improved handling of duplicates in validations

8 files changed, 710 insertions, 2 deletions

diff --git a/create_nestec_models.rb b/create_nestec_models.rb
index d9fa299..bfcf468 100644
--- a/create_nestec_models.rb
+++ b/create_nestec_models.rb
@@ -8,7 +8,8 @@ include OpenTox
   #"Mouse_TD50.csv",
   #"LOAEL_mg_corrected_smiles_mmol.csv",
   #"swissRat_chron_LOAEL_mmol.csv",
-  "LOAEL-rat-combined.csv"
+  #"LOAEL-rat-combined.csv",
+  "LOAEL-rat-combined_median.csv",
   #"LOAEL_mmol_corrected_smiles.csv",
   #"swissMouse_chron_LOAEL_mmol.csv",
   #"swissMultigen_LOAEL_mmol.csv",
diff --git a/paper/Makefile b/paper/Makefile
index 5fa2f47..22018a3 100644
--- a/paper/Makefile
+++ b/paper/Makefile
@@ -4,6 +4,11 @@ loael.pdf: loael.md functional-groups.pdf loael-dataset-correlation.pdf
 loael.docx: loael.md functional-groups.pdf loael-dataset-correlation.pdf
 	pandoc --filter pandoc-citeproc loael.md -s -o loael.docx
 
+%.md: %.Rmd
+	Rscript --vanilla -e "library(knitr); knit('$<');"
+
+%.pdf: %.md
+	pandoc --filter pandoc-citeproc $< -s -o $@
 
 functional-groups.pdf: functional-groups-reduced4R.csv functional-groups.R
 	R CMD BATCH functional-groups.R
diff --git a/paper/create-training-test-sets.rb b/paper/create-training-test-sets.rb
new file mode 100644
index 0000000..1079341
--- /dev/null
+++ b/paper/create-training-test-sets.rb
@@ -0,0 +1,42 @@
+require_relative '../../lazar/lib/lazar'
+include OpenTox
+dirpath = File.join(File.dirname(__FILE__),"..","regression")
+old = CSV.read File.join(dirpath,"LOAEL_mg_corrected_smiles_mmol.csv")
+old.shift
+new = CSV.read File.join(dirpath,"swissRat_chron_LOAEL_mmol.csv")
+new.shift
+p old.size
+p new.size
+# canonical smiles
+old.collect!{|r| [Compound.from_smiles(r.first).smiles, r.last]}
+new.collect!{|r| [Compound.from_smiles(r.first).smiles, r.last]}
+old_compounds = old.collect{|r| r.first}.uniq
+new_compounds = new.collect{|r| r.first}.uniq
+p old_compounds.size
+p new_compounds.size
+common_compounds = (old_compounds & new_compounds).uniq
+p common_compounds.size
+common = []
+# TODO: canonical smiles??
+common_compounds.each do |smi|
+  old_rows = old.select{|r| r.first == smi}
+  new_rows = new.select{|r| r.first == smi}
+  common += old_rows + new_rows
+  old -= old_rows
+  new -= new_rows
+end
+header = ["SMILES","LOAEL"]
+p old.size
+p new.size
+p common.size
+{
+  "mazzatorta-loael-training.csv" => old.uniq,
+  "swiss-loael-training.csv" => new.uniq,
+  "combined-training.csv" => (old+new).uniq,
+  "common-test.csv" => common.uniq,
+}.each do |file,data|
+  CSV.open(File.join(dirpath,file),"w+") do |csv|
+    csv << header
+    data.each{|row| csv << row}
+  end
+end
diff --git a/paper/loael.Rmd b/paper/loael.Rmd
new file mode 100644
index 0000000..65f9b34
--- /dev/null
+++ b/paper/loael.Rmd
@@ -0,0 +1,249 @@
+---
+author: |
+    Christoph Helma^1^, David Vorgrimmler^1^, Denis Gebele^1^, Martin Gütlein^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
+keywords: (Q)SAR, read-across, LOAEL
+date: \today
+abstract: " "
+documentclass: achemso
+bibliography: references.bib
+bibliographystyle: achemso
+biblio-style: achemso
+...
