Comparative Assessment of QSAR Models for Aquatic Toxicity

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E-State modelN. chemicals3020100< 0.001 0.001-0.01 0.01-0.1 0.1-1.0 1.0-10 10-100 100-1000 > 10008 training included 0 3 7 21 17 7 2 08 training excluded 0 3 7 14 16 7 2 0E-state model prediction / SIDS test dataModelapplicabilitydomainE-state model applied only to chemicals in its domainN.chemicals3020100< 0.001 0.001- 0.01-0.1 0.1-1.0 1.0-10 10-100 100-1000 > 10008 training included 0 0 0 16 9 0 0 08 training excluded 0 0 0 9 8 0 0 0E-state model prediction / SIDS test dataFigure 35 - Ratio values of the QSAR5 prediction over the SIDS LC50.46
7.2.6 Comparison between TerraQSAR model (QSAR6) and SIDS LC50.The number of reliable predictions of the SIDS chemicals provided by the TerraQSAR modelcould not be evaluated, as well as the model applicability domain.TerraQSARmodelprediction / SIDStest dataN. chem. underestimated N. chem. overestestimated1000Total0 0 1 33 22 0 1 0 57TerraQSAR modelFraction of chemicalsprediction / SIDS test datawithin a factor of:(%)1 60 (= (33+1/57)*100)1098 (=(33+1+22/57)*100)10098 (=(33+1+22/57)*100)Terra QSARN.chemicals302520151050< 0.001 0.001- 0.01-0.1 0.1-1.0 1.0-10 10-100 100- > 1000Terra QSAR 0 0 1 33 22 0 1 0TerraQSAR model prediction / SIDS test dataFigure 36 - Ratio values of the QSAR6 prediction over the SIDS LC50.In this case it was not possible to evaluate the model applicability domain due to lack ofinformation provided by the authors.47
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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 32 and 33: • Internal validation:2The model
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 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
Page 83 and 84: Table V - E-state indices model (QS
Page 85 and 86: Table VII - SIDS chemicals not suit
Page 87 and 88: Table VIII -QSAR 5 predictions for
Page 89 and 90: Table VIII -QSAR 5 predictions for
Page 91 and 92: Table IX - TerraQSAR (QSAR6) traini
Page 103 and 104: Table X -TerraQSAR predictions for
Page 105 and 106: Table X -TerraQSAR predictions for
Page 107 and 108:
Table X -TerraQSAR predictions for
Page 109 and 110:
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
Page 117 and 118:
where y ˆi/ idenotes the response
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correlation, i.e. collinearity with
Page 122:
Mission of the JRCThe mission of th
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Comparative Assessment of QSAR Models for Aquatic Toxicity

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