diff options
| author | Christoph Helma <helma@in-silico.ch> | 2017-02-13 15:24:11 +0100 |
|---|---|---|
| committer | Christoph Helma <helma@in-silico.ch> | 2017-02-13 15:24:11 +0100 |
| commit | 04baa2d6ddab1963759f99c87cf8f87cbd435831 (patch) | |
| tree | 9302cf57ba42b8c7efb76515e7acafb95ea6e683 | |
| parent | db82eef974b8783c40e7daa504feead3f555fdb8 (diff) | |
adjustments for latest lazar version
38 files changed, 4776 insertions, 1128 deletions
@@ -1,7 +1,7 @@ # Variables datasets = data/median-correlation.csv data/test_log10.csv data/training_log10.csv data/mazzatorta_log10.csv data/swiss_log10.csv data/test.json data/training.json data/mazzatorta.json data/swiss.json -crossvalidations = data/training-cv-0.csv data/training-cv-1.csv data/training-cv-2.csv +crossvalidations = data/training_log10-cv-0.csv data/training_log10-cv-1.csv data/training_log10-cv-2.csv validations = data/training-test-predictions.csv $(crossvalidations) data/misclassifications.csv figures = figures/functional-groups.pdf figures/test-prediction.pdf figures/test-correlation.pdf figures/crossvalidation.pdf figures/dataset-variability.pdf @@ -11,7 +11,7 @@ loael.pdf: loael.md references.bibtex pandoc -r markdown+simple_tables+table_captions+yaml_metadata_block -s -S --bibliography=references.bibtex --latex-engine=pdflatex --filter pandoc-crossref --filter pandoc-citeproc -o loael.pdf loael.md loael.md: loael.Rmd $(figures) $(datasets) $(validations) - scripts/--vanilla -e "library(knitr); knit('loael.Rmd');" + Rscript --vanilla -e "library(knitr); knit('loael.Rmd');" loael.docx: loael.md pandoc --filter pandoc-crossref --filter pandoc-citeproc loael.md -s -o loael.docx @@ -38,17 +38,20 @@ figures/test-correlation.pdf: data/training-test-predictions.csv data/median-cor data/misclassifications.csv: data/training-test-predictions.csv scripts/misclassifications.rb -data/training-test-predictions.csv: data/test_log10.csv data/training_log10.csv - scripts/test-validation.rb training.csv +data/training-test-predictions.csv: data/training-test-predictions.id + scripts/test-validation-results.rb > data/training-test-predictions.csv -data/training-cv-0.csv: data/training_log10.csv - scripts/crossvalidation.rb training.csv 0 +data/training-test-predictions.id: data/test_log10.csv data/training_log10.csv + scripts/test-validation.rb > data/training-test-predictions.id -data/training-cv-1.csv: data/training_log10.csv - scripts/crossvalidation.rb training.csv 1 +data/training_log10-cv-0.csv: data/training_log10.csv + scripts/crossvalidation.rb training_log10.csv 0 -data/training-cv-2.csv: data/training_log10.csv - scripts/crossvalidation.rb training.csv 2 +data/training_log10-cv-1.csv: data/training_log10.csv + scripts/crossvalidation.rb training_log10.csv 1 + +data/training_log10-cv-2.csv: data/training_log10.csv + scripts/crossvalidation.rb training_log10.csv 2 # Datasets @@ -56,19 +59,19 @@ data/functional-groups-reduced4R.csv: data/functional-groups-reduced.csv scripts/functional-groups4R.rb # Medians for dataset correlation -data/median-correlation.csv: data/mazzatorta.csv data/swiss.csv - scripts/create-median-correlation.rb +data/median-correlation.csv: data/mazzatorta_log10.csv data/swiss_log10.csv + scripts/create-median-correlation.rb > data/median-correlation.csv # Test set data/test_log10.csv: data/mazzatorta_log10.csv data/swiss_log10.csv - scripts/create-test.rb + scripts/create-test.rb > data/test_log10.csv data/test.json: data/mazzatorta.json cp data/mazzatorta.json data/test.json # Combined training set data/training_log10.csv: data/mazzatorta_log10.csv data/swiss_log10.csv - scripts/create-training.rb + scripts/create-training.rb > data/training_log10.csv data/training.json: data/mazzatorta.json cp data/mazzatorta.json data/training.json diff --git a/data/common.csv b/data/common.csv index db3c68f..67efef6 100644 --- a/data/common.csv +++ b/data/common.csv @@ -1,59 +1,59 @@ -BrC1COC(C1)(Cn1cncn1)c1ccc(cc1Cl)Cl,0.017185,"mazzatorta, swiss" -c1ccc(cc1)Nc1ccccc1,0.14773,"mazzatorta, swiss" -c1scc(n1)c1nc2c([nH]1)cccc2,0.19876,"mazzatorta, swiss" -Cc1nc(Nc2ccccc2)nc(c1)C1CC1,0.15802,"mazzatorta, swiss" -CC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)C,0.42802,"mazzatorta, swiss" -CC(Cc1ccc(cc1)C(C)(C)C)CN1CC(C)OC(C1)C,0.0056016,"mazzatorta, swiss" -CCCC1COC(O1)(Cn1cncn1)c1ccc(cc1Cl)Cl,0.28052,"mazzatorta, swiss" -CCCCN(SN(C(=O)Oc1cccc2c1OC(C2)(C)C)C)CCCC,0.065695,"mazzatorta, swiss" -CCCCOCCOCCOCc1cc2OCOc2cc1CCC,0.29547,"mazzatorta, swiss" -C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,0.050475,"mazzatorta, swiss" -CCN(C(=O)C(Oc1cccc2c1cccc2)C)CC,0.36852,"mazzatorta, swiss" -CCOc1ccc2c(c1)C(=CC(N2)(C)C)C,0.055221,"mazzatorta, swiss" -CC(Oc1cccc(c1)NC(=O)c1ccccc1C(F)(F)F)C,0.26909,"mazzatorta, swiss" -CCOC(=O)c1cn2nc(cc2nc1C)OP(=S)(OCC)OCC,0.010713,"mazzatorta, swiss" -CCOC(=O)C(Cc1cc(c(cc1Cl)F)n1nc(n(c1=O)C(F)F)C)Cl,0.029113,"mazzatorta, swiss" -CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,0.005604,"mazzatorta, swiss" -CCOP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OCC,0.0028524,"mazzatorta, swiss" -CCOP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OCC,0.028524,"mazzatorta, swiss" -CCOP(=S)(Oc1ncn(n1)c1ccccc1)OCC,0.0041492,"mazzatorta, swiss" -CCSCSP(=S)(OCC)OCC,0.00061449,"mazzatorta, swiss" -Clc1ccccc1c1nnc(nn1)c1ccccc1Cl,0.057068,"mazzatorta, swiss" -Clc1cc(Cl)c(cc1n1nc(n(c1=O)C(F)F)C)NS(=O)(=O)C,0.17304,"mazzatorta, swiss" -ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.016161,"mazzatorta, swiss" -ClC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)Cl,0.063892,"mazzatorta, swiss" -ClC(SN1C(=O)C2C(C1=O)CC=CC2)(Cl)Cl,0.33268,"mazzatorta, swiss" -CNC(=O)Oc1cccc2c1OC(C2)(C)C,0.022599,"mazzatorta, swiss" -CN(C(=O)Oc1nc(nc(c1C)C)N(C)C)C,0.051619,"mazzatorta, swiss" -CN(C(=S)SSC(=S)N(C)C)C,0.04991,"mazzatorta, swiss" -COc1cccc(c1C)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,1.1154,"mazzatorta, swiss" -COCC(N(c1c(C)cccc1CC)C(=O)CCl)C,0.52855,"mazzatorta, swiss" -COCN(c1c(CC)cccc1CC)C(=O)CCl,0.051897,"mazzatorta, swiss" -COC(=O)c1c(Cl)c(Cl)c(c(c1Cl)Cl)C(=O)OC,0.030124,"mazzatorta, swiss" -COC(=O)c1c(nc(c(c1CC(C)C)C1=NCCS1)C(F)(F)F)C(F)F,0.11151,"mazzatorta, swiss" -COC(=O)N(c1ccccc1COc1ccn(n1)c1ccc(cc1)Cl)OC,0.023207,"mazzatorta, swiss" -COC(=O)N(C(=O)N1COC2(C(=N1)c1ccc(cc1C2)Cl)C(=O)OC)c1ccc(cc1)OC(F)(F)F,0.0068203,"mazzatorta, swiss" -COC(=O)NC(=S)Nc1ccccc1NC(=S)NC(=O)OC,0.09346,"mazzatorta, swiss" -COC(=O)NS(=O)(=O)c1ccc(cc1)N,0.78179,"mazzatorta, swiss" -CON(C(=O)Nc1ccc(c(c1)Cl)Cl)C,0.025091,"mazzatorta, swiss" -COP(=O)(NC(=O)C)SC,0.013649,"mazzatorta, swiss" -COP(=O)(OC=C(Cl)Cl)OC,0.010408,"mazzatorta, swiss" -COP(=O)(SC)N,0.0020549,"mazzatorta, swiss" -CSc1ccc(cc1C)OP(=S)(OC)OC,0.0025869,"mazzatorta, swiss" -N#Cc1sc2=c(sc1C#N)c(=O)c1c(c2=O)cccc1,0.020248,"mazzatorta, swiss" -N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Br)Br,0.0049485,"mazzatorta, swiss" -N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.12011,"mazzatorta, swiss" -N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.18016,"mazzatorta, swiss" -N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C(c1ccc(cc1)Cl)C(C)C,0.059538,"mazzatorta, swiss" -Oc1ccccc1c1ccccc1,5.8752,"mazzatorta, swiss" -O=C(c1ccc(cc1S(=O)(=O)C)C(F)(F)F)c1cnoc1C1CC1,0.055661,"mazzatorta, swiss" -O=C(C1(C)CCCCC1)Nc1ccc(c(c1Cl)Cl)O,0.96626,"mazzatorta, swiss" -O=Cc1ccco1,0.62445,"mazzatorta, swiss" -OC(C(C)(C)C)C(n1cncn1)Oc1ccc(cc1)c1ccccc1,0.074093,"mazzatorta, swiss" -O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.38809,"mazzatorta, swiss" -O=CNC(C(Cl)(Cl)Cl)N1CCN(CC1)C(C(Cl)(Cl)Cl)NC=O,0.22991,"mazzatorta, swiss" -OC(=O)CNCP(=O)(O)O,1.7744,"mazzatorta, swiss" -OC(=O)COc1ccc(cc1Cl)Cl,0.022621,"mazzatorta, swiss" -OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.00027647,"mazzatorta, swiss" -OC(=O)COc1nc(F)c(c(c1Cl)N)Cl,1.9605,"mazzatorta, swiss" -O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.0071266,"mazzatorta, swiss" +BrC1COC(C1)(Cn1cncn1)c1ccc(cc1Cl)Cl,0.017185,"mazzatorta and swiss" +c1ccc(cc1)Nc1ccccc1,0.14773,"mazzatorta and swiss" +c1scc(n1)c1nc2c([nH]1)cccc2,0.19876,"mazzatorta and swiss" +Cc1nc(Nc2ccccc2)nc(c1)C1CC1,0.15802,"mazzatorta and swiss" +CC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)C,0.42802,"mazzatorta and swiss" +CC(Cc1ccc(cc1)C(C)(C)C)CN1CC(C)OC(C1)C,0.0056016,"mazzatorta and swiss" +CCCC1COC(O1)(Cn1cncn1)c1ccc(cc1Cl)Cl,0.28052,"mazzatorta and swiss" +CCCCN(SN(C(=O)Oc1cccc2c1OC(C2)(C)C)C)CCCC,0.065695,"mazzatorta and swiss" +CCCCOCCOCCOCc1cc2OCOc2cc1CCC,0.29547,"mazzatorta and swiss" +C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,0.050475,"mazzatorta and swiss" +CCN(C(=O)C(Oc1cccc2c1cccc2)C)CC,0.36852,"mazzatorta and swiss" +CCOc1ccc2c(c1)C(=CC(N2)(C)C)C,0.055221,"mazzatorta and swiss" +CC(Oc1cccc(c1)NC(=O)c1ccccc1C(F)(F)F)C,0.26909,"mazzatorta and swiss" +CCOC(=O)c1cn2nc(cc2nc1C)OP(=S)(OCC)OCC,0.010713,"mazzatorta and swiss" +CCOC(=O)C(Cc1cc(c(cc1Cl)F)n1nc(n(c1=O)C(F)F)C)Cl,0.029113,"mazzatorta and swiss" +CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,0.005604,"mazzatorta and swiss" +CCOP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OCC,0.0028524,"mazzatorta and swiss" +CCOP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OCC,0.028524,"mazzatorta and swiss" +CCOP(=S)(Oc1ncn(n1)c1ccccc1)OCC,0.0041492,"mazzatorta and swiss" +CCSCSP(=S)(OCC)OCC,0.00061449,"mazzatorta and swiss" +Clc1ccccc1c1nnc(nn1)c1ccccc1Cl,0.057068,"mazzatorta and swiss" +Clc1cc(Cl)c(cc1n1nc(n(c1=O)C(F)F)C)NS(=O)(=O)C,0.17304,"mazzatorta and swiss" +ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.016161,"mazzatorta and swiss" +ClC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)Cl,0.063892,"mazzatorta and swiss" +ClC(SN1C(=O)C2C(C1=O)CC=CC2)(Cl)Cl,0.33268,"mazzatorta and swiss" +CNC(=O)Oc1cccc2c1OC(C2)(C)C,0.022599,"mazzatorta and swiss" +CN(C(=O)Oc1nc(nc(c1C)C)N(C)C)C,0.051619,"mazzatorta and swiss" +CN(C(=S)SSC(=S)N(C)C)C,0.04991,"mazzatorta and swiss" +COc1cccc(c1C)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,1.1154,"mazzatorta and swiss" +COCC(N(c1c(C)cccc1CC)C(=O)CCl)C,0.52855,"mazzatorta and swiss" +COCN(c1c(CC)cccc1CC)C(=O)CCl,0.051897,"mazzatorta and swiss" +COC(=O)c1c(Cl)c(Cl)c(c(c1Cl)Cl)C(=O)OC,0.030124,"mazzatorta and swiss" +COC(=O)c1c(nc(c(c1CC(C)C)C1=NCCS1)C(F)(F)F)C(F)F,0.11151,"mazzatorta and swiss" +COC(=O)N(c1ccccc1COc1ccn(n1)c1ccc(cc1)Cl)OC,0.023207,"mazzatorta and swiss" +COC(=O)N(C(=O)N1COC2(C(=N1)c1ccc(cc1C2)Cl)C(=O)OC)c1ccc(cc1)OC(F)(F)F,0.0068203,"mazzatorta and swiss" +COC(=O)NC(=S)Nc1ccccc1NC(=S)NC(=O)OC,0.09346,"mazzatorta and swiss" +COC(=O)NS(=O)(=O)c1ccc(cc1)N,0.78179,"mazzatorta and swiss" +CON(C(=O)Nc1ccc(c(c1)Cl)Cl)C,0.025091,"mazzatorta and swiss" +COP(=O)(NC(=O)C)SC,0.013649,"mazzatorta and swiss" +COP(=O)(OC=C(Cl)Cl)OC,0.010408,"mazzatorta and swiss" +COP(=O)(SC)N,0.0020549,"mazzatorta and swiss" +CSc1ccc(cc1C)OP(=S)(OC)OC,0.0025869,"mazzatorta and swiss" +N#Cc1sc2=c(sc1C#N)c(=O)c1c(c2=O)cccc1,0.020248,"mazzatorta and swiss" +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Br)Br,0.0049485,"mazzatorta and swiss" +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.12011,"mazzatorta and swiss" +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.18016,"mazzatorta and swiss" +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C(c1ccc(cc1)Cl)C(C)C,0.059538,"mazzatorta and swiss" +Oc1ccccc1c1ccccc1,5.8752,"mazzatorta and swiss" +O=C(c1ccc(cc1S(=O)(=O)C)C(F)(F)F)c1cnoc1C1CC1,0.055661,"mazzatorta and swiss" +O=C(C1(C)CCCCC1)Nc1ccc(c(c1Cl)Cl)O,0.96626,"mazzatorta and swiss" +O=Cc1ccco1,0.62445,"mazzatorta and swiss" +OC(C(C)(C)C)C(n1cncn1)Oc1ccc(cc1)c1ccccc1,0.074093,"mazzatorta and swiss" +O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.38809,"mazzatorta and swiss" +O=CNC(C(Cl)(Cl)Cl)N1CCN(CC1)C(C(Cl)(Cl)Cl)NC=O,0.22991,"mazzatorta and swiss" +OC(=O)CNCP(=O)(O)O,1.7744,"mazzatorta and