+
+Introduction
+============
+
+Christoph + Elena + Benoit
+
+The main objectives of this study are
+
+-   to investigate the experimental variability of LOAEL data
+
+-   develop predictive model for lowest observed effect levels
+
+-   compare the performance of model predictions with experimental
+    variability
+
+Materials and Methods
+=====================
+
+Datasets
+--------
+
+### Mazzatorta dataset
+
+Just referred to the paper 2008.
+
+### Swiss Federal Office dataset
+
+Elena + Swiss Federal Office contribution (input)
+
+Only rat LOAEL values were used for the current investigation, because
+they correspond directly to the Mazzatorta dataset.
+
+### Preprocessing
+
+Christoph
+
+Chemical structures in both datasets are represented as SMILES strings
+(Weininger 1988). Syntactically incorrect and missing SMILES were
+generated from other identifiers (e.g names, CAS numbers) when possible.
+Studies with undefined (“0”) or empty LOAEL entries were removed for
+this study.
+
+Algorithms
+----------
+
+Christoph
+
+For this study we are using the modular lazar (*la*zy *s*tructure
+*a*ctivity *r*elationships) framework (Maunz et al. 2013) for model
+development and validation.
+
+lazar follows the following basic workflow: For a given chemical
+structure it searches in a database for similar structures (neighbors)
+with experimental data, builds a local (Q)SAR model with these neighbors
+and uses this model to predict the unknown activity of the query
+compound. This procedure resembles an automated version of *read across*
+predictions in toxicology, in machine learning terms it would be
+classified as a *k-nearest-neighbor* algorithm.
+
+Apart from this basic workflow lazar is completely modular and allows
+the researcher to use any algorithm for neighbor identification and
+local (Q)SAR modelling. Within this study we are using the following
+algorithms:
+
+### Neighbor identification
+
+Christoph
+
+Similarity calculations are based on MolPrint2D fingerprints (Bender et
+al. 2004) from the OpenBabel chemoinformatics library (OBoyle et al.
+2011).
+
+The MolPrint2D fingerprint uses atom environments as molecular
+representation, which resemble basically the chemical concept of
+functional groups. For each atom in a molecule it represents the
+chemical environment with the atom types of connected atoms.
+
+The main advantage of MolPrint2D fingerprints over fingerprints with
+predefined substructures (such as OpenBabel FP3, FP4 or MACCs
+fingerprints) is that it may capture substructures of toxicological
+relevance that are not included in predefined substructure lists.
+Preliminary experiments have shown that predictions with MolPrint2D
+fingerprints are indeed more accurate than fingerprints with predefined
+substructures.
+
+From MolPrint2D fingerprints we can construct a feature vector with all
+atom environments of a compound, which can be used to calculate chemical
+similarities.
+
+[//]: # https://openbabel.org/docs/dev/FileFormats/MolPrint2D_format.html#molprint2d-format
+
+The chemical similarity between two compounds is expressed as the
+proportion between atom environments common in both structures and the
+total number of atom environments (Jaccard/Tanimoto index (1)).
+
+(1) $sim = \frac{|A \cap B|}{|A \cup B|}$, $A$ atom environments of
+    compound A, $B$ atom environments of compound B.
+
+### Local (Q)SAR models
+
+Christoph
+
+As soon as neighbors for a query compound have been identified, we can
+use their experimental LOAEL values to predict the activity of the
+untested compound. In this case we are using the weighted mean of the
+neighbors LOAEL values, where the contribution of each neighbor is
+weighted by its similarity to the query compound.
+
+### Validation
+
+Christoph
+
+Results
+=======
+
+### Dataset comparison
+
+Christoph + Elena
+
+The main objective of this section is to compare the content of both
+databases in terms of structural composition and LOAEL values, to
+estimate the experimental variability of LOAEL values and to establish a
+baseline for evaluating prediction performance.
