Modeling of non-additive mixture properties using the Online CHEmical database and Modeling environment (OCHEM).

0.46 MB·English

by  Oprisiu, Ioana

Oprisiuetal.JournalofCheminformatics2013,5:4
http://www.jcheminf.com/content/5/1/4
SOFTWARE Open Access
Modeling of non-additive mixture properties
using the Online CHEmical database and
Modeling environment (OCHEM)
Ioana Oprisiu1, Sergii Novotarskyi2 and Igor V Tetko1,2*
Abstract
TheOnlineChemicalModelingEnvironment(OCHEM,http://ochem.eu)isaweb-basedplatformthatprovidestoolsfor
automationoftypicalstepsnecessarytocreateapredictiveQSAR/QSPRmodel.Theplatformconsistsoftwomajor
subsystems:adatabaseofexperimentalmeasurementsandamodelingframework.Sofar,OCHEMhasbeenlimitedto
theprocessingofindividualcompounds.Inthiswork,weextendedOCHEMwithanewabilitytostoreandmodel
propertiesofbinarynon-additivemixtures.Thedevelopedsystemispubliclyaccessible,meaningthatanyuseronthe
Webcanstorenewdataforbinarymixturesanddevelopmodelstopredicttheirnon-additiveproperties.
Thedatabasealreadycontainsalmost10,000datapointsforthedensity,bubblepoint,andazeotropicbehaviorof
binarymixtures.Forthesedata,wedevelopedmodelsforbothqualitative(azeotrope/zeotrope)andquantitative
endpoints(densityandbubblepoints)usingdifferentlearningmethodsandspeciallydevelopeddescriptorsfor
mixtures.Thepredictionperformanceofthemodelswassimilartoormoreaccuratethanresultsreportedinprevious
studies.Thus,wehavedevelopedandmadepubliclyavailableapowerfulsystemformodelingmixturesofchemical
compoundsontheWeb.
Background In our previous work [3,6] we developed new types of
Generally, QSPR (Quantitative Structure Property Rela- descriptors, basedonclassical(single compound)descrip-
tionship) models are limited to predicting properties of tors derived from the mixture components. Further, we
pure compounds. However, in recent years several stud- analyzed two different strategies of model validation: a
ies have attempted to develop QSPR models to predict “mixtures out” and a “compounds out” approach. How-
non-additive properties (density [1], infinite dilution ac- ever, the development of models for mixtures requires
tivity coefficient [2], bubble temperature [3], azeotropic verysubstantialeffortregardingthe storageand represen-
behavior [4], or excess molar volume [5]) of mixtures. tation of mixtures, calculation of descriptors, and imple-
There is also very considerable interest in modeling the mentation of correct validation protocols. Thus, in our
toxicityofchemical mixtures. previous work modeling of mixtures was limited to only
The most challenging problem in QSARs of mixtures is two sets of fragment-like descriptors, namely ISIDA frag-
representing a mixturebychemical descriptors. Therefore, ments[7]andsimplexdescriptors[8],plusafewmachine-
prior to modeling, investigators should decide which learningapproaches.
descriptors are appropriate for such modeling. A further In order to overcome these limitations, new features
questionconcernstheproperexternalvalidationofmodels were implemented in OCHEM [9] that allow reading and
formixtures,whichislessobviousthaninclassicalQSAR. uploadingofdataformixtures,creatingspecialdescriptors
for mixtures, and validatingmodels.Further, data for bin-
ary mixtures were collected from different sources [1,3,4]
and analyzed to demonstrate the usefulness of the new
*Correspondence:[email protected]
1InstituteofStructuralBiology,Helmholtz-ZentrumMünchen–German tool. The developed models for several target properties
ResearchCenterforEnvironmentalHealth,IngolstaedterLandstrasse1b.60w, are publicly available at http://ochem.eu/article/23416.