swiss" +OC(=O)COc1ccc(cc1Cl)Cl,0.022621,"mazzatorta and swiss" +OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.00027647,"mazzatorta and swiss" +OC(=O)COc1nc(F)c(c(c1Cl)N)Cl,1.9605,"mazzatorta and swiss" +O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.0071266,"mazzatorta and swiss" diff --git a/data/mazzatorta_log10.csv b/data/mazzatorta_log10.csv new file mode 100644 index 0000000..6717ade --- /dev/null +++ b/data/mazzatorta_log10.csv @@ -0,0 +1,568 @@ +SMILES,LOAEL +ClC12C3C4(C(C1(Cl)Cl)(C1(C2(C3(Cl)C(C41Cl)(Cl)Cl)Cl)Cl)Cl)Cl,4.7084979508104 +ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C=C2)(Cl)Cl)Cl,4.562186041152654 +ClC1C2OC2C2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,4.192363513110009 +ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,3.8818676000790955 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+CCC(Nc1c(cc(cc1[N+](=O)[O-])C(C)(C)C)[N+](=O)[O-])C,0.7713430418910647,mazzatorta +O=C1N(c2cc(Cl)cc(c2)Cl)C(=O)C2(C1(C)C2)C,0.7722932179393288,swiss +ClC(SN1C(=O)c2c(C1=O)cccc2)(Cl)Cl,0.7731424297112764,mazzatorta +CC(N1C(=O)c2ccccc2NS1(=O)=O)C,0.7786640204661717,mazzatorta +Cc1cccc2c1n1cnnc1s2,0.7856330784033956,mazzatorta +Cc1nc(Nc2ccccc2)nc(c1)C1CC1,0.8012879425395989,mazzatorta and swiss +OC(=O)C(Oc1ccc(cc1Cl)Cl)C,0.8088844776196935,swiss +COC(=O)NC(=S)Nc1ccccc1NC(=S)NC(=O)OC,0.8102570997584302,swiss +CCC(c1noc(c1)NC(=O)c1c(OC)cccc1OC)(CC)C,0.8166447297394875,mazzatorta +CCOC(=O)CC(C(=O)OCC)SP(=S)(OC)OC,0.8200175747074082,mazzatorta +N#Cc1c(Cl)c(C#N)c(c(c1Cl)Cl)Cl,0.8226655444942998,swiss +CC(N1C(=O)c2ccccc2NS1(=O)=O)C,0.8244012204816238,swiss +c1scc(n1)c1nc2c([nH]1)cccc2,0.8266097485348047,mazzatorta +c1ccc(cc1)Nc1ccccc1,0.8305313021730596,mazzatorta and swiss +CCOc1cc(ccc1[N+](=O)[O-])Oc1ccc(cc1Cl)C(F)(F)F,0.8340734503909036,swiss +CC(N1C(=O)c2ccccc2NS1(=O)=O)C,0.8366596943817484,mazzatorta +CC(OC(=O)Nc1cccc(c1)Cl)C,0.8526019606523453,swiss +OC(=O)COc1nc(Cl)c(cc1Cl)Cl,0.8527257000881574,mazzatorta +CC(NC(=O)N1CC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl)C,0.8559865342138013,mazzatorta +CC(C(c1cncnc1)(c1ccc(cc1)OC(F)(F)F)O)C,0.8600894441243174,swiss +CCC(Nc1c(cc(cc1[N+](=O)[O-])C(C)(C)C)[N+](=O)[O-])C,0.8617920673318369,swiss +Cc1cccc2c1n1cnnc1s2,0.8620448764676394,swiss +CCc1ccc(cc1)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,0.8622662052462875,swiss +CCc1ccc(cc1)C(=O)NN(C(C)(C)C)C(=O)c1cc(C)cc(c1)C,0.8658866701576757,mazzatorta +ClC(SN1C(=O)c2c(C1=O)cccc2)(Cl)Cl,0.8700524427193329,mazzatorta +C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,0.870954940112042,mazzatorta +O=C(N(C)C)Nc1cccc(c1)C(F)(F)F,0.8887374863409347,swiss +ClC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)Cl,0.8935371162038789,swiss +O=C(C1=C(C)OCCS1)Nc1ccccc1,0.894489815230026,mazzatorta +CON(C(=O)Nc1ccc(c(c1)Cl)Cl)C,0.9015293343493707,swiss +CNC(=O)Oc1cc(C)c(c(c1)C)SC,0.9056337016638656,swiss +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.9204208330298691,mazzatorta and swiss +N#CC(c1ccccc1)(Cn1cncn1)CCc1ccc(cc1)Cl,0.925329811075993,mazzatorta +CCNc1nc(NC(C)C)nc(n1)Cl,0.9358790941703782,mazzatorta +Clc1cc(ccc1Oc1ccc(c(c1)C(=O)NS(=O)(=O)C)[N+](=O)[O-])C(F)(F)F,0.9432475594325607,mazzatorta +COC(=O)c1c(nc(c(c1CC(C)C)C1=NCCS1)C(F)(F)F)C(F)F,0.9526861841846117,mazzatorta and swiss +CCOc1cc(ccc1[N+](=O)[O-])Oc1ccc(cc1Cl)C(F)(F)F,0.9562841419387937,mazzatorta +CCNc1nc(NC(C)C)nc(n1)Cl,0.9609032895585495,swiss +OC(=O)COc1ccc(cc1C)Cl,1.0236179201622833,swiss +COC(=O)NC(=S)Nc1ccccc1NC(=S)NC(=O)OC,1.0293742233117054,mazzatorta and swiss +N#CC(c1ccccc1)(Cn1cncn1)CCc1ccc(cc1)Cl,1.0360328271872385,swiss +CCC(=O)Nc1ccc(c(c1)Cl)Cl,1.0375833065542488,mazzatorta +Nc1nc(NC2CC2)nc(n1)N,1.0444950477343224,mazzatorta +O=C1N(OCC1(C)C)Cc1ccccc1Cl,1.047226923919808,mazzatorta +N#CC(c1ccccc1)(Cn1cncn1)CCc1ccc(cc1)Cl,1.0502734235899835,mazzatorta +CCOC(=O)CC(C(=O)OCC)SP(=S)(OC)OC,1.0565846338083325,swiss +O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,1.070034717984175,mazzatorta +OC(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,1.0730045445647032,mazzatorta +ClC(SN1C(=O)c2c(C1=O)cccc2)(Cl)Cl,1.0741672736326,swiss +COP(=S)(Oc1ccc(c(c1)C)[N+](=O)[O-])OC,1.0792351019025128,swiss +ClCCP(=O)(O)O,1.0806708219654175,mazzatorta +O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,1.0823684740575847,swiss +CCOC(=O)NCCOc1ccc(cc1)Oc1ccccc1,1.086355661467734,swiss +COP(=O)(C(C(Cl)(Cl)Cl)O)OC,1.1096404686828705,mazzatorta +CNC(=O)Oc1cccc2c1cccc2,1.1105470212498103,mazzatorta +Clc1ccc(cc1)CN(C(=O)Nc1ccccc1)C1CCCC1,1.1190378586994414,mazzatorta +ClCCOc1ccccc1S(=O)(=O)NC(=O)Nc1nc(C)nc(n1)OC,1.1269178314119968,swiss +OC(C(C)(C)C)C(n1cncn1)Oc1ccc(cc1)c1ccccc1,1.130222820427892,mazzatorta and swiss +O=C(N(C)C)Nc1cccc(c1)C(F)(F)F,1.1310207139253916,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(c(c1)Cl)OC(C(OC(F)(F)F)F)(F)F,1.1362846674663016,mazzatorta +CCCC1COC(O1)(Cn1cncn1)c1ccc(cc1Cl)Cl,1.13636788955756,mazzatorta +C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,1.1432529892239978,swiss +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)(C)C,1.1454085716982032,mazzatorta +CSc1nnc(c(=O)n1N)C(C)(C)C,1.1549081642369436,mazzatorta +COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1Cl,1.155665748672126,mazzatorta +CCOc1ccc(cc1)C(COCc1cccc(c1)Oc1ccccc1)(C)C,1.1607797146866263,mazzatorta +C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,1.16083631708535,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,1.1660755529404674,mazzatorta +CCOc1ccc(cc1)C(COCc1cccc(c1)Oc1ccccc1)(C)C,1.1692125122974353,swiss +CSc1nnc(c(=O)n1N)C(C)(C)C,1.1726371897097654,swiss +Clc1ccccc1c1nnc(nn1)c1ccccc1Cl,1.1806206011079126,mazzatorta +CCCCN(SN(C(=O)Oc1cccc2c1OC(C2)(C)C)C)CCCC,1.182467683028378,mazzatorta and swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,1.1831094015116075,mazzatorta +CC1N(C(=O)NC2CCCCC2)C(=O)SC1c1ccc(cc1)Cl,1.1840107302272953,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,1.1865991709300072,swiss +O=C(N(C)C)Nc1cccc(c1)C(F)(F)F,1.189774204881748,mazzatorta +OC(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,1.1921944677293754,swiss +CC(C1(C)N=C(NC1=O)c1nc2ccccc2cc1C(=O)O)C,1.192201228281402,mazzatorta +ClC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)Cl,1.1945535170115598,mazzatorta and swiss +CCOP(=S)(SCn1c(=O)oc2c1ccc(c2)Cl)OCC,1.1964063352286556,swiss +COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,1.2004664736572608,mazzatorta +CN(C(=S)SSC(=S)N(C)C)C,1.2049058977607419,mazzatorta +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(c(c1)Cl)OC(C(OC(F)(F)F)F)(F)F,1.2068664410308745,swiss +CC(OC(=O)C(c1ccc(cc1)Br)(c1ccc(cc1)Br)O)C,1.2165895677016367,mazzatorta +CSc1nnc(c(=O)n1N)C(C)(C)C,1.2170546392276442,swiss +CC(OC(=O)C(c1ccc(cc1)Br)(c1ccc(cc1)Br)O)C,1.2182589824272165,swiss +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)(C)C,1.2211327542550814,swiss +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C(c1ccc(cc1)Cl)C(C)C,1.225205758260884,mazzatorta and swiss +CCCN(C(=O)n1cncc1)CCOc1c(Cl)cc(cc1Cl)Cl,1.2435160355573907,swiss +Clc1ccccc1c1nnc(nn1)c1ccccc1Cl,1.2436073474297324,mazzatorta and swiss +COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1Cl,1.2525804230510118,swiss +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)(C)C,1.2544333907376006,mazzatorta +O=C(c1ccc(cc1S(=O)(=O)C)C(F)(F)F)c1cnoc1C1CC1,1.2544489954403404,mazzatorta and swiss +COCN(c1c(CC)cccc1CC)C(=O)CCl,1.2548939653344497,mazzatorta +CCOc1ccc2c(c1)C(=CC(N2)(C)C)C,1.2578957329638771,mazzatorta and swiss +C#CCC1=C(C)C(CC1=O)OC(=O)C1C(C1(C)C)C=C(C)C,1.2602062139183252,swiss +C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,1.265896559378767,mazzatorta +OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,1.2677429840395533,mazzatorta +C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,1.2716218656999372,swiss +Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,1.2770454101830115,swiss +O=C1N(c2cc(Cl)cc(c2)Cl)C(=O)C2(C1(C)C2)C,1.2774401112445704,mazzatorta +N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,1.2798406965940432,swiss +CN(C(=O)Oc1nc(nc(c1C)C)N(C)C)C,1.280188271227964,mazzatorta +COCN(c1c(CC)cccc1CC)C(=O)CCl,1.2848577466043891,mazzatorta and swiss +Cc1ccc(cc1)N(S(=O)(=O)N(C)C)SC(Cl)(Cl)F,1.285376897128516,mazzatorta +COP(=O)(C(C(Cl)(Cl)Cl)O)OC,1.286820378541783,swiss +Clc1ccc(cc1)CCC(C(C)(C)C)(Cn1cncn1)O,1.2868960417984983,mazzatorta +CN(C(=O)Oc1nc(nc(c1C)C)N(C)C)C,1.287190413172402,mazzatorta and swiss +C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,1.2969236723843232,mazzatorta and swiss +ClCC1CN(C(=O)C1Cl)c1cccc(c1)C(F)(F)F,1.2984147471171126,swiss +CN(C(=S)SSC(=S)N(C)C)C,1.3018124301338776,mazzatorta and swiss +CON(C(=O)Nc1ccc(cc1)Br)C,1.3165566912097522,mazzatorta +CN(C(=S)SSC(=S)N(C)C)C,1.3202996191280358,mazzatorta +CC1=C(C)S(=O)(=O)CCS1(=O)=O,1.322776417451734,mazzatorta +CCNc1nc(NC(C)C)nc(n1)Cl,1.3338191028424158,swiss +O=C1OC(C(=O)N1Nc1ccccc1)(C)c1ccc(cc1)Oc1ccccc1,1.3428771906003532,swiss +CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,1.344900000029291,mazzatorta +O=C1OC(C(=O)N1Nc1ccccc1)(C)c1ccc(cc1)Oc1ccccc1,1.3480148945878891,mazzatorta +CC(=CC1C(C1(C)C)C(=O)OCc1coc(c1)Cc1ccccc1)C,1.3533904493269542,swiss +N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,1.354479485094126,swiss +C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,1.3685663781727047,swiss +CC(N(C(=O)SCC(=C(Cl)Cl)Cl)C(C)C)C,1.369877357140688,swiss +Clc1ccc(c(c1)Cl)C1(OCCO1)Cn1cncn1,1.370111513834565,swiss +CCOC(=O)C(Oc1ccc(cc1)Oc1cnc2c(n1)ccc(c2)Cl)C,1.3811468505131965,swiss +CNC(=O)Oc1cc(C)c(c(c1)C)SC,1.3842918746267547,mazzatorta +CCC(=O)Nc1ccc(c(c1)Cl)Cl,1.384376054498974,swiss +CC(N(C(=O)SCC(=C(Cl)Cl)Cl)C(C)C)C,1.386909067965439,mazzatorta +CC(C(c1cncnc1)(c1ccc(cc1)OC(F)(F)F)O)C,1.411773125869789,mazzatorta +OC(=O)C(Oc1ccc(cc1Cl)Cl)C,1.4169486617141684,mazzatorta +CCOCn1c(c2ccc(cc2)Cl)c(c(c1C(F)(F)F)Br)C#N,1.4341521813264824,swiss +CC(NC(=O)N1CC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl)C,1.4395553268068126,swiss +O=C(C1=C(C)OCCS1)Nc1ccccc1,1.4578096795663429,swiss +CCCCC(c1ccc(cc1)Cl)(Cn1cncn1)C#N,1.4649211813101468,swiss +CCCCC(c1ccc(cc1)Cl)(Cn1cncn1)C#N,1.467564135493289,mazzatorta +CCCCC(c1ccc(cc1)Cl)(Cn1cncn1)C#N,1.4693393488275133,mazzatorta +N#Cc1sc2=c(sc1C#N)c(=O)c1c(c2=O)cccc1,1.4717648285027363,mazzatorta +CCOCn1c(c2ccc(cc2)Cl)c(c(c1C(F)(F)F)Br)C#N,1.47670887081321,mazzatorta +Clc1ccc(c(c1)Cl)C1(OCCO1)Cn1cncn1,1.4773210761437898,mazzatorta +CCOC(=O)NCCOc1ccc(cc1)Oc1ccccc1,1.4790581780219383,mazzatorta +CN1CN(C)CSC1=S,1.4859838195993507,swiss +Fc1ccc(cc1)[Si](c1ccc(cc1)F)(Cn1cncn1)C,1.4988448473794327,swiss +CC(C(c1cncnc1)(c1ccc(cc1)OC(F)(F)F)O)C,1.5033332211199513,swiss +CN(C(=S)SSC(=S)N(C)C)C,1.5176696240691985,swiss +COC(=O)c1c(Cl)c(Cl)c(c(c1Cl)Cl)C(=O)OC,1.521087361070694,mazzatorta and swiss +CCOC(=O)c1cn2nc(cc2nc1C)OP(=S)(OCC)OCC,1.530737774822598,swiss +CCOC(=O)C(Cc1cc(c(cc1Cl)F)n1nc(n(c1=O)C(F)F)C)Cl,1.5359130396208096,mazzatorta and swiss +N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,1.5408639423123824,mazzatorta +CCOP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OCC,1.5447895722241949,mazzatorta and swiss +COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,1.5472631473134424,mazzatorta +CC(N(C(=O)SCC(=C(Cl)Cl)Cl)C(C)C)C,1.5493198912379038,swiss +C=CC1(C)OC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl,1.5534473004973242,swiss +N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,1.558603397145632,mazzatorta +ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,1.5605567725507532,mazzatorta +O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,1.56812182123316,swiss +O=C(N(C)C)Nc1ccc(c(c1)Cl)Cl,1.571654591815115,mazzatorta +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,1.5892227666227903,mazzatorta +O=C(N(C)C)Nc1ccc(c(c1)Cl)Cl,1.5893745854018753,mazzatorta +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,1.600222475511201,mazzatorta +CON(C(=O)Nc1ccc(c(c1)Cl)Cl)C,1.6004820295651263,mazzatorta and swiss +COC(=O)N(c1ccccc1COc1ccn(n1)c1ccc(cc1)Cl)OC,1.6343809977454966,mazzatorta and swiss +CNC(=O)ON=C(C(=O)N(C)C)SC,1.6419889676679056,mazzatorta +CN(C(=S)SSC(=S)N(C)C)C,1.6429995095590777,mazzatorta +OC(=O)COc1ccc(cc1Cl)Cl,1.6454882002557272,mazzatorta and swiss +CNC(=O)Oc1cccc2c1OC(C2)(C)C,1.6459107778477526,mazzatorta and swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,1.6472195324286747,mazzatorta +CNC(=O)ON=C(C(=O)N(C)C)SC,1.6507613373118732,swiss +CN1CN(C)CSC1=S,1.6539601434301947,swiss +CNC(=O)CSP(=S)(OC)OC,1.6613443344212997,mazzatorta +N#Cc1sc2=c(sc1C#N)c(=O)c1c(c2=O)cccc1,1.6936178678498022,mazzatorta and swiss +OC(=O)COc1ccc(cc1C)Cl,1.7003184083376452,mazzatorta +CCCN(C(=O)n1cncc1)CCOc1c(Cl)cc(cc1Cl)Cl,1.7008851163994632,mazzatorta +CCN(C(=O)SCc1ccc(cc1)Cl)CC,1.7122878245565132,mazzatorta +OC(=O)C(CCP(=O)(O)C)N,1.7139254461244264,mazzatorta +CNC(=O)ON=C(C(=O)N(C)C)SC,1.718739312944987,swiss +CCOP(=S)(Oc1cc(C)nc(n1)C(C)C)OCC,1.7199454660267528,mazzatorta +N#Cc1c(Cl)cccc1Cl,1.7304170139333868,swiss +N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,1.7627076624325413,mazzatorta +ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,1.7646735975166132,mazzatorta +BrC1COC(C1)(Cn1cncn1)c1ccc(cc1Cl)Cl,1.764850463526756,mazzatorta and swiss +CCOP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OCC,1.7666484723467257,swiss +CCN(C(=O)SCc1ccc(cc1)Cl)CC,1.777777917639287,swiss +CCOP(=S)(Oc1cc(C)nc(n1)C(C)C)OCC,1.7843888703871507,swiss +ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,1.7915317697343827,mazzatorta and swiss +ClCC1CN(C(=O)C1Cl)c1cccc(c1)C(F)(F)F,1.7953374882517812,mazzatorta +Fc1ccc(cc1)[Si](c1ccc(cc1)F)(Cn1cncn1)C,1.7998885403761986,swiss +ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,1.8008758853766749,mazzatorta +N#Cc1c(Cl)c(C#N)c(c(c1Cl)Cl)Cl,1.8226655444942998,mazzatorta +N#Cc1c(Cl)cccc1Cl,1.8376148441427587,mazzatorta +CC(Oc1cc(c(cc1Cl)Cl)n1nc(oc1=O)C(C)(C)C)C,1.8391414693200085,mazzatorta +CCNc1nc(NC(C)C)nc(n1)Cl,1.8424525744221922,swiss +CON(C(=O)Nc1ccc(cc1)Br)C,1.8429362996173768,swiss +N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,1.8596176754405975,mazzatorta +ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,1.8615709799939963,mazzatorta +COP(=O)(NC(=O)C)SC,1.8648991662342804,mazzatorta and swiss +CCCN(C(=O)n1cncc1)CCOc1c(Cl)cc(cc1Cl)Cl,1.8683813356508745,mazzatorta +CSc1ccc(cc1C)OP(=S)(OC)OC,1.8705356902176227,mazzatorta +COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,1.8994226972104038,swiss +N#Cc1c(Cl)c(C#N)c(c(c1Cl)Cl)Cl,1.9062282185012702,swiss +O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,1.910554141726166,swiss +Clc1cc(ccc1Oc1ccc(c(c1)C(=O)NS(=O)(=O)C)[N+](=O)[O-])C(F)(F)F,1.9432475594325607,swiss +CCCSP(=O)(SCCC)OCC,1.953075825769965,mazzatorta +Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,1.9637704559137055,swiss +CCOC(=O)c1cn2nc(cc2nc1C)OP(=S)(OCC)OCC,1.9700888950875557,mazzatorta and swiss +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Br)Br,1.971062922929322,swiss +CC(Oc1cc(c(cc1Cl)Cl)n1nc(oc1=O)C(C)(C)C)C,1.9817989775037086,swiss +COP(=O)(OC=C(Cl)Cl)OC,1.98263271644647,mazzatorta and swiss +CCCSP(=O)(SCCC)OCC,1.997013659143215,swiss +CON(C(=O)Nc1ccc(c(c1)Cl)Cl)C,1.9984393473574271,mazzatorta +c1scc(n1)c1nc2c([nH]1)cccc2,2.002701007590486,mazzatorta +CCOC(=O)C(Oc1ccc(cc1)Oc1cnc2c(n1)ccc(c2)Cl)C,2.0032782361791086,mazzatorta +CCCCOC(=O)C(Oc1ccc(cc1)Oc1ccc(cn1)C(F)(F)F)C,2.0049706079134992,swiss +COP(=O)(OC=C(Cl)Cl)OC,2.011905013931404,mazzatorta +C=CC1(C)OC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl,2.0251706470154947,swiss +COP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OC,2.031451678702528,mazzatorta +COCN(c1c(CC)cccc1CC)C(=O)CCl,2.033046777818888,swiss +COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,2.0701418925937802,swiss +CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,2.086832774211438,mazzatorta +COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,2.089882745849489,mazzatorta +CCCCOC(=O)C(Oc1ccc(cc1)Oc1ccc(cn1)C(F)(F)F)C,2.106487904221944,mazzatorta +Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,2.1221450940410347,swiss +O=C(N(C)C)Nc1ccc(c(c1)Cl)Cl,2.137081876700897,swiss +O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,2.1471176164896835,mazzatorta and swiss +COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,2.149323138641405,mazzatorta +Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,2.1583590177532916,mazzatorta +CCOP(=S)(SCSC(C)(C)C)OCC,2.159009899464302,mazzatorta +CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,2.1625567716931373,swiss +COC(=O)N(C(=O)N1COC2(C(=N1)c1ccc(cc1C2)Cl)C(=O)OC)c1ccc(cc1)OC(F)(F)F,2.1661965219008414,mazzatorta and swiss +OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,2.170831362029858,mazzatorta +COc1sc(=O)n(n1)CSP(=S)(OC)OC,2.179450456028215,mazzatorta +COP(=O)(SC)N,2.1953767727402536,mazzatorta +Fc1ccc(cc1)[Si](c1ccc(cc1)F)(Cn1cncn1)C,2.197821700327843,mazzatorta +Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,2.2190565454174904,swiss +OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,2.22634517935135,swiss +COc1sc(=O)n(n1)CSP(=S)(OC)OC,2.244956432365732,swiss +COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,2.2462254954430265,swiss +CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,2.251501873386263,mazzatorta and swiss +CC(Cc1ccc(cc1)C(C)(C)C)CN1CC(C)OC(C1)C,2.2516879065790425,mazzatorta and swiss +CCOP(=S)(SCn1c(=O)oc2c1ccc(c2)Cl)OCC,2.2645927430857946,mazzatorta +COc1sc(=O)n(n1)CSP(=S)(OC)OC,2.276363751548955,mazzatorta +CNC(=O)Oc1cccc2c1OC(C2)(C)C,2.276700340769475,swiss +CCOP(=S)(OCC)SCSP(=S)(OCC)OCC,2.2838380008263486,mazzatorta +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Br)Br,2.3055264253989747,mazzatorta and swiss +CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,2.305860052357369,mazzatorta +CCOP(=S)(Oc1cc(C)nc(n1)C(C)C)OCC,2.3072764272991098,mazzatorta +CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,2.3291791803052426,mazzatorta +CCOP(=S)(OCC)SCSP(=S)(OCC)OCC,2.3295964190287313,swiss +CCOP(=S)(Oc1ncn(n1)c1ccccc1)OCC,2.3820356307764845,mazzatorta and swiss +COc1sc(=O)n(n1)CSP(=S)(OC)OC,2.3835770037343176,mazzatorta +OC(=O)COc1nc(Cl)c(cc1Cl)Cl,2.4090356263428507,swiss +CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,2.4732602189766197,swiss +COP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OC,2.5085682643170304,swiss +CCOP(=S)(Oc1nc(Cl)c(cc1Cl)Cl)OCC,2.544789572224195,mazzatorta and swiss +COc1sc(=O)n(n1)CSP(=S)(OC)OC,2.5773937472129362,mazzatorta +CSc1ccc(cc1C)OP(=S)(OC)OC,2.587220359179703,mazzatorta and swiss +CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,2.656532322530736,mazzatorta +COc1sc(=O)n(n1)CSP(=S)(OC)OC,2.6742996570840143,swiss +CCCSP(=O)(SCCC)OCC,2.685458670870119,swiss +COP(=O)(SC)N,2.687209307853558,mazzatorta and swiss +COP(=S)(Oc1ccc(c(c1)C)[N+](=O)[O-])OC,2.780101260955034,mazzatorta +CSc1ccc(cc1C)OP(=S)(OC)OC,2.7913436994927525,mazzatorta +CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,2.819186224245603,swiss +COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,2.9451954997790453,mazzatorta +CNC(=O)CSP(=S)(OC)OC,2.962374330085281,mazzatorta +CCOP(=O)(SC(CC)C)SC(CC)C,3.034055505212864,mazzatorta +CNC(=O)CSP(=S)(OC)OC,3.0592942995229597,swiss +CCOP(=O)(SC(CC)C)SC(CC)C,3.085640973700997,swiss +CSc1ccc(cc1C)OP(=S)(OC)OC,3.14352487453815,swiss +COP(=O)(SC)N,3.1496172391186557,mazzatorta +CCSCSP(=S)(OCC)OCC,3.211485180279816,mazzatorta and swiss +CCOP(=S)(SCSC(C)(C)C)OCC,3.4600461583436033,swiss +OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,3.558351987474352,mazzatorta and swiss +CCOP(=S)(SCSC(C)(C)C)OCC,3.68189490795996,mazzatorta +CCOP(=S)(SCSC(C)(C)C)OCC,3.7610761540075845,mazzatorta +N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,3.8624876468778706,mazzatorta +N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,3.8697627521147706,swiss diff --git a/data/training-cv-0.csv b/data/training-cv-0.csv deleted file mode 100644 index da27aae..0000000 --- a/data/training-cv-0.csv +++ /dev/null @@ -1,631 +0,0 @@ -SMILES,LOAEL_measured_median,LOAEL_predicted,Confidence -ClC12C3C4(C(C1(Cl)Cl)(C1(C2(C3(Cl)C(C41Cl)(Cl)Cl)Cl)Cl)Cl)Cl,1.9566e-05,0.0014217916687719956,0.625 -ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C=C2)(Cl)Cl)Cl,2.7404e-05,0.0013706062569694513,1 -N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,0.00013611,0.012636565759511635,1 -Clc1ccc2c(c1)[n+]([O-])nc(n2)n1cncc1,0.00020191,0.06134084368719656,1 -OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,0.00027647,0.010309205899374637,1 -CCOP(=S)(SCSC(C)(C)C)OCC,0.00027736000000000004,0.003281002651967321,1 -ClC1C2OC2C2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,0.00028896749999999995,0.0007308189563592216,1 -ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,0.000295345,0.00038858131599372076,1 -CCSCCSP(=S)(OCC)OCC,0.00036443,0.002479025960626162,1 -CNC(=O)ON=CC(SC)(C)C,0.00052559,0.049415575509958495,1 -COC(=O)C(Oc1ccc(cc1)Oc1ccc(cc1Cl)Cl)C,0.00058619,0.17281993122474898,1 -CCSCSP(=S)(OCC)OCC,0.00061449,0.0007932432885978219,1 -OC1CCCCCc2cc(O)cc(c2C(=O)OC(CCC1)C)O,0.00062036,0.46407564001892226,1 -ClC1C=CC2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,0.000843785,0.00023424144160233445,1 -c1ccc(cc1)[Sn](c1ccccc1)c1ccccc1,0.00085711,0.6089345620132928,1 -CCOP(=O)(SC(CC)C)SC(CC)C,0.000872805,0.00431082033723489,1 -CCS(=O)CCSP(=O)(OC)OC,0.00089328,0.0013951101578154512,1 -ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,0.000939545,0.0003282100247208412,1 -COP(=S)(Oc1ccc(cc1)N(=O)=O)OC,0.00094982,0.00808983327405174,1 -Clc1c(Cl)c(Cl)c(c(c1Cl)Cl)Cl,0.0010183,0.1496193233273976,1 -CNC(=O)CSP(=S)(OC)OC,0.0010905,0.009515127939063844,1 -COP(=O)(SCCS(=O)(=O)CC)OC,0.0011438,0.0012475492886251487,1 -COC1CC(OC2C(C)C=CC=C3COC4C3(O)C(C=C(C4O)C)C(=O)OC3CC(CC=C2C)OC2(C3)C=CC(C(O2)C(CC)C)C)OC(C1OC1CC(OC)C(C(O1)C)O)C.COC1CC(OC(C1OC1CC(OC)C(C(O1)C)O)C)OC1C(C)C=CC=C2COC3C2(O)C(C=C(C3O)C)C(=O)OC2CC(CC=C1C)OC1(C2)C=CC(C(O1)C(C)C)C,0.0011546,0.004752783530343022,1 -ClC12C(Cl)(Cl)C3(C4(C1(Cl)C1(C2(Cl)C3(C4(C1(Cl)Cl)Cl)Cl)Cl)Cl)Cl,0.0012831,6.240058509303313e-05,0.625 -CCOP(=O)(N1CCSC1=O)SC(CC)C,0.0013411,0.014243323330903914,1 -O=C1CCCC(=O)C1C(=O)c1ccc(cc1[N+](=O)[O-])S(=O)(=O)C,0.0014146,0.013126761603553482,1 -CCOP(=S)(Oc1ccccc1C(=O)OC(C)C)NC(C)C,0.0014476,0.1593163042859205,1 -CCSCCSP(=O)(OC)OC,0.0015199,0.000892447632027103,1 -CCOc1cc(nc(n1)CC)OP(=S)(OC)OC,0.0015396,0.008735989693461715,1 -COC(=O)C=C(OP(=O)(OC)OC)C,0.0015615,0.0023812687906506857,1 -COC(=O)/C=C(/OP(=O)(OC)OC)\C,0.0015615,0.0023812687906506857,1 -OC(=O)c1ccccc1.CCC(C1OC2(C=CC1C)OC1CC=C(C)C(OC3CC(OC)C(C(O3)C)OC3CC(OC)C(C(O3)C)NC)C(C)C=CC=C3C4(C(C(=O)OC(C2)C1)C=C(C)C(C4OC3)O)O)C,0.001735715,0.001650952778414861,1 -C1CCC(CC1)[Sn](n1ncnc1)(C1CCCCC1)C1CCCCC1,0.001811,0.01739584228447045,1 -ClC1C2(Cl)C3C4C5C1(Cl)C(C2(Cl)C5C3C1C4O1)(Cl)Cl,0.0018377,0.00017075213457395932,1 -CNC(=O)CCSCCSP(=O)(OC)OC,0.0018793,0.0033386848364462857,1 -COC1CC(OC2C(C)C=CC=C3COC4C3(O)C(C=C(C4O)C)C(=O)OC3CC(CC=C2C)OC2(C3)C=CC(C(O2)C(C)C)C)OC(C1OC1CC(OC)C(C(O1)C)NC(=O)C)C,0.00194425,0.0019671733161550544,1 -N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)[C@H](C(C)C)Nc1ccc(cc1Cl)C(F)(F)F,0.0019884,0.007439697832488631,1 -CNC(=O)C=C(OP(=O)(OC)OC)C,0.0020165,0.006177137380174914,1 -COP(=O)(SC)N,0.0020549,0.33146588454802073,1 -COP(=S)(Oc1ccc(cc1)[N+](=O)[O-])OC,0.0020896,0.009162643797104183,1 -CSc1ccc(cc1C)OP(=S)(OC)OC,0.00210185,0.014159970849698342,1 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+CCCCC(COC(=O)c1ccccc1C(=O)OCC(CCCC)CC)CC,0.29065370490612896,mazzatorta +CCOc1nc(nc(n1)NC)NC(=O)NS(=O)(=O)c1ccccc1C(=O)OC,0.29099307039793554,swiss +CCCOC(=O)NCCCN(C)C.Cl,0.29475225982109493,swiss +N#Cc1c[nH]cc1c1cccc(c1Cl)Cl,0.2957207588154646,swiss +CCOC(=O)OC1=C(C(=O)NC21CCC(CC2)OC)c1cc(C)ccc1C,0.29576366269121235,swiss +CCOC(=O)OC1=C(C(=O)NC21CCC(CC2)OC)c1cc(C)ccc1C,0.29576366269121235,swiss +O=c1[nH]c2CCCc2c(=O)n1C1CCCCC1,0.2978797847408851,swiss +ClC(Cl)Cl,0.29876887487274084,mazzatorta +COC(=O)C(NC(=O)C(CC(=O)O)N)Cc1ccccc1,0.3014732021092552,mazzatorta +OC(=O)C(Oc1cccc(c1)Cl)C,0.30236968685337956,mazzatorta +CN1N(C)C(CC1c1ccccc1)c1ccccc1,0.3050966001866355,mazzatorta +CCCCc1c(C)nc(nc1OS(=O)(=O)N(C)C)NCC,0.30713546264273256,swiss +CC1OC(C)CN(C1)C1CCCCCCCCCCC1,0.3159000388714622,swiss +Clc1c(Cl)c([N+](=O)[O-])c(c(c1Cl)Cl)Cl,0.32109939155527684,swiss +ClCC[N+](C)(C)C.