+
+#### Applicability domain
+
+##### Ches-Mapper analysis
+
+Martin
+
+CheS-Mapper (Chemical Space Mapping and Visualization in 3D,
+http://ches-mapper.org/, (Gutlein, Karwath, and Kramer 2012)) can be
+used to analyze the relationship between the structure of chemical
+compounds, their physico-chemical properties, and biological or toxic
+effects. CheS-Mapper embeds a dataset into 3D space, such that compounds
+with similar feature values are close to each other. The following two
+screenshots visualise the comparison. The datasets are embeded into 3D
+Space based on structural fragments from three Smart list (OpenBabel
+FP3, OpenBabel FP4 and OpenBabel MACCS).
+
+##### Distribution of functional groups
+
+Christoph
+
+Figure 1 shows the frequency of selected functional groups in both
+datasets. A complete table for 138 functional groups from OpenBabel FP4
+fingerprints can be found in the appendix.
+
+![Frequency of functional groups.](functional-groups.pdf)
+
+### Experimental variability versus prediction uncertainty 
+
+Christoph
+
+Duplicated LOAEL values can be found in both datasets and there is a
+substantial overlap of compounds, with LOAEL values in both datasets.
+
+##### Intra dataset variability
+
+The Mazzatorta dataset has 562 LOAEL values with 439 unique structures,
+the Swiss Federal Office dataset has 493 rat LOAEL values with 381
+unique structures. Figure 2 shows the intra-dataset variability, where
+each vertical line represents a single compound and each dot represents
+an individual LOAEL value. The experimental variance of LOAEL values is
+similar in both datasets (p-value: 0.48).
+
+[//]: # p-value: 0.4750771581019402
+
+![Intra dataset variability: Each vertical line represents a compound, dots are individual LOAEL values.](loael-dataset-comparison-all-compounds.pdf)
+
+##### Inter dataset variability
+
+Figure 3 shows the same situation for the combination of the Mazzatorta
+and Swiss Federal Office datasets. Obviously the experimental
+variability is larger than for individual datasets.
+
+![Inter dataset variability](loael-dataset-comparison-common-compounds.pdf)
+
+
+##### LOAEL correlation between datasets
+
+Figure 4 depicts the correlation between LOAEL data from both datasets
+(using means for multiple measurements). Correlation analysis shows a
+significant correlation with r\^2: 0.61, RMSE: 1.22, MAE: 0.80
+
+[//]: #   MAE: 0.801626064534318
+[//]: # with identical values
+
+![LOAEL correlation](loael-dataset-correlation.pdf)
+
+
+### Local (Q)SAR models
+
+Christoph
+
+Discussion
+==========
+
+### Elena + Benoit
+
+### 
+
+Summary
+=======
+
+References
+==========
+
+Bender, Andreas, Hamse Y. Mussa, and Robert C. Glen, and Stephan
+Reiling. 2004. “Molecular Similarity Searching Using Atom Environments,
+Information-Based Feature Selection, and a Naïve Bayesian Classifier.”
+*Journal of Chemical Information and Computer Sciences* 44 (1): 170–78.
+doi:[10.1021/ci034207y](https://doi.org/10.1021/ci034207y).
+
+Gütlein, Martin, Andreas Karwath, and Stefan Kramer. 2012. “CheS-Mapper
+- Chemical Space Mapping and Visualization in 3D.” *Journal of
+Cheminformatics* 4 (1): 7.
+doi:[10.1186/1758-2946-4-7](https://doi.org/10.1186/1758-2946-4-7).
+
+Maunz, Andreas, Martin Gütlein, Micha Rautenberg, David Vorgrimmler,
+Denis Gebele, and Christoph Helma. 2013. “Lazar: A Modular Predictive
+Toxicology Framework.” *Frontiers in Pharmacology* 4. Frontiers Media
+SA.
+doi:[10.3389/fphar.2013.00038](https://doi.org/10.3389/fphar.2013.00038).
+
+OBoyle, Noel M, Michael Banck, Craig A James, Chris Morley, Tim
+Vandermeersch, and Geoffrey R Hutchison. 2011. “Open Babel: An Open
+Chemical Toolbox.” *Journal of Cheminformatics* 3 (1). Springer Science;
+Business Media: 33.
