117 S99S132 S151S165S177S169 S159 S156 S154S145 S141 S113S142 S124 S125 S122S138 S147S150 S120 S137 S119 S117 S136S163S167 S168S146 S123 S135S130 S116 S107 S103 S106 S97S75 S78S96 S12 S89S100S72 S49 S86 S64 S51 S21 S81 S74 S15S11S92S112 S114 S110S91 S84S3 T86T110 T63T97T99T100S95S88S68S83S73 S53 S37 S36S48 S43 S52 S32 S27 S26 S30S24T84T66 T68T67T36 T35 T7T8 T6 T5 T34T101 T111 T88T71T53T113 T112T79 T80 T81T108 S18 S8 S70S121 S108S111 S76 S94 S23T106 T107 T105 T104 T103 T102S87S134 S77S40 S56 S65 S63 S29 S127S115 S31T114T39 T69 T37 T10 T38 T9 T61T76 T72 T78S47 S82 T75S175 S174S171 S140S59S58S60 S69 S57 S149 S54 S71S128S4 S155S5S6 S109 S7S101 T115 S66 S44T89T74 T83T41T19T18 T17 T15 T14 T13 T3 T12T11 T4T2 T58S102 S39 S62 S67S139S133 S166S45S35S38S41T109S46S61 T91 T90 T93 T16T22 T21 T20T92T62S42 T87T70S98T25 T26 T30 T28 T24 T29 T27T73T116 S152T64T94 T46 T43 T44 T23S172 S33 T48T95 T40 T45 T42 T47 T52 T49 T51 T50S162S173S164S160-5.0 -2.5 0.0 2.5PC1S34T96T565.07.5Figure 21 - SIDS and training score plot PC1 vs PC2 calculated on E-state indices.22
PC4543210-1-2-3-4S167Score plot PC3 vs PC4Cum E.V.= 20.2%T67S149 S94S140S44 S43S76 S125 T65T63S109 T60 T59T82T61 T84 T58 T68S113 S82T62S58 S42T69 T91 T90S159 S138S29T30T29 T28 T27 T26 T25 T24 S38 T66T55 T54S23 T48 T52 S163S146T51 T50 T49 T47 S50T33T32 T31S175S139S75S22S49 S72 S85S174S39 S20T117 T85 T118S131 S133 S124S144 S171 S137S134S130 S60S28S66S98S55S74 S15 S84 S91S88S97 S120 S168 S172 S132S141 S177 S166 S119 S151 S150 S121S147S142 S117 S106S112 S114S86 S110S77S70S69S14 S48 S41S65S62S12 S11 T88 T110 T57T111T7 T6T119T115 T109 T120S57S46S108S107 S104 S102 S100S68 S73 S96 S83 S71S136 S169 S156S116 S61S37 S53S32 S52S10S26 S25 S47 S36T89T86 T83T116T121T113 T106 T107S8S5T114 S35 S45 T112 T105 T104 T103 T99T74T72 T71T36 T35T64T10 T9T2T102T101T17 T34T19 T18 T70 T87T73 T53 T8 T22 T21 T20T100 T98S143S154 S64 S51 S24 S9S128 S105S157 S158 S176 S170 S122 S95S30 S4 T75 T97 T77T46T44 T43 T45 T42T23T41T108S59T94T96S89 S21 S56 S27 S81T78 T76 T12 T15 T14 T13 T16T56S103 S54 T93 T92S67S16S18 T95S90 S92 T81T80 T79 T39 T38 T37 T40T11S152S153S164S161 S129 S145 S111 S123 S40 S63 S3S126S87 S31S93 S165 S33S34S155S162S99 S115 S135 S127 S78S2S160S19S7 S6S118S101S173S17S148StatusSidsTraining-5.0-2.50.02.5PC35.07.510.0Figure 22 - SIDS and training score plot PC3 vs PC4 calculated on E-state indices.Since all the model descriptors are meaningful and relevant, the first four PCs catch onlyabout the 50% of the total explained variance. Thus to avoid analyzing all the modeldimensions the applicability domain according to the descriptors selected in the model wasinvestigated by the Multidimensional scaling approach. The Multidimensional scaling (MDS)can be considered to be an alternative to factor analysis, typically used as an exploratorytechnique to visualize objects in a low dimensional space. In general, the analysis allowsdetection of meaningful underlying dimensions for similarities or dissimilarities (distances)between the investigated chemicals. In factor analysis, the similarities between objects (e.g.,variables) are expressed in the correlation matrix. With MDS it is possible to analyze notonly correlation matrices but also any kind of similarity or dissimilarity matrix. Non-metricmultidimensional scaling is based on a distance matrix computed with any distancemeasures. The algorithm then attempts to place the data points in a two-dimensionalcoordinate system such that the ranked differences are preserved.23
Page 3 and 4: EUROPEAN COMMISSIONDIRECTORATE GENE
Page 8 and 9: 7.2 Model comparison by ratio of QS
Page 12 and 13: 5. Estimation of predictive ability
Page 14 and 15: 4. MIXED MODE OF ACTION QSAR4 EVALU
Page 16: modified; SDEC = Standard Deviation
Page 19 and 20: away each chemical was from the PC
Page 21 and 22: Dotplot of Log KowMOA-SetAChECNSENM
Page 23 and 24: 54Model descriptor space (LogKow -
Page 25 and 26: Moreover, one SIDS chemical (Hexade
Page 28 and 29: The following fitness regression pa
Page 30 and 31: The first two chemicals (1-amino-2,
Page 34 and 35: Multidimensional scaling0.200.15S1S
Page 36 and 37: It has to be pointed out that, whil
Page 38 and 39: 6. TERRAQSAR FHM QSAR 6 EVALUATION6
Page 40 and 41: The TerraQSAR TM FHM program estima
Page 42 and 43: 32• Outlier detection:Residual pl
Page 44 and 45: 8TerraQSAR NN model SIDS prediction
Page 46 and 47: 7. COMPARATIVE ANALYSIS OF THE MODE
Page 48 and 49: factor of 1, 10 and 100, respective
Page 50 and 51: TGD - PN model30N. chemicals2520151
Page 52 and 53: N Model30N. chemicals2520151050< 0.
Page 54 and 55: Mixed model30N. chemicals2520151050
Page 56 and 57: E-State modelN. chemicals3020100< 0
Page 58 and 59: ACKNOWLEDGEMENTSThe authors would l
Page 60 and 61: Kaiser, K.L.E., and S.P. Niculescu.
Page 63 and 64: TABLESTable I - SIDS test data.ID C
Page 65 and 66: Table I - SIDS test data (continued
Page 73 and 74: Table II - Mixed model (QSAR4) trai
Page 75 and 76: Table II - Mixed model (QSAR4) trai
Page 77 and 78: Table IV - QSAR 4 predictions for t
Page 79 and 80: Table IV - QSAR 4 predictions for t
Page 81 and 82: Table V - E-state indices model (QS
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Table V - E-state indices model (QS
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Table VII - SIDS chemicals not suit
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Table VIII -QSAR 5 predictions for
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Table VIII -QSAR 5 predictions for
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Table IX - TerraQSAR (QSAR6) traini
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Table X -TerraQSAR predictions for
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Table XI - Model performance compar
Page 111 and 112:
APPENDIX I: TERMINOLOGY AND STATIST
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nTSS = ∑( yi − y)i=1This is the
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manipulating X to give the so-calle
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where y ˆi/ idenotes the response
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correlation, i.e. collinearity with
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Mission of the JRCThe mission of th
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Comparative Assessment of QSAR Models for Aquatic Toxicity

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