[Cl-],0.32378208897472166,swiss +Clc1c(Cl)c([N+](=O)[O-])c(c(c1Cl)Cl)Cl,0.3241850105488455,mazzatorta +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1F)Oc1ccc(cc1Cl)C(F)(F)F,0.3273757636235727,swiss +Clc1cc(ccc1Oc1ccc(c(c1)C(=O)[O-])[N+](=O)[O-])C(F)(F)F.[Na+],0.3286512527182731,mazzatorta +CC(C#C)(CC)O,0.32910504647978983,mazzatorta +COCN(c1c(CC)cccc1CC)C(=O)CCl,0.3306180266382941,swiss +Cc1cc(N)c(cc1C)C,0.3316606839304537,mazzatorta +CSC(=O)c1cccc2c1snn2,0.33646868040700656,swiss +CCSC(=O)N(CC(C)C)CC(C)C,0.33720440522271905,mazzatorta +N#Cc1c[nH]cc1c1cccc2c1OC(O2)(F)F,0.34169281115210776,swiss +CC1N(C(=O)NC2CCCCC2)C(=O)SC1c1ccc(cc1)Cl,0.34350890569841486,mazzatorta +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1F)Oc1ccc(cc1Cl)C(F)(F)F,0.3506456666356317,swiss +N#Cc1c[nH]cc1c1cccc2c1OC(O2)(F)F,0.35338065059536966,mazzatorta +CO/C=C(\c1ccccc1COc1cccc(n1)C(F)(F)F)/C(=O)OC,0.3555318676947022,swiss +CC(Oc1ccccn1)COc1ccc(cc1)Oc1ccccc1,0.3608722506294798,mazzatorta +CCOC(=O)CC(C(=O)OCC)SP(=S)(OC)OC,0.363652544891169,swiss +CC(Oc1ccccn1)COc1ccc(cc1)Oc1ccccc1,0.3649286265357719,swiss +CC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)C,0.36853593732630685,mazzatorta and swiss +NCCNc1cccc2c1cccc2,0.37248052866231735,mazzatorta +Clc1c(Cl)c([N+](=O)[O-])c(c(c1Cl)Cl)Cl,0.3734030332190271,mazzatorta +O=C1N(OCC1(C)C)Cc1ccccc1Cl,0.3796661101133605,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,0.3954067884187704,swiss +CCCCNC(=O)n1c(NC(=O)OC)nc2c1cccc2,0.4021732296220636,mazzatorta +N#Cc1ccc(cc1)C/C(=N/NC(=O)Nc1ccc(cc1)OC(F)(F)F)/c1ccc(cc1)C(F)(F)F,0.40346887816639565,swiss +CO/N=C(\c1ccccc1Oc1ncnc(c1F)Oc1ccccc1Cl)/C1=NOCCO1,0.4039750104769187,swiss +CCC(n1c(=O)[nH]c(c(c1=O)Br)C)C,0.40399702961561096,swiss +COC(=O)Nc1nc2c([nH]1)cccc2,0.40639276248013095,mazzatorta +O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.41106754767681414,mazzatorta and swiss +O=C(Nc1ccnc(c1)Cl)Nc1ccccc1,0.4254131908061819,swiss +CC(=CC1C(C1(C)C)C(=O)OCc1coc(c1)Cc1ccccc1)C,0.43257365514742613,mazzatorta +Cc1nnc(c(=O)n1N)c1ccccc1,0.4330678000342624,swiss +CCN(C(=O)C(Oc1cccc2c1cccc2)C)CC,0.4335389375093063,mazzatorta and swiss +O=CNC(C(Cl)(Cl)Cl)N1CCN(CC1)C(C(Cl)(Cl)Cl)NC=O,0.4343292396214822,swiss +Cc1cc(C)cc(c1)C(=O)N(C(C)(C)C)NC(=O)c1ccc2c(c1C)CCCO2,0.4343882750979413,swiss +CCCCC(c1ccc(cc1)Cl)(Cn1cncn1)C#N,0.4352511081869822,swiss +COc1ccc(cc1)C(C(Cl)(Cl)Cl)c1ccc(cc1)OC,0.4417235394286475,mazzatorta +N=C(NC(=N)N)NCCc1ccccc1,0.4489771869661306,mazzatorta +OC(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.44975941346983,mazzatorta +Fc1ccc(cc1)C(=O)CCCN1CCN(CC1)c1ccccn1,0.45437130300715145,mazzatorta +OC(=O)C(Cl)(Cl)C,0.4562671121127415,mazzatorta +CCC(=O)Nc1ccc(c(c1)Cl)Cl,0.4577972175659718,mazzatorta +Clc1cc(ccc1Oc1ccc(c(c1)C(=O)O)[N+](=O)[O-])C(F)(F)F,0.4613885686555222,mazzatorta +OC(=O)COc1ccc(cc1Cl)Cl,0.46940334082401963,swiss +CN(C1C(=O)C(=C(O)N)C(=O)C2(C1CC1C(=C(O)c3c(C1(C)O)cccc3O)C2=O)O)C,0.47171335503894035,mazzatorta +OC(=O)CSc1cc(/N=c/2\sc(=O)n3n2CCCC3)c(cc1Cl)F,0.4769562541646596,swiss +ClC(SN1C(=O)C2C(C1=O)CC=CC2)(Cl)Cl,0.4779733071886505,mazzatorta and swiss +CCCCCCC(c1cc(cc(c1OC(=O)/C=C\C)[N+](=O)[O-])[N+](=O)[O-])C,0.4795033665674557,swiss +ClC(SN1C(=O)C2C(C1=O)CC=CC2)(Cl)Cl,0.48674243602099465,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,0.49291359170948634,swiss +OC(=O)CCl,0.4982973080098996,mazzatorta +C#CCC1=C(C)C(CC1=O)OC(=O)C1C(C1(C)C)C=C(C)C,0.49996946581612567,mazzatorta +ClCC(=O)N(c1c(C)cccc1C)Cn1cccn1,0.5041366495179556,swiss +COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1C(=O)OC,0.5057897307706736,swiss +CNC(=O)Oc1cc(C)cc(c1C)C,0.5137821153320272,mazzatorta +NC1CC(N=C(C(=O)O)N)C(OC1OC1C(O)C(O)C(C(C1O)O)O)C,0.5145909236774223,swiss +CCCCCCCCSC(=O)Oc1cc(Cl)nnc1c1ccccc1,0.5178413045887236,swiss +Fc1ccc(c(c1)F)NC(=O)c1cccnc1Oc1cccc(c1)C(F)(F)F,0.5180846348916255,swiss +CCOC(=O)c1ccccc1S(=O)(=O)NC(=O)Nc1nc(Cl)cc(n1)OC,0.5209576284133699,mazzatorta +CN(C(=S)[S-])C.CN(C(=S)[S-])C.CN(C(=S)[S-])C.[Fe+3],0.5227050622218726,swiss +CNC(=O)Oc1cccc2c1cccc2,0.5240819585605319,swiss +COC(=O)c1ccccc1S(=O)(=O)NC(=O)Nc1nc(OC(F)F)cc(n1)OC(F)F,0.5244304978725236,mazzatorta +CNC(=O)Oc1cccc2c1cccc2,0.5255214895917043,mazzatorta +CCCCOCCOCCOCc1cc2OCOc2cc1CCC,0.5294866078313349,mazzatorta and swiss +Cc1ccc(cc1)N(S(=O)(=O)N(C)C)SC(Cl)(Cl)F,0.5406527536056628,swiss +C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,0.5446525636058117,mazzatorta +CCCC1COC(O1)(Cn1cncn1)c1ccc(cc1Cl)Cl,0.5520361697732551,mazzatorta and swiss +OC(=O)COc1ccc(cc1Cl)Cl,0.5520671344078197,swiss +COc1cc(OC)nc(n1)NC(=O)NS(=O)(=O)c1ncccc1S(=O)(=O)CC,0.5521445556544645,swiss +CC(CC(c1sccc1NC(=O)c1cn(nc1C(F)(F)F)C)C)C,0.5555960442504486,swiss +CCSC(CC1CC(=O)C(=C(NOCC=CCl)CC)C(=O)C1)C,0.5561895988786021,mazzatorta +ClC=C(c1cc(Cl)c(cc1Cl)Cl)OP(=O)(OC)OC,0.5634398293782596,mazzatorta +OCC(CCl)O,0.5663901566762818,swiss +Nc1cnn(c(=O)c1Cl)c1ccccc1,0.5674957011389724,swiss +CC(Oc1cccc(c1)NC(=O)c1ccccc1C(F)(F)F)C,0.5701024413128524,mazzatorta and swiss +Fc1ccc(cc1)Oc1ccnc2c1c(Cl)cc(c2)Cl,0.585645126657133,swiss +Cc1ccc(cc1)N(S(=O)(=O)N(C)C)SC(Cl)(Cl)F,0.5864152712755908,swiss +CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.5921334169692242,swiss +C=CC1(C)OC(=O)N(C1=O)c1cc(Cl)cc(c1)Cl,0.5938005763366871,mazzatorta +[S]C(=S)NCCNC(=S)S[Mn],0.597669823868334,swiss +CC1OC(C)OC(OC(O1)C)C,0.6025814576486522,swiss +CCc1cc(C)cc(c1c1c(OC(=O)C(C)(C)C)n2n(c1=O)CCOCC2)CC,0.6026162443427237,swiss +OC(=O)c1nc(Cl)c(c(c1Cl)N)Cl,0.6046910839214119,mazzatorta +C=CCC1=C(C)C(CC1=O)OC(=O)C1C(C1(C)C)C=C(C)C,0.605530807652566,mazzatorta +CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.6055658314011255,swiss +Fc1ccc2c(c1)n(c(c2Br)C)S(=O)(=O)c1ncn(n1)S(=O)(=O)N(C)C,0.6194450778457865,swiss +CCC(n1c(=O)[nH]c(c(c1=O)Br)C)C,0.6209484238156326,mazzatorta +CNC(=O)Oc1ccccc1OC(C)C,0.6216747904039281,mazzatorta +CCSC(CC1CC(=O)C(=C(NOCC=CCl)CC)C(=O)C1)C,0.6216929651431873,mazzatorta +Cl/C=C/CO/N=C(\C1=C(O)CC(CC1=O)CC(SCC)C)/CC,0.6216929651431873,swiss +CC1=C(C)S(=O)(=O)CCS1(=O)=O,0.6238064131157152,swiss +Clc1c(ccc(c1N)[N+](=O)[O-])Oc1ccccc1,0.6303018610501191,swiss +CCNC(=O)C(OC(=O)Nc1ccccc1)C,0.6330356796840821,swiss +CC(CC(c1sccc1NC(=O)c1cn(nc1C(F)(F)F)C)C)C,0.6365196444403993,swiss +O=CNC(C(Cl)(Cl)Cl)N1CCN(CC1)C(C(Cl)(Cl)Cl)NC=O,0.6384421385594775,mazzatorta and swiss +CNC(=O)Oc1ccccc1OC(C)C,0.6394065864347511,swiss +Nc1cnn(c(=O)c1Cl)c1ccccc1,0.6466801557572905,swiss +Nc1ccc2c(c1)nc1c(c2)ccc(c1)N,0.6485515770908835,mazzatorta +COCC(=O)N(c1c(C)cccc1C)C(C(=O)OC)C,0.6502369560120504,mazzatorta +CC(Nc1nc(NC(C)C)nc(n1)Cl)C,0.653607901682671,swiss +ClCC(=O)N(c1c(C)cccc1CC)COC(C)C,0.6536665888044787,swiss +CC(c1ccc(cc1)O)(c1ccc(cc1)O)C,0.6595162253742823,mazzatorta +O=C1CCCC(=O)C1C(=O)c1ccc(cc1Cl)S(=O)(=O)C,0.6595558851598816,swiss +CC(Nc1nc(NC(C)C)nc(n1)Cl)C,0.66220142273775,mazzatorta +ClCC(=O)N(c1c(CC)cccc1CC)CNC(=O)C,0.6765828786495807,mazzatorta +COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1CCC(F)(F)F,0.6781324193692118,swiss +CO/C=C(\c1ccccc1Oc1ncnc(c1)Oc1ccccc1C#N)/C(=O)OC,0.6897954111019945,swiss +O=C(NS(=O)(=O)c1ccccc1C(=O)OC1COC1)Nc1nc(C)cc(n1)C,0.6898804626531899,mazzatorta +Cn1nc(c(c1)C(=O)Nc1ccccc1C1CC1C1CC1)C(F)F,0.6942188487450178,swiss +C=Cc1ccccc1,0.6954448498795582,mazzatorta +Nc1ccc(c(c1)N)O,0.6959836634792338,mazzatorta +c1scc(n1)c1nc2c([nH]1)cccc2,0.7016710119265048,mazzatorta and swiss +O=c1nc(N(C)C)n(c(=O)n1C1CCCCC1)C,0.7029620906206402,mazzatorta +OC(=O)C(Cl)(Cl)C,0.7054456011689532,mazzatorta +OC1CN(C(=O)N1c1nnc(s1)C(C)(C)C)C,0.7098095160296959,mazzatorta +CCCCCCC(c1cc(cc(c1OC(=O)/C=C\C)[N+](=O)[O-])[N+](=O)[O-])C,0.7103218791002677,swiss +N#CCNC(=O)c1cnccc1C(F)(F)F,0.7157046517694734,swiss +COP(=O)(NC(=O)C)SC,0.7187847673886838,mazzatorta +COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1CCC(F)(F)F,0.7200594271604471,mazzatorta +O=N(=O)c1ccc(c(c1)N(=O)=O)C,0.7289022077868816,mazzatorta +CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.7320074096344176,mazzatorta +CN1CN(C)CSC1=S,0.7331335587788191,mazzatorta +c1ccc(cc1)Nc1ccccc1,0.7370982373458622,mazzatorta +CC1=NNC(=O)N(C1)/N=C/c1cccnc1,0.742513420926119,swiss +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.7443416268287478,mazzatorta and swiss +COCC(=O)N(c1c(C)cccc1C)N1CCOC1=O,0.7455486046549836,mazzatorta +CBr,0.7470014198431081,swiss +N#Cc1cc(C)c(c(c1)C(=O)NC)NC(=O)c1cc(nn1c1ncccc1Cl)Br,0.7471227074362395,swiss +Clc1ccc(cc1)CCC(C(C)(C)C)(Cn1cncn1)O,0.747924056200199,swiss +CCCCCCCCSC(=O)Oc1cc(Cl)nnc1c1ccccc1,0.7492385520203118,mazzatorta +CCCCCCCCCC[N+](CCCCCCCCCC)(C)C.[Cl-],0.7526160071097443,swiss +CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.7542899704547085,swiss +CCCCCCCCc1cc(N(=O)=O)c(c(c1)N(=O)=O)OC(=O)C=CC,0.7554012986711326,mazzatorta +CCCCCCC(c1cc(cc(c1OC(=O)/C=C/C)[N+](=O)[O-])[N+](=O)[O-])C,0.7554012986711326,swiss +CCN(C(=O)C(Oc1cccc2c1cccc2)C)CC,0.7562924133382095,swiss +CNC(=O)N(c1nnc(s1)C(C)(C)C)C,0.7564658981679381,mazzatorta +COC(=O)c1ccc(cc1C1=NC(C(=O)N1)(C)C(C)C)C,0.7609258617641064,mazzatorta +Clc1cc(Cl)c(cc1n1nc(n(c1=O)C(F)F)C)NS(=O)(=O)C,0.7618534935689649,mazzatorta and swiss +Cc1ccc(c2c1cccc2)C,0.7623808700538108,swiss +CCC(Nc1c(cc(cc1[N+](=O)[O-])C(C)(C)C)[N+](=O)[O-])C,0.7713430418910647,mazzatorta +O=C1N(c2cc(Cl)cc(c2)Cl)C(=O)C2(C1(C)C2)C,0.7722932179393288,swiss +CNc1cnn(c(=O)c1Cl)c1cccc(c1)C(F)(F)F,0.7727047496082895,mazzatorta +ClC(SN1C(=O)c2c(C1=O)cccc2)(Cl)Cl,0.7731424297112764,mazzatorta +CC(N1C(=O)c2ccccc2NS1(=O)=O)C,0.7786640204661717,mazzatorta +C#CC(NC(=O)c1cc(Cl)cc(c1)Cl)(C)C,0.780075086811172,swiss +Cc1cccc2c1n1cnnc1s2,0.7856330784033956,mazzatorta +CC(OC(=O)Nc1ccccc1)C,0.7909678031634652,swiss +CCOC(=O)CN(c1c(CC)cccc1CC)C(=O)CCl,0.794903952421516,mazzatorta +Cc1nc(Nc2ccccc2)nc(c1)C1CC1,0.8012879425395989,mazzatorta and swiss +COP(=S)(Oc1cc(Cl)c(cc1Cl)Cl)OC,0.8082696066371436,mazzatorta +OC(=O)C(Oc1ccc(cc1Cl)Cl)C,0.8088844776196935,swiss +COC(=O)NC(=S)Nc1ccccc1NC(=S)NC(=O)OC,0.8102570997584302,swiss 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+c1coc(c1)c1nc2c([nH]1)cccc2,0.9035418882825467,swiss +CNC(=O)Oc1cc(C)c(c(c1)C)SC,0.9056337016638656,swiss +CNC(=O)ON=C(SC)C,0.9090369234042683,mazzatorta +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,0.9204208330298691,mazzatorta and swiss +CCCCc1c(=O)nc([nH]c1C)NCC,0.9228138459141033,mazzatorta +COc1cc(ccc1OC)C(=CC(=O)N1CCOCC1)c1ccc(cc1)Cl,0.9231048061339493,mazzatorta +CC(N(c1c(cc(cc1N(=O)=O)C(F)(F)F)N(=O)=O)C(C)C)C,0.9233595563296582,mazzatorta +N#CC(c1ccccc1)(Cn1cncn1)CCc1ccc(cc1)Cl,0.925329811075993,mazzatorta +CCNC(=O)NC(=O)/C(=N\OC)/C#N,0.9259885539910551,swiss +Nc1ccc(cc1)S(=O)(=O)Nc1nc(C)cc(n1)C,0.926061809364747,mazzatorta +CCN(c1c(cc(cc1[N+](=O)[O-])C(F)(F)F)[N+](=O)[O-])Cc1c(F)cccc1Cl,0.926061809364747,mazzatorta +CNC(=O)O/N=C(\SC)/C,0.9267583523880014,swiss +COc1c(OC)cc(c(c1OC)C(=O)c1c(OC)ncc(c1C)Cl)C,0.9308130748480904,swiss +CCNc1nc(NC(C)C)nc(n1)Cl,0.9358790941703782,mazzatorta +CBr,0.9360664758369617,swiss 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+Clc1cc(cnc1CNC(=O)c1c(Cl)cccc1Cl)C(F)(F)F,1.0855457707987952,swiss +CCOC(=O)NCCOc1ccc(cc1)Oc1ccccc1,1.086355661467734,swiss +NC(=N)NCCCCCCCCNC(=N)N,1.0914077611524307,swiss +C=CCC1=C(C)C(CC1=O)OC(=O)C1C(C1(C)C)C=C(C)C,1.0914292030286552,mazzatorta +CC(c1cc(ccc1O)C(c1ccc(c(c1)C(C)C)O)(C)C)C,1.0968340181230818,mazzatorta +CCSC(=O)N1CCCCCC1,1.1019882612024983,mazzatorta +N#C/N=C(/N(Cc1ccc(nc1)Cl)C)\C,1.1046327112266383,swiss +COP(=O)(C(C(Cl)(Cl)Cl)O)OC,1.1096404686828705,mazzatorta +CNC(=O)Oc1cccc2c1cccc2,1.1105470212498103,mazzatorta +Clc1ccc(cc1)CN(C(=O)Nc1ccccc1)C1CCCC1,1.1190378586994414,mazzatorta +Cn1cc(c2cccc(c2)C(F)(F)F)c(=O)c(c1)c1ccccc1,1.1196724036355312,mazzatorta +Cn1nc(c(c1)C(=O)Nc1cccc2c1C1CCC2C1=C(Cl)Cl)C(F)F,1.1201303088977508,swiss +CCCSc1ccc2c(c1)[nH]c(n2)NC(=O)OC,1.1227611514363303,mazzatorta +ClCCOc1ccccc1S(=O)(=O)NC(=O)Nc1nc(C)nc(n1)OC,1.1269178314119968,swiss +OC(C(C)(C)C)C(n1cncn1)Oc1ccc(cc1)c1ccccc1,1.130222820427892,mazzatorta and swiss +O=C(N(C)C)Nc1cccc(c1)C(F)(F)F,1.1310207139253916,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(c(c1)Cl)OC(C(OC(F)(F)F)F)(F)F,1.1362846674663016,mazzatorta +CCCC1COC(O1)(Cn1cncn1)c1ccc(cc1Cl)Cl,1.13636788955756,mazzatorta +CO/N=C(\c1ccccc1CO/N=C(/c1cccc(c1)C(F)(F)F)\C)/C(=O)OC,1.138298355243177,swiss +CCN1CCN(CC1)c1cc2c(cc1F)c(=O)c(cn2C1CC1)C(=O)O,1.1405974821668619,mazzatorta +C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,1.1432529892239978,swiss +COc1cc(OC)n2c(n1)nc(n2)S(=O)(=O)Nc1c(Cl)ccc(c1Cl)C,1.1443173332617613,swiss +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)(C)C,1.1454085716982032,mazzatorta +CO/N=C(/c1ccccc1COc1cc(C)ccc1C)\C(=O)NC,1.1520080543334354,swiss +CSc1nnc(c(=O)n1N)C(C)(C)C,1.1549081642369436,mazzatorta +COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1Cl,1.155665748672126,mazzatorta +CCO/N=C(/C1=C(O)CC(CC1=O)c1c(C)cc(cc1C)C)\CC,1.1560388165278184,swiss +CCOc1ccc(cc1)C(COCc1cccc(c1)Oc1ccccc1)(C)C,1.1607797146866263,mazzatorta +C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,1.16083631708535,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,1.1660755529404674,mazzatorta +CCOc1ccc(cc1)C(COCc1cccc(c1)Oc1ccccc1)(C)C,1.1692125122974353,swiss +CSc1nnc(c(=O)n1N)C(C)(C)C,1.1726371897097654,swiss +[O-][N+](=O)NC1=NCCN1Cc1ccc(nc1)Cl,1.1772175409292547,mazzatorta +Clc1ccccc1c1nnc(nn1)c1ccccc1Cl,1.1806206011079126,mazzatorta +CCCCN(SN(C(=O)Oc1cccc2c1OC(C2)(C)C)C)CCCC,1.182467683028378,mazzatorta and swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,1.1831094015116075,mazzatorta +CC1N(C(=O)NC2CCCCC2)C(=O)SC1c1ccc(cc1)Cl,1.1840107302272953,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(c(c1F)Cl)F,1.1865991709300072,swiss +O=C(N(C)C)Nc1ccc(c(c1)Cl)n1nc(oc1=O)C(C)(C)C,1.187507779053734,swiss +O=C(N(C)C)Nc1cccc(c1)C(F)(F)F,1.189774204881748,mazzatorta +COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccsc1C(=O)OC,1.1902114152572187,mazzatorta +OC(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,1.1921944677293754,swiss +CC(C1(C)N=C(NC1=O)c1nc2ccccc2cc1C(=O)O)C,1.192201228281402,mazzatorta +O=C(NS(=O)(=O)c1c(C)cccc1C(=O)O)Nc1nc(OCC(F)(F)F)nc(n1)N(C)C,1.1940711751182476,swiss +ClC(=CC1C(C1(C)C)C(=O)OCc1cccc(c1)Oc1ccccc1)Cl,1.1945535170115598,mazzatorta and swiss +CCOP(=S)(SCn1c(=O)oc2c1ccc(c2)Cl)OCC,1.1964063352286556,swiss +ClCC(=O)N(c1c(C)cccc1C)Cn1cccn1,1.198143706994319,swiss +COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,1.2004664736572608,mazzatorta +Clc1cc(F)c(cc1C(=O)NS(=O)(=O)N(C(C)C)C)n1c(=O)cc(n(c1=O)C)C(F)(F)F,1.2027809477033562,swiss +[O-][N+](=O)c1cc(cc(c1)[N+](=O)[O-])[N+](=O)[O-],1.2044119266519366,mazzatorta +CN(C(=S)SSC(=S)N(C)C)C,1.2049058977607419,mazzatorta +CCNc1nc(SC)nc(n1)NC(C)(C)C,1.2065658549221894,mazzatorta +COC(=O)c1cccc(c1S(=O)(=O)NC(=O)Nc1nc(OCC(F)(F)F)nc(n1)N(C)C)C,1.2066217621691122,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(c(c1)Cl)OC(C(OC(F)(F)F)F)(F)F,1.2068664410308745,swiss +CNc1cnn(c(=O)c1Cl)c1cccc(c1)C(F)(F)F,1.2093982050371732,mazzatorta +CNC(=O)ON=C(SC)C,1.2100810083310742,mazzatorta 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swiss +CCOC(=O)C(c1ccc(cc1)Cl)(c1ccc(cc1)Cl)O,1.2473140302104877,mazzatorta +CNC(=O)Oc1cccc(c1)N=CN(C)C,1.2479822997809782,mazzatorta +N#CC(c1c(Cl)ccc(c1Cl)n1ncc(=O)[nH]c1=O)c1ccc(cc1)Cl,1.248543826213122,mazzatorta +O=C(C(Oc1ccc(cc1)Oc1cnc2c(n1)ccc(c2)Cl)C)OCCON=C(C)C,1.2493219317505007,swiss +COc1nc(nc(n1)C)NC(=O)[N-]S(=O)(=O)c1cc(I)ccc1C(=O)OC.[Na+],1.2508978152709322,swiss +COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1Cl,1.2525804230510118,swiss +CCOC(=O)COC(=O)c1cc(ccc1[N+](=O)[O-])Oc1ccc(cc1Cl)C(F)(F)F,1.253093479525902,mazzatorta +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)C1C(C1(C)C)(C)C,1.2544333907376006,mazzatorta +O=C(c1ccc(cc1S(=O)(=O)C)C(F)(F)F)c1cnoc1C1CC1,1.2544489954403404,mazzatorta and swiss +COCN(c1c(CC)cccc1CC)C(=O)CCl,1.2548939653344497,mazzatorta +CCOc1ccc2c(c1)C(=CC(N2)(C)C)C,1.2578957329638771,mazzatorta and swiss +CNC1=C(c2cccc(c2)C(F)(F)F)C(=O)C(O1)c1ccccc1,1.2580215857840296,swiss +C#CCC1=C(C)C(CC1=O)OC(=O)C1C(C1(C)C)C=C(C)C,1.2602062139183252,swiss +C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,1.265896559378767,mazzatorta +OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,1.2677429840395533,mazzatorta +C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,1.2716218656999372,swiss +NC(=NCCCCCCCCNCCCCCCCCN=C(N)N)N,1.2721660588211523,mazzatorta +O=C(N/C(=N\OCC1CC1)/c1c(F)c(F)ccc1C(F)(F)F)Cc1ccccc1,1.2728492955894033,swiss +CC(C(c1ccc(cc1)Cl)(Cn1ncnc1)O)C1CC1,1.2735988378970775,swiss +Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,1.2770454101830115,swiss +O=C1N(c2cc(Cl)cc(c2)Cl)C(=O)C2(C1(C)C2)C,1.2774401112445704,mazzatorta +O=N(=O)c1ccc(c(c1)N)C,1.279187677997853,mazzatorta +N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,1.2798406965940432,swiss +CN(C(=O)Oc1nc(nc(c1C)C)N(C)C)C,1.280188271227964,mazzatorta +CNC(=O)Oc1cccc(c1)/N=C/N(C)C,1.2841971462426878,swiss +COCN(c1c(CC)cccc1CC)C(=O)CCl,1.2848577466043891,mazzatorta and swiss +Cc1ccc(cc1)N(S(=O)(=O)N(C)C)SC(Cl)(Cl)F,1.285376897128516,mazzatorta +CNC(=O)Oc1cc(C)c(c(c1)C)C,1.286098038116031,mazzatorta +COP(=O)(C(C(Cl)(Cl)Cl)O)OC,1.286820378541783,swiss +Clc1ccc(cc1)CCC(C(C)(C)C)(Cn1cncn1)O,1.2868960417984983,mazzatorta +CN(C(=O)Oc1nc(nc(c1C)C)N(C)C)C,1.287190413172402,mazzatorta and swiss +CCNc1nc(c(s1)C(=O)NC(c1cccs1)C#N)CC,1.2908912329029336,swiss +C#CCN1C(=O)COc2c1cc(c(c2)F)N1C(=O)C2=C(C1=O)CCCC2,1.2941362877160807,swiss +CCSC(CC1CC(=O)C(C(=O)C1)C(=NOCC)CCC)C,1.2961242226802938,mazzatorta +C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,1.2969236723843232,mazzatorta and swiss +ClCC1CN(C(=O)C1Cl)c1cccc(c1)C(F)(F)F,1.2984147471171126,swiss +COC(=O)Nc1nc2c([nH]1)cc(cc2)Sc1ccccc1,1.3000842641666306,mazzatorta +CN(C(=S)SSC(=S)N(C)C)C,1.3018124301338776,mazzatorta and swiss +O=C(c1cccc(c1C(=O)NC(CS(=O)(=O)C)(C)C)I)Nc1ccc(cc1C)C(C(F)(F)F)(C(F)(F)F)F,1.3025526926274749,swiss +CCC(C(=O)NCc1ccccc1)Oc1ccc(c(c1)C(F)(F)F)F,1.302657301989526,swiss +Cc1cccc(c1O)C,1.3087946941073485,mazzatorta +CON(C(=O)Nc1ccc(cc1)Br)C,1.3165566912097522,mazzatorta +S=C1NCCCN1,1.3169619721135797,swiss +CN(C(=S)SSC(=S)N(C)C)C,1.3202996191280358,mazzatorta +[O-][Br](=O)=O,1.321545358579602,mazzatorta +C[n+]1ccc(cc1)c1cc[n+](cc1)C,1.322183249787766,swiss +CC1=C(C)S(=O)(=O)CCS1(=O)=O,1.322776417451734,mazzatorta +CCCN(C(=O)SCC)CCC,1.3229499580527195,mazzatorta +Nc1ccc(cc1)Cl,1.3276065528189747,mazzatorta +CCNc1nc(NC(C)C)nc(n1)Cl,1.3338191028424158,swiss +CCOC(=O)Cn1c(=O)sc2c1c(Cl)ccc2,1.3372138456888123,mazzatorta +ClCC=CCl,1.3376388791974851,mazzatorta +C=CCC1=C(C)C(CC1=O)OC(=O)C1C(C1(C)C)C=C(C)C,1.3407114619354228,mazzatorta +O=C1OC(C(=O)N1Nc1ccccc1)(C)c1ccc(cc1)Oc1ccccc1,1.3428771906003532,swiss +CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,1.344900000029291,mazzatorta +[O-][As](=O)([O-])[O-],1.3468840068344332,mazzatorta +O=C1OC(C(=O)N1Nc1ccccc1)(C)c1ccc(cc1)Oc1ccccc1,1.3480148945878891,mazzatorta +[S-]C(=S)NCCNC(=S)[S-].[Zn+2],1.3505969838253369,swiss +CC(=CC1C(C1(C)C)C(=O)OCc1coc(c1)Cc1ccccc1)C,1.3533904493269542,swiss +N#CC(c1ccc(c(c1)Oc1ccccc1)F)OC(=O)C1C(C1(C)C)C=C(Cl)Cl,1.354479485094126,swiss +CC(Cc1ccc(cc1)C(C)(C)C)CN1CCCCC1,1.3639034458432429,swiss +OC1(Cn2ncnc2)C(CCC1(C)C)Cc1ccc(cc1)Cl,1.3650393299393615,swiss +S=C1NCCCN1,1.3661781926983205,swiss +ClC(C(SN1C(=O)C2C(C1=O)CC=CC2)(Cl)Cl)Cl,1.3668144329404721,mazzatorta +O=C(CC(C)(C)C)OC1=C(C(=O)OC21CCCC2)c1c(C)cc(cc1C)C,1.367370643858509,swiss +C#CCOS(=O)OC1CCCCC1Oc1ccc(cc1)C(C)(C)C,1.3685663781727047,swiss +CC(N(C(=O)SCC(=C(Cl)Cl)Cl)C(C)C)C,1.369877357140688,swiss +Clc1ccc(c(c1)Cl)C1(OCCO1)Cn1cncn1,1.370111513834565,swiss +CN(C(=S)S[Zn]SC(=S)N(C)C)C,1.371508895032877,swiss +Fc1ccc(c(c1)c1ccc(c(c1)Cl)Cl)NC(=O)c1cn(nc1C(F)F)C,1.3766679947209994,swiss +CCOC(=O)C(Oc1ccc(cc1)Oc1cnc2c(n1)ccc(c2)Cl)C,1.3811468505131965,swiss +CNC(=O)Oc1cc(C)c(c(c1)C)SC,1.3842918746267547,mazzatorta +CCC(=O)Nc1ccc(c(c1)Cl)Cl,1.384376054498974,swiss +CCN(C(=O)C(=C(OP(=O)(OC)OC)C)Cl)CC,1.3867609023838614,mazzatorta +CC(N(C(=O)SCC(=C(Cl)Cl)Cl)C(C)C)C,1.386909067965439,mazzatorta +OC1(Cn2ncnc2)C(CCC1(C)C)Cc1ccc(cc1)Cl,1.3876506550542822,swiss +CCCOC/C(=N\c1ccc(cc1C(F)(F)F)Cl)/n1cncc1,1.3926307718235211,swiss +CCCN(c1c(cc(cc1[N+](=O)[O-])S(=O)(=O)N)[N+](=O)[O-])CCC,1.3934036908207148,swiss +CNC(=S)S,1.3967148564744987,swiss +O=C(CC(C)(C)C)OC1=C(C(=O)OC21CCCC2)c1c(C)cc(cc1C)C,1.398504958795708,swiss +O=C(NC(=O)c1c(F)cccc1F)Nc1cc(Cl)c(cc1Cl)OC(C(C(F)(F)F)F)(F)F,1.3989182722159768,swiss +CCOP(=S)(Oc1nn(c(n1)Cl)C(C)C)OCC,1.3996588688637353,mazzatorta +OC(=O)COc1cc(Cl)c(cc1Cl)Cl,1.4073569873961393,mazzatorta +CC(C(c1cncnc1)(c1ccc(cc1)OC(F)(F)F)O)C,1.411773125869789,mazzatorta +OC(=O)C(Oc1ccc(cc1Cl)Cl)C,1.4169486617141684,mazzatorta +CCOC(=O)CSc1nc(nn1C(=O)N(C)C)C(C)(C)C,1.4183119750954112,swiss +Clc1ccc(cc1)c1ccccc1NC(=O)c1cccnc1Cl,1.4216129602890024,swiss +CC(OP(=S)(OC(C)C)SCCNS(=O)(=O)c1ccccc1)C,1.4232556456725831,mazzatorta +Clc1c(O)c(Cl)c(c(c1Cl)Cl)Cl,1.425436324184721,mazzatorta 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+S=C1NCCN1,2.647547338246984,mazzatorta +CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,2.656532322530736,mazzatorta +COc1sc(=O)n(n1)CSP(=S)(OC)OC,2.6742996570840143,swiss +COP(=S)(Oc1ccc(cc1)[N+](=O)[O-])OC,2.67993684040502,swiss +CCCSP(=O)(SCCC)OCC,2.685458670870119,swiss +COP(=O)(SC)N,2.687209307853558,mazzatorta and swiss +CNC(=O)C=C(OP(=O)(OC)OC)C,2.6954017736563625,mazzatorta +N#CC(c1cccc(c1)Oc1ccccc1)OC(=O)[C@H](C(C)C)Nc1ccc(cc1Cl)C(F)(F)F,2.7014962455330385,swiss +COP(=S)(Oc1ccc(cc1)[N+](=O)[O-])OC,2.7213378390900194,swiss +CNC(=O)CCSCCSP(=O)(OC)OC,2.7260038862407203,mazzatorta +ClC1C2(Cl)C3C4C5C1(Cl)C(C2(Cl)C5C3C1C4O1)(Cl)Cl,2.7357253846684157,mazzatorta +C1CCC(CC1)[Sn](n1ncnc1)(C1CCCCC1)C1CCCCC1,2.7420815496859414,swiss +COP(=S)(Oc1ccc(c(c1)C)[N+](=O)[O-])OC,2.780101260955034,mazzatorta +CSc1ccc(cc1C)OP(=S)(OC)OC,2.7913436994927525,mazzatorta +COC(=O)C=C(OP(=O)(OC)OC)C,2.8064580114337825,mazzatorta +COC(=O)/C=C(/OP(=O)(OC)OC)\C,2.8064580114337825,swiss +CCOc1cc(nc(n1)CC)OP(=S)(OC)OC,2.8125920975774465,mazzatorta +CCSCCSP(=O)(OC)OC,2.8181849850005256,swiss +CCOP(=O)(Oc1ccc(c(c1)C)SC)NC(C)C,2.819186224245603,swiss +CCOP(=S)(Oc1ccccc1C(=O)OC(C)C)NC(C)C,2.839351425563838,mazzatorta +CCOP(=S)(Oc1ccc(cc1)N(=O)=O)OCC,2.84103473961659,mazzatorta +O=C1CCCC(=O)C1C(=O)c1ccc(cc1[N+](=O)[O-])S(=O)(=O)C,2.849366346253572,swiss +CCOP(=O)(N1CCSC1=O)SC(CC)C,2.8725388374884626,swiss +ClC12C(Cl)(Cl)C3(C4(C1(Cl)C1(C2(Cl)C3(C4(C1(Cl)Cl)Cl)Cl)Cl)Cl)Cl,2.8917394950235265,mazzatorta +ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,2.9135689793436312,mazzatorta +O=C1CCCC(=O)C1C(=O)c1ccc(cc1Cl)S(=O)(=O)C,2.9148164919472213,swiss +COC1CC(OC2C(C)C=CC=C3COC4C3(O)C(C=C(C4O)C)C(=O)OC3CC(CC=C2C)OC2(C3)C=CC(C(O2)C(CC)C)C)OC(C1OC1CC(OC)C(C(O1)C)O)C.COC1CC(OC(C1OC1CC(OC)C(C(O1)C)O)C)OC1C(C)C=CC=C2COC3C2(O)C(C=C(C3O)C)C(=O)OC2CC(CC=C1C)OC1(C2)C=CC(C(O1)C(C)C)C,2.9375684468373877,swiss +COP(=O)(SCCS(=O)(=O)CC)OC,2.9416499077893463,swiss +COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,2.9451954997790453,mazzatorta +COC1CC(OC2C(C)C=CC=C3COC4C3(O)C(C=C(C4O)C)C(=O)OC3CC(CC=C2C)OC2(C3)C=CC(C(O2)C(C)C)C)OC(C1OC1CC(OC)C(C(O1)C)NC(=O)C)C,2.9542859410591324,mazzatorta +CNC(=O)CSP(=S)(OC)OC,2.962374330085281,mazzatorta +Clc1c(Cl)c(Cl)c(c(c1Cl)Cl)Cl,2.992124256232415,mazzatorta +ClC1C=CC2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,2.9922948856352196,mazzatorta +OC(=O)c1ccccc1.CCC(C1OC2(C=CC1C)OC1CC=C(C)C(OC3CC(OC)C(C(O3)C)OC3CC(OC)C(C(O3)C)NC)C(C)C=CC=C3C4(C(C(=O)OC(C2)C1)C=C(C)C(C4OC3)O)O)C,3.003562759689381,swiss +COP(=S)(Oc1ccc(cc1)N(=O)=O)OC,3.0223586898833017,mazzatorta +CCOP(=O)(SC(CC)C)SC(CC)C,3.034055505212864,mazzatorta +CCS(=O)CCSP(=O)(OC)OC,3.0490123894936456,swiss +CNC(=O)CSP(=S)(OC)OC,3.0592942995229597,swiss +c1ccc(cc1)[Sn](c1ccccc1)c1ccccc1,3.066963437904847,mazzatorta +CCOP(=O)(SC(CC)C)SC(CC)C,3.085640973700997,swiss +CCSCCSP(=S)(OCC)OCC,3.095966448265008,mazzatorta +CSc1ccc(cc1C)OP(=S)(OC)OC,3.14352487453815,swiss +COP(=O)(SC)N,3.1496172391186557,mazzatorta +ClC(C(c1ccc(cc1)Cl)c1ccc(cc1)Cl)(Cl)Cl,3.151656905172596,mazzatorta +ClC1C=CC2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,3.1741391562322345,mazzatorta +ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,3.181187083707245,mazzatorta +ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,3.1828843613117277,mazzatorta +OC1CCCCCc2cc(O)cc(c2C(=O)OC(CCC1)C)O,3.207356212694859,mazzatorta +CCSCSP(=S)(OCC)OCC,3.211485180279816,mazzatorta and swiss +ClC1CC2C(C1Cl)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl,3.2146060934806684,mazzatorta +COC(=O)C(Oc1ccc(cc1)Oc1ccc(cc1Cl)Cl)C,3.2319615946008633,swiss +CNC(=O)ON=CC(SC)(C)C,3.279352906359489,mazzatorta +ClC1C2OC2C2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,3.2892735261180657,mazzatorta +CCSCCSP(=S)(OCC)OCC,3.438385878959815,mazzatorta +CCOP(=S)(SCSC(C)(C)C)OCC,3.4600461583436033,swiss +ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,3.483914356975709,mazzatorta +OC(=O)C(Oc1ccc(cc1)Oc1ncc(cc1Cl)C(F)(F)F)C,3.558351987474352,mazzatorta and swiss +ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,3.580821061438942,mazzatorta +CCOP(=S)(SCSC(C)(C)C)OCC,3.68189490795996,mazzatorta +Clc1ccc2c(c1)[n+]([O-])nc(n2)n1cncc1,3.6948421712135144,swiss +CCOP(=S)(SCSC(C)(C)C)OCC,3.7610761540075845,mazzatorta +CCSCCSP(=S)(OCC)OCC,3.8363318462173313,mazzatorta +N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,3.8624876468778706,mazzatorta +N#Cc1nn(c(c1S(=O)C(F)(F)F)N)c1c(Cl)cc(cc1Cl)C(F)(F)F,3.8697627521147706,swiss +ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C1C2O1)(Cl)Cl)Cl,3.8818676000790955,mazzatorta +ClC1C2OC2C2C1C1(Cl)C(=C(C2(C1(Cl)Cl)Cl)Cl)Cl,4.192363513110009,mazzatorta +ClC1=C(Cl)C2(C(C1(Cl)C1C2C2CC1C=C2)(Cl)Cl)Cl,4.562186041152654,mazzatorta +ClC12C3C4(C(C1(Cl)Cl)(C1(C2(C3(Cl)C(C41Cl)(Cl)Cl)Cl)Cl)Cl)Cl,4.7084979508104,mazzatorta diff --git a/figures/crossvalidation.pdf b/figures/crossvalidation.pdf Binary files differindex 5c31c0c..360bb18 100644 --- a/figures/crossvalidation.pdf +++ b/figures/crossvalidation.pdf diff --git a/figures/dataset-variability.pdf b/figures/dataset-variability.pdf Binary files differindex aad1c21..7db8068 100644 --- a/figures/dataset-variability.pdf +++ b/figures/dataset-variability.pdf diff --git a/figures/test-correlation.pdf b/figures/test-correlation.pdf Binary files differindex d3f49a7..b75158e 100644 --- a/figures/test-correlation.pdf +++ b/figures/test-correlation.pdf diff --git a/figures/test-prediction.pdf b/figures/test-prediction.pdf Binary files differindex 2083f96..7e64b08 100644 --- a/figures/test-prediction.pdf +++ b/figures/test-prediction.pdf @@ -52,10 +52,10 @@ Federal Office* dataset). Elena: do you have a reference and the name of the department? ```{r echo=F} -m = read.csv("data/mazzatorta.csv",header=T) -s = read.csv("data/swiss.csv",header=T) -t = read.csv("data/test.csv",header=T) -c = read.csv("data/training.csv",header=T) +m = read.csv("data/mazzatorta_log10.csv",header=T) +s = read.csv("data/swiss_log10.csv",header=T) +t = read.csv("data/test_log10.csv",header=T) +c = read.csv("data/training_log10.csv",header=T) ``` `r length(unique(t$SMILES))` compounds are common in both datasets and we use @@ -261,7 +261,7 @@ generic and can be employed with different kinds of features. [@fig:ches-mapper-pc] shows an embedding that is based on physico-chemical (PC) descriptors. -{#fig:ches-mapper-pc} +{#fig:ches-mapper-pc} Martin: please explain light colors at bottom of histograms @@ -293,7 +293,7 @@ functional groups with a frequency > 25 are depicted, the complete table for all functional groups can be found in the data directory of the supplemental material (`data/functional-groups.csv`). -{#fig:fg} +{#fig:fg} ### Experimental variability versus prediction uncertainty @@ -317,10 +317,10 @@ m.dupnr <- length(m.dupsmi) s.dupnr <- length(s.dupsmi) c.dupnr <- length(c.dupsmi) -m.dup$sd <- ave(-log10(m.dup$LOAEL),m.dup$SMILES,FUN=sd) -s.dup$sd <- ave(-log10(s.dup$LOAEL),s.dup$SMILES,FUN=sd) -c.dup$sd <- ave(-log10(c.dup$LOAEL),c.dup$SMILES,FUN=sd) -t$sd <- ave(-log10(t$LOAEL),t$SMILES,FUN=sd) +m.dup$sd <- ave(m.dup$LOAEL,m.dup$SMILES,FUN=sd) +s.dup$sd <- ave(s.dup$LOAEL,s.dup$SMILES,FUN=sd) +c.dup$sd <- ave(c.dup$LOAEL,c.dup$SMILES,FUN=sd) +t$sd <- ave(t$LOAEL,t$SMILES,FUN=sd) p = t.test(m.dup$sd,s.dup$sd)$p.value ``` @@ -340,7 +340,7 @@ a statistically significant difference with a p-value (t-test) of `r round(p,2)` The combined test set has a mean standard deviation of `r round(mean(c.dup$sd),2)` log10 units. -{#fig:intra} +{#fig:intra} ##### Inter dataset variability @@ -350,10 +350,10 @@ log10 units. ```{r echo=F} data <- read.csv("data/median-correlation.csv",header=T) -cor <- cor.test(-log(data$mazzatorta),-log(data$swiss)) +cor <- cor.test(data$mazzatorta,data$swiss) median.p <- cor$p.value -median.r.square <- round(rsquare(-log(data$mazzatorta),-log(data$swiss)),2) -median.rmse <- round(rmse(-log(data$mazzatorta),-log(data$swiss)),2) +median.r.square <- round(rsquare(data$mazzatorta,data$swiss),2) +median.rmse <- round(rmse(data$mazzatorta,data$swiss),2) ``` [@fig:corr] depicts the correlation between LOAEL values from both datasets. As @@ -368,8 +368,8 @@ correlation between the experimental data in both datasets with r\^2: ```{r echo=F} training = read.csv("data/training-test-predictions.csv",header=T) -training.r_square = round(rsquare(-log(training$LOAEL_measured_median),-log(training$LOAEL_predicted)),2) -training.rmse = round(rmse(-log(training$LOAEL_measured_median),-log(training$LOAEL_predicted)),2) +training.r_square = round(rsquare(training$LOAEL_measured_median,training$LOAEL_predicted),2) +training.rmse = round(rmse(training$LOAEL_measured_median,training$LOAEL_predicted),2) misclassifications = read.csv("data/misclassifications.csv",header=T) incorrect_predictions = length(misclassifications$SMILES) correct_predictions = length(training$SMILES)-incorrect_predictions @@ -390,7 +390,7 @@ Experimental data and 95\% prediction intervals did not overlap in `r incorrect_ [@fig:comp] shows a comparison of predicted with experimental values: -{#fig:comp} +{#fig:comp} Correlation analysis was performed between individual predictions and the median of experimental data. All correlations are statistically highly @@ -405,18 +405,18 @@ Prediction vs. Test median | `r training.r_square` | `r training.rms : Comparison of model predictions with experimental variability. {#tbl:common-pred} -{#fig:corr} +{#fig:corr} ```{r echo=F} -t0 = read.csv("data/training-cv-0.csv",header=T) -cv.t0.r_square = round(rsquare(-log(t0$LOAEL_measured_median),-log(t0$LOAEL_predicted)),2) -cv.t0.rmse = round(rmse(-log(t0$LOAEL_measured_median),-log(t0$LOAEL_predicted)),2) -t1 = read.csv("data/training-cv-1.csv",header=T) -cv.t1.r_square = round(rsquare(-log(t1$LOAEL_measured_median),-log(t1$LOAEL_predicted)),2) -cv.t1.rmse = round(rmse(-log(t1$LOAEL_measured_median),-log(t1$LOAEL_predicted)),2) -t2 = read.csv("data/training-cv-2.csv",header=T) -cv.t2.r_square = round(rsquare(-log(t2$LOAEL_measured_median),-log(t2$LOAEL_predicted)),2) -cv.t2.rmse = round(rmse(-log(t2$LOAEL_measured_median),-log(t2$LOAEL_predicted)),2) +t0 = read.csv("data/training_log10-cv-0.csv",header=T) +cv.t0.r_square = round(rsquare(t0$LOAEL_measured_median,t0$LOAEL_predicted),2) +cv.t0.rmse = round(rmse(t0$LOAEL_measured_median,t0$LOAEL_predicted),2) +t1 = read.csv("data/training_log10-cv-1.csv",header=T) +cv.t1.r_square = round(rsquare(t1$LOAEL_measured_median,t1$LOAEL_predicted),2) +cv.t1.rmse = round(rmse(t1$LOAEL_measured_median,t1$LOAEL_predicted),2) +t2 = read.csv("data/training_log10-cv-2.csv",header=T) +cv.t2.r_square = round(rsquare(t2$LOAEL_measured_median,t2$LOAEL_predicted),2) +cv.t2.rmse = round(rmse(t2$LOAEL_measured_median,t2$LOAEL_predicted),2) ``` For a further assessment of model performance three independent @@ -431,7 +431,7 @@ All correlations of predicted with experimental values are statistically highly : Results from 3 independent 10-fold crossvalidations {#tbl:cv} -{#fig:cv} +{#fig:cv} Discussion ========== @@ -466,8 +466,8 @@ we present a brief analysis of the two most severe mispredictions: ```{r echo=F} smi = "COP(=O)(SC)N" misclass = training[which(training$SMILES==smi),] -med = round(-log10(misclass[,2]),2) -pred = round(-log10(misclass[,3]),2) +med = round(misclass[,2],2) +pred = round(misclass[,3],2) pi = round(log10(misclass[,4]),2) ``` @@ -476,9 +476,9 @@ The compound with the largest deviation of prediction intervals is (amino-methyl ```{r echo=F} smi = "O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl" misclass = training[which(training$SMILES==smi),] -med = round(-log10(misclass[,2]),2) -pred = round(-log10(misclass[,3]),2) -pi = round(log10(misclass[,4]),2) +med = round(misclass[,2],2) +pred = round(misclass[,3],2) +pi = round(misclass[,4],2) ``` The compound with second largest deviation of prediction intervals is @@ -1,9 +1,9 @@ --- author: | - Christoph Helma^1^, David Vorgrimmler^1^, Denis Gebele^1^, Martin Gütlein^2^, Benoit Schilter^3^, Elena Lo Piparo^3^ + Christoph Helma^1^, David Vorgrimmler^1^, Denis Gebele^1^, Martin G<c3><bc>tlein^2^, Benoit Schilter^3^, Elena Lo Piparo^3^ title: | Modeling Chronic Toxicity: A comparison of experimental variability with read across predictions -include-before: ^1^ in silico toxicology gmbh, Basel, Switzerland\newline^2^ Inst. f. Computer Science, Johannes Gutenberg Universität Mainz, Germany\newline^3^ Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland +include-before: ^1^ in silico toxicology gmbh, Basel, Switzerland\newline^2^ Inst. f. Computer Science, Johannes Gutenberg Universit<c3><a4>t Mainz, Germany\newline^3^ Chemical Food Safety Group, Nestl<c3><a9> Research Center, Lausanne, Switzerland keywords: (Q)SAR, read-across, LOAEL date: \today abstract: " " @@ -163,7 +163,7 @@ of connected atoms. MolPrint2D fingerprints are generated dynamically from chemical structures and do not rely on predefined lists of fragments (such as OpenBabel FP3, FP4 or -MACCs fingerprints or lists of toxocophores/toxicophobes). That has the +MACCs fingerprints or lists of toxocophores/toxicophobes). This has the advantage the they may capture substructures of toxicological relevance that are not included in other fingerprints. Preliminary experiments have shown that predictions with MolPrint2D fingerprints are indeed more accurate than @@ -244,7 +244,7 @@ baseline for evaluating prediction performance. ##### Ches-Mapper analysis We applied the visualization tool CheS-Mapper (Chemical Space Mapping and -Visualization in 3D, http://ches-mapper.org, @Gütlein2012) to compare both +Visualization in 3D, http://ches-mapper.org, @G<U+FFFD><U+FFFD>tlein2012) to compare both datasets. CheS-Mapper can be used to analyze the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. It embeds a dataset into 3D space, such that @@ -253,7 +253,7 @@ generic and can be employed with different kinds of features. [@fig:ches-mapper-pc] shows an