+doi:[10.1186/1758-2946-3-33](https://doi.org/10.1186/1758-2946-3-33).
+
+Weininger, David. 1988. “SMILES, a Chemical Language and Information
+System. 1. Introduction to Methodology and Encoding Rules.” *Journal of
+Chemical Information and Computer Sciences* 28 (1): 31–36.
+doi:[10.1021/ci00057a005](https://doi.org/10.1021/ci00057a005).
diff --git a/paper/loael.md b/paper/loael.md
index ac7a50d..9926bec 100644
--- a/paper/loael.md
+++ b/paper/loael.md
@@ -142,7 +142,7 @@ baseline for evaluating prediction performance.
 
 ##### Ches-Mapper analysis
 
-Christoph
+Martin
 
 CheS-Mapper (Chemical Space Mapping and Visualization in 3D,
 http://ches-mapper.org/, (Gutlein, Karwath, and Kramer 2012)) can be
diff --git a/paper/loael.pdf b/paper/loael.pdf
index 93749fc..cbca010 100644
--- a/paper/loael.pdf
+++ b/paper/loael.pdf
diff --git a/paper/predict-testset.rb b/paper/predict-testset.rb
new file mode 100644
index 0000000..7da6f31
--- /dev/null
+++ b/paper/predict-testset.rb
@@ -0,0 +1,19 @@
+require_relative '../../lazar/lib/lazar'
+include OpenTox
+
+dir = File.join(File.dirname(__FILE__),"..","regression")
+test = Dataset.from_csv_file(File.join(dir,"common-test.csv"))
+[
+  "LOAEL_mg_corrected_smiles_mmol.csv",
+  "swissRat_chron_LOAEL_mmol.csv",
+  "LOAEL-rat-combined.csv"
+].each do |train|
+  file = File.join(dir,train)
+  params = {
+    :prediction_algorithm => "OpenTox::Algorithm::Regression.local_pls_regression",
+  }
+  dataset = Dataset.from_csv_file file
+  model = Model::LazarRegression.create dataset, params
+  validation = Validation.create model, dataset, test
+  puts "#{train}: #{validation.id.to_s}"
+end
diff --git a/regression/common-test.csv b/regression/common-test.csv
new file mode 100644
index 0000000..864c4e6
--- /dev/null
+++ b/regression/common-test.csv
@@ -0,0 +1,392 @@
+SMILES,LOAEL
+O=Cc1ccco1,0.624453213155231
+CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.18534506246313948
+CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.17607780933998252
+CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.24799169923196304
+CCOCN(c1c(C)cccc1CC)C(=O)CCl,0.2557761861991325
+COCN(c1c(CC)cccc1CC)C(=O)CCl,0.05560351873894184
+COCN(c1c(CC)cccc1CC)C(=O)CCl,0.05189661748967905
+COCN(c1c(CC)cccc1CC)C(=O)CCl,0.009267253123156974
+COCN(c1c(CC)cccc1CC)C(=O)CCl,0.4670695574071115
+CCOC(=O)C(Oc1ccc(cc1)Oc1cnc2c(n1)ccc(c2)Cl)C,0.009924832004782804
+CCOC(=O)C(Oc1ccc(cc1)Oc1cnc2c(n1)ccc(c2)Cl)C,0.04157699893895499
+COC(=O)NS(=O)(=O)c1ccc(cc1)N,0.7817895162025876
+COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,0.0011344859332252924
+COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,0.0070905370826580775
+COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,0.00813048252144793
+COP(=S)(SCn1nnc2c(c1=O)cccc2)OC,0.008508644649457775
+CNC(=O)Oc1ccccc1OC(C)C,0.23895810443138246
+CNC(=O)Oc1ccccc1OC(C)C,0.22939978025412716
+O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.08510674803234901
+O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.3880867710275115
+O=C(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.08272375649019124
+CNC(=O)Oc1cccc2c1cccc2,0.07752660703214034