D-85764Neuherberg,Germany
2eADMETGmbH,IngolstädterLandstraße1,60w,85764Neuherberg, The main goal of the project was to develop a web-based
Germany
©2013Oprisiuetal.;licenseeChemistryCentralLtd.ThisisanOpenAccessarticledistributedunderthetermsoftheCreative
CommonsAttributionLicense(http://creativecommons.org/licenses/by/2.0),whichpermitsunrestricteduse,distribution,and
reproductioninanymedium,providedtheoriginalworkisproperlycited.
Oprisiuetal.JournalofCheminformatics2013,5:4 Page2of7
http://www.jcheminf.com/content/5/1/4
public resource for storage and analysis of chemical mix- Validationprotocol
tures. Further, we have demonstrated that the previously As in classical QSAR, a rigorous external validation
proposed methodology for calculating descriptors of mix- protocol is required to estimate the modeling of mix-
turesusingfragment-baseddescriptorsiseasilyextendable tures. However, the conventional external cross-
and can provide high accuracy models for other types of validation procedure, where the points (compounds) are
descriptors. randomly placed in the external set (or fold), is un-
acceptable. This results in overestimation of the predict-
Descriptorsformixtures ive performances of the developed models, especially
Mixture descriptors developed in this study were con- when mixtures of the same compounds with different
structed as suggested in previous work [3,6], based on ratios are present several times in the dataset. Indeed, if
the descriptors of individual components of the mixture. bothtrainingandexternalsetsincludedatapointscorre-
Figure 1 shows two different descriptor types developed sponding to the same mixture, the true predictive per-
accordingtothemodeledproperty. formanceofamodelwillnotbeestimated properly.
Rigorous protocols for external validation were devel-
(i) Whenthe valueofamixture’spropertydoesnot oped for QSAR modeling of mixtures and are presen-
depend onthe concentrationofitscomponents, ted in [3,6]. They involve three different strategies
mixture descriptorsareobtainedbya simple (Figure 2):
(unweighted)averageorsumand theabsolute
difference ofthedescriptor values correspondingto (cid:1) “Pointsout”:data pointsarerandomlyplacedineach
theindividualconstituentsofthe mixture. These foldoftheexternalcross-validationset.Each
mixture descriptorsweresuccessfullyusedto mixture ispresent simultaneouslyinboththe
predictthe azeotropicbehaviorof binarymixtures training andtheexternalsets.Thisisthe weakest
inapreviousstudy[4]aswellasinour work. It validationprotocol.
shouldbenoted thateach mixture correspondsto (cid:1) “Mixturesout”: Alldata pointscorresponding to
oneproperty value. mixtures composedofthesame constituents,butin
(ii)Whenthe valueofthemixturepropertychanges different ratios,aresimultaneously removedand
with the concentrationofitscomponents,mixture placedinthe sameexternalfold.Thus,every
descriptorsare calculatedasmole-weightedsumsor mixture ispresentineitherthe trainingorthe
weightedsumsandweightedabsolutedifferences, externalset,butneverinboth sets.
usingthedescriptor valueandmole fraction ofeach (cid:1) “Compoundsout”:Pure compoundsandtheir
pure componentinthe mixture. These mixture mixtures aresimultaneouslyplacedinthesame
descriptorshavebeenused topredict thedensity externalfold.Thus,everymixtureintheexternalset
andbubblepoint of binary mixtures inprevious contains atleastone compoundthat isabsentfrom
studies [1,3]aswellasinour work. It shouldbe thetrainingset.Thisisthemost rigorous validation
noted thatinthiscaseeach mixtureisdefinedby protocol.
severalpoints(severalproperty values)
correspondingtothedifferentconcentration values The“pointsout”strategyreflectstheabilityofmodelsto
ofitscomponents. predict existing mixtures with novel compositions and is
rather limited. The second protocol evaluates the predic-
In our previous work, both types of mixture descrip- tion performance of a model for new mixtures, while the
tors were based exclusively on the ISIDA fragment “compounds out” protocol does so for new compounds.
descriptors. In the current study, we extended this ap- Therefore, only the “mixtures out” and “compounds out”
proach toarbitrary descriptortypes. strategieswereusedinthecurrentstudy.