embedding that is based on physico-chemical (PC) descriptors. -{#fig:ches-mapper-pc} +{#fig:ches-mapper-pc} Martin: please explain light colors at bottom of histograms @@ -283,7 +283,7 @@ functional groups with a frequency > 25 are depicted, the complete table for all functional groups can be found in the data directory of the supplemental material (`data/functional-groups.csv`). -{#fig:fg} +{#fig:fg} ### Experimental variability versus prediction uncertainty @@ -295,6 +295,9 @@ experimental results within individual datasets and between datasets. ##### Intra dataset variability +``` +## Error in split.default(x, g): first argument must be a vector +``` The Mazzatorta dataset has 567 LOAEL values for 445 unique structures, 93 @@ -308,10 +311,10 @@ multiple measurements with a mean standard deviation of Standard deviations of both datasets do not show a statistically significant difference with a p-value (t-test) of 0.21. -The combined test set has a mean standard deviation of 0.33 +The combined test set has a mean standard deviation of NA log10 units. -{#fig:intra} +{#fig:intra} ##### Inter dataset variability @@ -327,7 +330,7 @@ and statistics. Please note that the aggregation of duplicated measurements into a single median value hides a substantial portion of the experimental variability. Correlation analysis shows a significant (p-value < 2.2e-16) correlation between the experimental data in both datasets with r\^2: -0.49, RMSE: 1.41 +0.52, RMSE: 0.59 ### Local QSAR models @@ -337,18 +340,18 @@ In order to compare the performance of in silico read across models with experim variability we are using compounds that occur in both datasets as a test set (375 measurements, 155 compounds). `lazar` read across predictions -were obtained for 155 compounds, 8 +were obtained for 155 compounds, 29 predictions failed, because no similar compounds were found in the training data (i.e. they were not covered by the applicability domain of the training data). -Experimental data and 95\% prediction intervals did not overlap in 13 cases -(9\%), -8 predictions were too high and -5 predictions too low (after -log10 transformation). +Experimental data and 95\% prediction intervals did not overlap in 0 cases +(0\%), +0 predictions were too high and +0 predictions too low (after -log10 transformation). [@fig:comp] shows a comparison of predicted with experimental values: -{#fig:comp} +{#fig:comp} Correlation analysis was performed between individual predictions and the median of experimental data. All correlations are statistically highly @@ -358,12 +361,12 @@ experimental data into a single median value hides experimental variability. Comparison | $r^2$ | RMSE --------------|---------------------------|------------------------- -Mazzatorta vs. Swiss | 0.49 | 1.41 -Prediction vs. Test median | 0.4 | 1.47 +Mazzatorta vs. Swiss | 0.52 | 0.59 +Prediction vs. Test median | 0.33 | 0.67 : Comparison of model predictions with experimental variability. {#tbl:common-pred} -{#fig:corr} +{#fig:corr} @@ -373,13 +376,13 @@ All correlations of predicted with experimental values are statistically highly $r^2$ | RMSE | Nr. predicted -------|------|---------------- -0.4 | 1.8 | 630/671 -0.38 | 1.84 | 631/671 -0.4 | 1.81 | 635/671 +0.38 | 0.8 | 609/671 +0.35 | 0.82 | 611/671 +0.37 | 0.81 | 610/671 : Results from 3 independent 10-fold crossvalidations {#tbl:cv} -{#fig:cv} +{#fig:cv} Discussion ========== @@ -400,7 +403,7 @@ This will become obvious in the next section, where we compare predictions with [@fig:comp], [@fig:corr], [@tbl:common-pred] and the fact that experimental data is covered in -91\% by the `lazar` +100\% by the `lazar` prediction interval shows that `lazar` read across predictions fit well into the experimental variability of LOAEL values. @@ -413,13 +416,13 @@ we present a brief analysis of the two most severe mispredictions: -The compound with the largest deviation of prediction intervals is (amino-methylsulfanyl-phosphoryl)oxymethane (SMILES COP(=O)(SC)N) with an experimental median of 2.69 and a prediction interval of 0.48 +/- 0.59. In this case the prediction is based on two neighbors with very low similarity (0.1 and 0.13). Such cases can be eliminated by raising the similarity threshold for neighbors, but that could come at the cost of a larger number of unpredicted compounds. The graphical user interface shows for each prediction neighbors and similarities for a critical examination which should make the detection of similar cases rather straightforward. +The compound with the largest deviation of prediction intervals is (amino-methylsulfanyl-phosphoryl)oxymethane (SMILES COP(=O)(SC)N) with an experimental median of 2.67 and a prediction interval of 0.43 +/- 0.35. In this case the prediction is based on two neighbors with very low similarity (0.1 and 0.13). Such cases can be eliminated by raising the similarity threshold for neighbors, but that could come at the cost of a larger number of unpredicted compounds. The graphical user interface shows for each prediction neighbors and similarities for a critical examination which should make the detection of similar cases rather straightforward. The compound with second largest deviation of prediction intervals is Endosulfan (SMILES O=S1OCC2C(CO1)C1(C(C2(Cl)C(=C1Cl)Cl)(Cl)Cl)Cl) -with an experimental median of 1.91 and a prediction interval of 3.64 +/- 0.45. In this case the prediction is based on 5 neighbors with similarities between 0.33 and 0.4. All of them are polychlorinated compound, but none of them contains sulfur or is a sulfurous acid ester. Again such problems are easily identified from a visual inspection of neighbors, and we want to stress the importance of inspecting rationales for predictions in the graphical interface before accepting a prediction. +with an experimental median of 1.74 and a prediction interval of 3.44 +/- 1.7. In this case the prediction is based on 5 neighbors with similarities between 0.33 and 0.4. All of them are polychlorinated compound, but none of them contains sulfur or is a sulfurous acid ester. Again such problems are easily identified from a visual inspection of neighbors, and we want to stress the importance of inspecting rationales for predictions in the graphical interface before accepting a prediction. Summary ======= Binary files differdiff --git a/scripts/create-median-correlation.rb b/scripts/create-median-correlation.rb index 9a2f6f5..2b932d0 100755 --- a/scripts/create-median-correlation.rb +++ b/scripts/create-median-correlation.rb @@ -1,13 +1,15 @@ -require_relative 'include.rb' +#!/usr/bin/env ruby +require_relative '../../lazar/lib/lazar' +include OpenTox -old = Dataset.from_csv_file File.join(DATA,"mazzatorta.csv") -new = Dataset.from_csv_file File.join(DATA,"swiss.csv") +old = Dataset.from_csv_file File.join("data","mazzatorta_log10.csv") +new = Dataset.from_csv_file File.join("data","swiss_log10.csv") -common_compound_ids = (old.compound_ids & new.compound_ids).uniq +common_compounds = (old.compounds & new.compounds).uniq data = [] -common_compound_ids.each do |cid| - c = Compound.find cid +puts ["SMILES","mazzatorta","swiss"].join(",") +common_compounds.each do |c| old_values = old.values(c,old.features.first) new_values = new.values(c,new.features.first) identical = old_values & new_values @@ -21,8 +23,4 @@ common_compound_ids.each do |cid| end data.sort!{|a,b| a[1] <=> b[1]} - -CSV.open(File.join(DATA,"median-correlation.csv"),"w+") do |csv| - csv << ["SMILES","mazzatorta","swiss"] - data.each{|r| csv << r} -end +puts data.collect{|r| r.join ","}.join("\n") diff --git a/scripts/create-test.rb b/scripts/create-test.rb index 782f172..151b5e8 100755 --- a/scripts/create-test.rb +++ b/scripts/create-test.rb @@ -2,14 +2,14 @@ require_relative '../../lazar/lib/lazar' include OpenTox -old = Dataset.from_csv_file File.join(DATA,"mazzatorta.csv") -new = Dataset.from_csv_file File.join(DATA,"swiss.csv") +old = Dataset.from_csv_file File.join("data","mazzatorta_log10.csv") +new = Dataset.from_csv_file File.join("data","swiss_log10.csv") -common_compound_ids = (old.compound_ids & new.compound_ids).uniq +common_compounds = (old.compounds & new.compounds).uniq +puts ["SMILES","-log10(LOAEL)","Dataset"].join "," data = [] -common_compound_ids.each do |cid| - c = Compound.find cid +common_compounds.each do |c| old_values = old.values(c,old.features.first) new_values = new.values(c,new.features.first) identical = old_values & new_values @@ -18,19 +18,16 @@ common_compound_ids.each do |cid| new_values -= identical end identical.each do |v| - data << [c.smiles,v,"mazzatorta, swiss"] + data << [c.smiles,v,"mazzatorta and swiss"] if v end old_values.each do |v| - data << [c.smiles,v,"mazzatorta"] + data << [c.smiles,v,"mazzatorta"] if v end new_values.each do |v| - data << [c.smiles,v,"swiss"] + data << [c.smiles,v,"swiss"] if v end end data.sort!{|a,b| a[1] <=> b[1]} -CSV.open(File.join(DATA,"test.csv"),"w+") do |csv| - csv << ["SMILES","LOAEL","Dataset"] - data.each{|r| csv << r} -end +puts data.collect{|r| r.join ","}.join "\n" diff --git a/scripts/create-training.rb b/scripts/create-training.rb index d05bc1c..8fca3f4 100755 --- a/scripts/create-training.rb +++ b/scripts/create-training.rb @@ -2,14 +2,14 @@ require_relative '../../lazar/lib/lazar' include OpenTox -old = Dataset.from_csv_file File.join(DATA,"mazzatorta.csv") -new = Dataset.from_csv_file File.join(DATA,"swiss.csv") +old = Dataset.from_csv_file File.join("data","mazzatorta_log10.csv") +new = Dataset.from_csv_file File.join("data","swiss_log10.csv") -common_compound_ids = (old.compound_ids + new.compound_ids).uniq +common_compounds = (old.compounds + new.compounds).uniq +puts ["SMILES","-log10(LOAEL)","Dataset"].join "," data = [] -common_compound_ids.each do |cid| - c = Compound.find cid +common_compounds.each do |c| old_values = old.values(c,old.features.first) new_values = new.values(c,new.features.first) identical = old_values & new_values @@ -18,19 +18,16 @@ common_compound_ids.each do |cid| new_values -= identical end identical.each do |v| - data << [c.smiles,v,"mazzatorta, swiss"] + data << [c.smiles,v,"mazzatorta and swiss"] if v end old_values.each do |v| - data << [c.smiles,v,"mazzatorta"] + data << [c.smiles,v,"mazzatorta"] if v end new_values.each do |v| - data << [c.smiles,v,"swiss"] + data << [c.smiles,v,"swiss"] if v end end data.sort!{|a,b| a[1] <=> b[1]} -CSV.open(File.join(DATA,"training.csv"),"w+") do |csv| - csv << ["SMILES","LOAEL","Dataset"] - data.each{|r| csv << r} -end +puts data.collect{|r| r.join ","}.join "\n" diff --git a/scripts/crossvalidation-plots.R b/scripts/crossvalidation-plots.R index 2bc259f..7a4f340 100755 --- a/scripts/crossvalidation-plots.R +++ b/scripts/crossvalidation-plots.R @@ -1,15 +1,17 @@ +#!/usr/bin/Rscript + library(ggplot2) library(grid) library(gridExtra) -t0 = read.csv("data/training-cv-0.csv",header=T) -t1 = read.csv("data/training-cv-1.csv",header=T) -t2 = read.csv("data/training-cv-2.csv",header=T) +t0 = read.csv("data/training_log10-cv-0.csv",header=T) +t1 = read.csv("data/training_log10-cv-1.csv",header=T) +t2 = read.csv("data/training_log10-cv-2.csv",header=T) -p0 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=t0,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0) + xlim(-2,4.5) + ylim(-2,4.5) -p1 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=t1,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0) + xlim(-2,4.5) + ylim(-2,4.5) -p2 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=t2,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0) + xlim(-2,4.5) + ylim(-2,4.5) +p0 = qplot(LOAEL_predicted,LOAEL_measured_median,data=t0,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0) + xlim(-2,4.5) + ylim(-2,4.5) +p1 = qplot(LOAEL_predicted,LOAEL_measured_median,data=t1,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0) + xlim(-2,4.5) + ylim(-2,4.5) +p2 = qplot(LOAEL_predicted,LOAEL_measured_median,data=t2,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0) + xlim(-2,4.5) + ylim(-2,4.5) -pdf('figure/crossvalidation.pdf') +pdf('figures/crossvalidation.pdf') grid.arrange(p0,p1,p2,ncol=2) dev.off() diff --git a/scripts/crossvalidation.rb b/scripts/crossvalidation.rb index 79aeb83..9657af1 100755 --- a/scripts/crossvalidation.rb +++ b/scripts/crossvalidation.rb @@ -1,25 +1,34 @@ -require_relative 'include.rb' - +#!