+CNC(=O)Oc1cccc2c1cccc2,0.2981792578159244
+CNC(=O)Oc1cccc2c1cccc2,0.2991731924668564
+O=C(C1=C(C)OCCS1)Nc1ccccc1,0.1274956638724717
+O=C(C1=C(C)OCCS1)Nc1ccccc1,0.034848813981213346
+COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1Cl,0.06987675250196507
+COc1nc(nc(n1)C)NC(=O)NS(=O)(=O)c1ccccc1Cl,0.05590140200157206
+CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,0.0022052807653206367
+CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,0.004686221626306353
+CCOP(=S)(Oc1ccc2c(c1)oc(=O)c(c2C)Cl)OCC,0.0033630532459809582
+Nc1nc(NC2CC2)nc(n1)N,0.09026150563412319
+Nc1nc(NC2CC2)nc(n1)N,0.9387196585948812
+COC(=O)c1c(Cl)c(Cl)c(c(c1Cl)Cl)C(=O)OC,1.5061863289853148
+COC(=O)c1c(Cl)c(Cl)c(c(c1Cl)Cl)C(=O)OC,0.030123726579706293
+COP(=O)(OC=C(Cl)Cl)OC,0.010408382386229365
+COP(=O)(OC=C(Cl)Cl)OC,0.009729574839301364
+COP(=O)(OC=C(Cl)Cl)OC,0.010408382170442241
+OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,0.05398319600278186
+OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,0.006747899500347733
+OC(C(Cl)(Cl)Cl)(c1ccc(cc1)Cl)c1ccc(cc1)Cl,0.005938151689011985
+O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,0.025749696789273527
+O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,0.02510595436954169
+O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,0.022530984690614337
+O=C(NC(=O)c1c(F)cccc1F)Nc1ccc(cc1)Cl,0.4023390123323988
+CC1=C(C)S(=O)(=O)CCS1(=O)=O,0.047557630336441704
+CC1=C(C)S(=O)(=O)CCS1(=O)=O,0.23778815168220852
+CNC(=O)CSP(=S)(OC)OC,0.001090477150926923
+CNC(=O)CSP(=S)(OC)OC,0.02180954301853846
+CNC(=O)CSP(=S)(OC)OC,0.000872381733741038
+c1ccc(cc1)Nc1ccccc1,0.1831908345016181
+c1ccc(cc1)Nc1ccccc1,0.14773454395291782
+O=C(N(C)C)Nc1ccc(c(c1)Cl)Cl,0.02574063309087087
+O=C(N(C)C)Nc1ccc(c(c1)Cl)Cl,0.026813159469657157
+O=C(N(C)C)Nc1ccc(c(c1)Cl)Cl,0.007293179580314936
+ClCCP(=O)(O)O,1.0381053884590363
+ClCCP(=O)(O)O,3.0866333550182015
+ClCCP(=O)(O)O,0.08304843107672291
+ClCCP(=O)(O)O,0.9066120392542251
+ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.017192183580611947
+ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.015816808894162992
+ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.016160652565775233
+ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.027507493728979118
+ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.013753746864489559
+ClC1C(Cl)C(Cl)C(C(C1Cl)Cl)Cl,0.01616065190994549
+C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,0.13459866849613178
+C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,0.05047450068604942
+C=CCOC(c1ccc(cc1Cl)Cl)Cn1cncc1,0.05350296944357954
+CCC(c1noc(c1)NC(=O)c1c(OC)cccc1OC)(CC)C,0.15252975563710267
+CCC(c1noc(c1)NC(=O)c1c(OC)cccc1OC)(CC)C,1.5854670852219546
+CCC(c1noc(c1)NC(=O)c1c(OC)cccc1OC)(CC)C,1.8050858655278421
+CCOC(=O)CC(C(=O)OCC)SP(=S)(OC)OC,0.1513509494941276
+CCOC(=O)CC(C(=O)OCC)SP(=S)(OC)OC,1.0897268363577188
+CCOC(=O)CC(C(=O)OCC)SP(=S)(OC)OC,0.08778355070659401
+CCOC(=O)CC(C(=O)OCC)SP(=S)(OC)OC,0.43286371555320496
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.006615259485207122
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.002646103794082849
