Comparative Assessment of QSAR Models for Aquatic Toxicity

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Histogram of Log Kow2015Frequency10501234Log Kow567Figure 4 - Histogram of training set LogKow distribution.40Histogram of ELUMO30Frequency20100-2.5-2.0-1.5-1.0ELUMO-0.50.00.5Figure 5 - Histogram of training set E LUMO distribution.Since, this QSAR model is based on more than one descriptor, its applicability domain has to beevaluated not only by the separated descriptor distribution analysis but also by accounting for itsoverall model space, which is a two dimensional space. For this purpose, a Principal ComponentAnalysis (PCA) was performed and the Hotelling control chart was used to evaluate how far8
away each chemical was from the PC model hyper plane. The Hotelling ellipse was computedwith a 0.05 (95% confidence) significance level.PC23210-1-254Score plot518480 81 8356 11 8550 7853 797781015 5 20 6 4534733549 795574 75 8887 7693 10372633 2 4 59 101 8223102 3348 868911819 21 22 2790252829 32585760 61 919510492 94 98 97 96 996230314764691767 66100 11410641105 36 109 7042 68 107 3765 108444371 26111 38244016 39110 112 1135214121346-3-3 -2 -1 0 1 2 3 4Figure 6 - Score plot PC1 vs PC2 calculated on LogKow and E LUMO descriptors.This plot highlights the already establish highly influential behavior of the 2,4,6-tri-tertbutylphenol(13) and 2,2'-methylenebis(3,4,6-trichlorophenol) (46), confirming the resultsprovided by the leverage approach.PC1• Internal validation:The model evaluated by leave-one-out internal cross-validation ( Q2LOO) and by bootstrappingwith 5000 iterations shows a good predictive power. It was also verified by Y-scramblingwith 300 iterations: the models based on randomized responses, have all extremely low R 2and Q 2 compared with the real model meaning that the model was not obtained by chancecorrelation.2QLOO2Qboostrap(5000 iterations)SDEP75.94 75.83 0.49522QLOO= explained variance in prediction; Qboostrap= explained variance in prediction bybootstrapping; SDEP = Standard Deviation Error in Prediction9
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 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 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 67 and 68:
Table I - SIDS test data (continued
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Table II - Mixed model (QSAR4) trai
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Table II - Mixed model (QSAR4) trai
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Table IV - QSAR 4 predictions for t
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Table IV - QSAR 4 predictions for t
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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
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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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