/usr/bin/env ruby +require_relative '../../lazar/lib/lazar' +include OpenTox +require 'yaml' name = File.basename ARGV[0], ".csv" -file = File.join DATA,ARGV[0] +file = File.join "data",ARGV[0] dataset = Dataset.from_csv_file file -model = Model::LazarRegression.create(dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_fingerprint_regression") +model = Model::LazarRegression.create(training_dataset: dataset)#, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_fingerprint_regression") #model = Model::LazarRegression.create(dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression") #model = Model::LazarRegression.create(dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_weighted_average") -csv_file = File.join(DATA,ARGV[0].sub(/.csv/,"-cv-#{ARGV[1]}.csv")) -id_file = File.join(DATA,ARGV[0].sub(/.csv/,"-cv-#{ARGV[1]}.id")) -cv = RegressionCrossValidation.create model +csv_file = File.join("data",ARGV[0].sub(/.csv/,"-cv-#{ARGV[1]}.csv")) +id_file = File.join("data",ARGV[0].sub(/.csv/,"-cv-#{ARGV[1]}.id")) +cv = Validation::RegressionCrossValidation.create model File.open(id_file,"w+"){|f| f.puts cv.id} - +#cv = Validation::RegressionCrossValidation.first +p cv.id data = [] -cv.predictions.each do |p| - smi = Compound.find(p[0]).smiles - data << [smi,p[1].median,p[2],p[3]] +cv.predictions.each do |cid,p| + smi = Compound.find(cid).smiles + if p["prediction_interval"] + data << [smi,p["value"],p["measurements"].median,p["prediction_interval"][0],p["prediction_interval"][1]] + else + data << [smi,p["value"],p["measurements"].median,nil,nil] + end end data.sort!{|a,b| a[1] <=> b[1]} CSV.open(csv_file,"w+") do |csv| - csv << ["SMILES","LOAEL_measured_median","LOAEL_predicted","Confidence"] + csv << ["SMILES","LOAEL_measured_median","LOAEL_predicted","Prediction_interval_low","Prediction_interval_high"] data.each{|r| csv << r} end +=begin +=end diff --git a/scripts/dataset-variability.R b/scripts/dataset-variability.R index 775fd03..65234a4 100755 --- a/scripts/dataset-variability.R +++ b/scripts/dataset-variability.R @@ -1,17 +1,18 @@ +#!/usr/bin/Rscript library(ggplot2) library(grid) library(gridExtra) -m = read.csv("data/mazzatorta.csv",header=T) -s = read.csv("data/swiss.csv",header=T) +m = read.csv("data/mazzatorta_log10.csv",header=T) +s = read.csv("data/swiss_log10.csv",header=T) m.dupsmi = unique(m$SMILES[duplicated(m$SMILES)]) s.dupsmi = unique(s$SMILES[duplicated(s$SMILES)]) m.dup = m[m$SMILES %in% m.dupsmi,] s.dup = s[s$SMILES %in% s.dupsmi,] -m.dup$LOAEL= -log10(m.dup$LOAEL) -s.dup$LOAEL= -log10(s.dup$LOAEL) +#m.dup$LOAEL= -log10(m.dup$LOAEL) +#s.dup$LOAEL= -log10(s.dup$LOAEL) m.dup$SMILES <- reorder(m.dup$SMILES,m.dup$LOAEL) s.dup$SMILES <- reorder(s.dup$SMILES,s.dup$LOAEL) @@ -22,12 +23,12 @@ p2 <- ggplot(s.dup, aes(SMILES,LOAEL),ymin = min(LOAEL), ymax=max(LOAEL)) + ylab #grid.arrange(p1,p2,ncol=1) #dev.off() -data <- read.csv("data/test.csv",header=T) -data$LOAEL = -log(data$LOAEL) +data <- read.csv("data/test_log10.csv",header=T) +#data$LOAEL = -log(data$LOAEL) data$SMILES <- reorder(data$SMILES,data$LOAEL) img = ggplot(data,aes(SMILES,LOAEL,ymin = min(LOAEL), ymax=max(LOAEL),color=Dataset)) + geom_point() img <- img + ylab('-log(LOAEL mg/kg_bw/day)') + xlab('Compound') + theme(axis.text.x = element_blank()) + theme(legend.title=element_blank()) img = img + scale_fill_discrete(breaks=c("Mazzatorta", "Both", "Swiss Federal Office")) img = img -ggsave(file='figure/dataset-variability.pdf', plot=img, width=12,height=8) +ggsave(file='figures/dataset-variability.pdf', plot=img, width=12,height=8) diff --git a/scripts/functional-groups-images.rb b/scripts/functional-groups-images.rb new file mode 100755 index 0000000..346bd5f --- /dev/null +++ b/scripts/functional-groups-images.rb @@ -0,0 +1,26 @@ +#!/usr/bin/env ruby +# www.smartsview.de/smartsview/auto/<image-format>/<visualization-modus>/<legend-option>/<SMARTS> +# syntax rules +# image-format: pdf, png or svg +# visualization modus: 1 or 2 (1 = Complete Visualization, 2 = Element Symbols) +# legend option: both, none, static, dynamic +# SMARTS: All special symbols used in SMARTS can be used except '#', which has to be escaped with %23 + +require 'uri' +SERVICE_URI = "http://www.smartsview.de/smartsview/auto/pdf/2/both/" + + +inFile = File.join("data","functional-groups-smarts.csv") +hash = {} +File.readlines(inFile).each do |line| + columns = line.split(",",2) + group = columns[0].strip + smarts = columns[1].sub(/^'|'$/,"").strip.sub(/^'|'$/,"").strip + hash[group] = smarts +end + +hash.each do |group,smarts| + `wget '#{URI.escape(SERVICE_URI+smarts)}' -O "#{File.join("figures",group+'.pdf')}"` +end + + diff --git a/scripts/functional-groups.R b/scripts/functional-groups.R index 01f2043..6121073 100755 --- a/scripts/functional-groups.R +++ b/scripts/functional-groups.R @@ -1,3 +1,4 @@ +#!/usr/bin/Rscript library("ggplot2") data <- read.csv("data/functional-groups-reduced4R.csv",header=F) @@ -6,4 +7,4 @@ names(data) = c("V1","V2","Dataset") data$V1 <- reorder(data$V1,-data$V2) ggplot(data,aes(x=V1,y=V2,fill=Dataset)) + geom_bar(stat="identity", position=position_dodge()) + xlab("") + ylab("") + coord_flip() -ggsave("figure/functional-groups.pdf") +ggsave("figures/functional-groups.pdf") diff --git a/scripts/misclassifications.rb b/scripts/misclassifications.rb index 171077c..d285868 100755 --- a/scripts/misclassifications.rb +++ b/scripts/misclassifications.rb @@ -1,4 +1,6 @@ -require_relative 'include.rb' +#!/usr/bin/env ruby +require_relative '../../lazar/lib/lazar' +include OpenTox class Range def intersection(other) @@ -9,21 +11,21 @@ class Range end experimental = {} -CSV.foreach(File.join(DATA,"test.csv")) do |row| +CSV.foreach(File.join("data","test_log10.csv")) do |row| experimental[row[0]] ||= [] experimental[row[0]] << row[1].to_f end predictions = {} -CSV.foreach(File.join(DATA,"training-test-predictions.csv"),:headers => true) do |row| - predictions[row[0]] = [-Math.log10(row[2].to_f),Math.log10(row[3].to_f).abs] +CSV.foreach(File.join("data","training-test-predictions.csv"),:headers => true) do |row| + predictions[row[0]] = [row[2].to_f,row[3].to_f.abs] end outside_experimental_values = 0 within_experimental_values = 0 out = [] predictions.each do |smi,pred| - exp = experimental[smi].collect{|e| -Math.log10(e)}.uniq + exp = experimental[smi].uniq # https://en.wikipedia.org/wiki/Prediction_interval min = pred[0]-1.96*pred[1] max = pred[0]+1.96*pred[1] diff --git a/scripts/test-correlation-plot.R b/scripts/test-correlation-plot.R index 74a2739..ef69058 100755 --- a/scripts/test-correlation-plot.R +++ b/scripts/test-correlation-plot.R @@ -1,15 +1,17 @@ +#!/usr/bin/Rscript + library(ggplot2) library(grid) library(gridExtra) experimental <- read.csv("data/median-correlation.csv",header=T) -p1 = qplot(-log10(mazzatorta),-log10(swiss),data=experimental,xlab="-log10(LOAEL Mazzatorta median)",ylab="-log10(LOAEL Swiss Federal Office median)",main="Experimental data") + geom_point() + geom_abline(intercept=0.0) + xlim(-1,4) + ylim(-1,4) +p1 = qplot(mazzatorta,swiss,data=experimental,xlab="-log10(LOAEL Mazzatorta median)",ylab="-log10(LOAEL Swiss Federal Office median)",main="Experimental data") + geom_point() + geom_abline(intercept=0.0) + xlim(-1,4) + ylim(-1,4) training = read.csv("data/training-test-predictions.csv",header=T) -p2 = qplot(-log10(LOAEL_predicted),-log10(LOAEL_measured_median),data=training,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0) + xlim(-1,4) + ylim(-1,4) +p2 = qplot(LOAEL_predicted,LOAEL_measured_median,data=training,xlab="-log10(LOAEL predicted)",ylab="-log10(LOAEL measured median)",main="Combined") + geom_point() + geom_abline(intercept=0.0) + xlim(-1,4) + ylim(-1,4) -pdf('figure/test-correlation.pdf') +pdf('figures/test-correlation.pdf') grid.arrange(p1,p2,ncol=1,respect=T) dev.off() diff --git a/scripts/test-prediction-plot.R b/scripts/test-prediction-plot.R index db003d3..7201e1a 100755 --- a/scripts/test-prediction-plot.R +++ b/scripts/test-prediction-plot.R @@ -1,7 +1,9 @@ +#!/usr/bin/Rscript + library(ggplot2) training = read.csv("data/training-test-predictions.csv",header=T) -test <- read.csv("data/test.csv",header=T) +test <- read.csv("data/test_log10.csv",header=T) n = c("SMILES","LOAEL","Source") data = data.frame(factor(test$SMILES),test$LOAEL,factor(test$Dataset)) @@ -11,11 +13,11 @@ comb = data.frame(factor(training$SMILES),training$LOAEL_predicted,factor(traini names(comb) = n comb$Type = "predicted" data = rbind(data,comb) -data$LOAEL = -log(data$LOAEL) +#data$LOAEL = -log(data$LOAEL) data$SMILES <- reorder(data$SMILES,data$LOAEL) #img <- ggplot(data, aes(SMILES,LOAEL,ymin = min(LOAEL), ymax=max(LOAEL),shape=Source,color=Type)) img <- ggplot(data, aes(SMILES,LOAEL,ymin = min(LOAEL), ymax=max(LOAEL),color=Type)) img <- img + ylab('-log(LOAEL mg/kg_bw/day)') + xlab('Compound') + theme(axis.text.x = element_blank()) + theme(legend.title=element_blank()) img <- img + geom_point() -ggsave(file='figure/test-prediction.pdf', plot=img,width=12, height=8) +ggsave(file='figures/test-prediction.pdf', plot=img,width=12, height=8) diff --git a/scripts/test-validation-results.rb b/scripts/test-validation-results.rb new file mode 100755 index 0000000..2750019 --- /dev/null +++ b/scripts/test-validation-results.rb @@ -0,0 +1,14 @@ +#!/usr/bin/env ruby +require_relative '../../lazar/lib/lazar' +include OpenTox + +validation = Validation::TrainTest.find File.read("data/training-test-predictions.id").chomp + +data = [] +puts ["SMILES","LOAEL_measured_median","LOAEL_predicted","Error","Dataset"].join(",") +validation.predictions.each do |id,p| + data << [Compound.find(id).smiles, p["measurements"].median, p["value"], (p["measurements"].median-p["value"]).abs,"test-prediction"] +end + +data.sort!{|a,b| a[1] <=> b[1]} +puts data.collect{|r| r.join ","}.join("\n") diff --git a/scripts/test-validation.rb b/scripts/test-validation.rb index 0bbcc42..5c07449 100755 --- a/scripts/test-validation.rb +++ b/scripts/test-validation.rb @@ -1,26 +1,10 @@ -require_relative "include.rb" +#!/usr/bin/env ruby +require_relative '../../lazar/lib/lazar' +include OpenTox -test = Dataset.from_csv_file(File.join(DATA,"test.csv")) +test = Dataset.from_csv_file(File.join("data","test_log10.csv")) +train = Dataset.from_csv_file(File.join("data","training_log10.csv")) -file = File.join(DATA,ARGV[0]) -dataset = Dataset.from_csv_file file -model = Model::LazarRegression.create(dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_fingerprint_regression") -#model = Model::LazarRegression.create(dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_physchem_regression") -#model = Model::LazarRegression.create(dataset, :prediction_algorithm => "OpenTox::Algorithm::Regression.local_weighted_average") -validation = RegressionValidation.create model, dataset, test -csv_file = file.sub(".csv","-test-predictions.csv") -id_file = file.sub(".csv","-test-predictions.id") -File.open(id_file,"w+"){|f| f.puts validation.id} -name = File.basename(ARGV[0],".csv") - -data = [] -validation.predictions.each do |p| - data << [Compound.find(p[0]).smiles, p[1].median, p[2], p[3],"#{name}-prediction"] -end - -data.sort!{|a,b| a[1] <=> b[1]} - -CSV.open(csv_file,"w+") do |csv| - csv << ["SMILES","LOAEL_measured_median","LOAEL_predicted","RMSE","Dataset"] - data.each{|r| csv << r} -end +model = Model::LazarRegression.create(training_dataset: train) +validation = Validation::TrainTest.create model, train, test +puts validation.id |