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.004134537178254452
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.005292207588165698
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.0021168829879502555
+COc1sc(=O)n(n1)CSP(=S)(OC)OC,0.005689123251910172
+CSc1nnc(c(=O)n1N)C(C)(C)C,0.06999926640768805
+CSc1nnc(c(=O)n1N)C(C)(C)C,0.060666030886662975
+CSc1nnc(c(=O)n1N)C(C)(C)C,0.06719929397120725
+CC(Oc1cc(c(cc1Cl)Cl)n1nc(oc1=O)C(C)(C)C)C,0.01448347496337274
+CC(Oc1cc(c(cc1Cl)Cl)n1nc(oc1=O)C(C)(C)C)C,0.010428101697378017
+CNC(=O)ON=C(C(=O)N(C)C)SC,0.02280382932847922
+CNC(=O)ON=C(C(=O)N(C)C)SC,0.019109609238234706
+CNC(=O)ON=C(C(=O)N(C)C)SC,0.022347753176858155
+COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,0.06302765174348351
+COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,0.02836244328456758
+COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,0.005672488506643871
+COP(=S)(SCN1C(=O)c2c(C1=O)cccc2)OC,0.012605530348696702
+OC(=O)c1nc(Cl)c(c(c1Cl)N)Cl,0.24848916516834604
+OC(=O)c1nc(Cl)c(c(c1Cl)N)Cl,0.8282972172278201
+OC(=O)c1nc(Cl)c(c(c1Cl)N)Cl,1.0353715215347752
+CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,0.008187766847509327
+CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,0.04519647299825149
+CCN(c1nc(cc(n1)C)OP(=S)(OC)OC)CC,0.0068777238395693234
+CCC(=O)Nc1ccc(c(c1)Cl)Cl,0.09170952329114665
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+OC(C(C)(C)C)C(n1ncnc1)Oc1ccc(cc1)Cl,0.06424027322808253
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+OC(=O)COc1ccc(cc1C)Cl,0.0947069010825298
+Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,0.00694452873492003
+Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,0.006038720639060896
+Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,0.007548400798826121
+Clc1ccc(cc1)C(c1ccccc1Cl)(c1cncnc1)O,0.01086969686236098
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+Cc1nc(Nc2ccccc2)nc(c1)C1CC1,0.15801924849469393
+O=C1OC(C(=O)N1Nc1ccccc1)(C)c1ccc(cc1)Oc1ccccc1,0.044873074905021335
+O=C1OC(C(=O)N1Nc1ccccc1)(C)c1ccc(cc1)Oc1ccccc1,0.045407278177700156
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+COC(=O)N(C(=O)N1COC2(C(=N1)c1ccc(cc1C2)Cl)C(=O)OC)c1ccc(cc1)OC(F)(F)F,0.006820319575237628
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+CC(Cc1ccc(cc1)C(C)(C)C)CN1CC(C)OC(C1)C,0.005601648122412352
+Fc1ccc(cc1)[Si](c1ccc(cc1)F)(Cn1cncn1)C,0.006341300659739408
+Fc1ccc(cc1)[Si](c1ccc(cc1)F)(Cn1cncn1)C,0.01585325164934852
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+CC(Oc1ccccn1)COc1ccc(cc1)Oc1ccccc1,0.4315900691721648
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+CCCSP(=O)(SCCC)OCC,0.002063225311384027
+CCCSP(=O)(SCCC)OCC,0.010068539755671456
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+CCOP(=S)(SCn1c(=O)oc2c1ccc(c2)Cl)OCC,0.0636200517424888
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+CCOP(=S)(Oc1ncn(n1)c1ccccc1)OCC,0.004149211896481245
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+CNC(=O)Oc1cc(C)c(c(c1)C)SC,0.1242747128033579
+COC(=O)Nc1nc2c([nH]1)cccc2,0.3922867840256219
+COC(=O)Nc1nc2c([nH]1)cccc2,1.3076226134187396