Mixture A-B (one mixture ->one descriptor vector) DiA - the ithdescriptor of
Descriptors generation for mixture A-B Each mixture has only one property value. compound A
Compound A {(DA+DB)/2} OR {DA+DB, |DA-DB|} XA+XB=1
{DA,DA DA} Unweighteddescriptors i i i i i i
1 2 n
+
Mixture A-B (one mixture -> several descriptor vectors)
Weighteddescriptors The property of one mixture depends on the composition of its pure composants.
Compound B
{DB,DB DB} {X *DA+X *DB} OR {X *DA+X *DB, |X *DA-X *DB|}
1 2 n A i B i A i B i A i B i
Figure1Methodologyusedtocalculatedescriptorsformixtures.
Oprisiuetal.JournalofCheminformatics2013,5:4 Page3of7
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Cmp1 Cmp2 Cmp3 Cmp4 Cmp5 Cmp6 Cmp7 Cmp8
Cmp1
No data
Cmp2
Training set
Cmp3
Points out
Cmp4
Mixtures out
Cmp5
Compounds out
Cmp6
Cmp7
Cmp8
Figure2Protocolsforvalidationofmixturepropertymodels.
Implementation The OCHEM record refers to one compound only. In
IntegrationwiththeOnlineChemicalModeling order to allow calculation of descriptors for mixtures,
Environment thesecondcompound shouldbealsopresent.Thisposes
OCHEM is a user-friendly tool allowing any user on the a challenging problem for the design of the descriptor
Web to upload new data and develop QSAR models as calculation procedure. We solved this by requiring all
well as access data and models published by others. The compounds with a molar fraction >0.5 to be present in
experimental data stored in the database can be easily the training set at least once, i.e., as a first compound in
manipulated to build predictable QSAR models using the mixture record, and/or they should be included with
different machine-learning techniques (e.g., neural net- their pure properties. Indeed, the properties of pure
works,supportvectormachines,randomforest,etc.). compounds are always more easily accessible. For all the
Prior to the current work, OCHEM was limited to the studies reported below, we extended the datasets with
analysis of individual compounds. Based on knowledge pure compounds.
of mixture predictions, new features were incorporated
into OCHEM allowing users to store and model proper- Descriptorsandvalidationprotocol
tiesof binarynon-additivemixtures. As mentioned in section 2.1, special descriptors for mix-
tures were constructed based on those computed for the
Dataformat individual compounds constituting the mixture. Users
A support for storing mixture data in the OCHEM data- can choose between four different descriptor types,
base was developed. To do so, an Excel file is required obtained by simple averaging of the descriptors, the sum
containing the necessary information for mixtures. Each and absolute difference of the descriptors, the weighted
data point is represented by a row in the file. This must sum of the descriptors, or the weighted sum and
contain the structure (e.g., SMILES or SDF) of the com- weightedabsolute difference ofthedescriptors.
pound with the largest molar fraction in the mixture, Further, two different validation types were implemen-
its molar fraction, and the molecular ID or structure ted: “mixtures out” and “compounds out.” The default
(SMILESorSDF)correspondingtothesecondpurecom- validationtype was“compoundsout”,i.e., themostrigor-
pound in the OCHEM database. The experimental value ous validation protocol. Figure 3 shows a screenshot of
of the mixture property, its unit, and publication source the OCHEM Web interface giving the mixture calcula-
should be also provided. An example of the Excel file tion options.
requiredtouploadmixturesisavailableontheWikiweb-
siteathttp://wiki.ochem.eu/w/Upload_of_mixture_data. Results and discussion
The first compound in the binary mixture is always the Modelingpropertiesofbinarymixtures
onewiththehighestmolarfraction.Thus,themolarfrac- Qualitative and quantitative models were developed
tion values reported in the database range between 0.5 using different learning methods and different sets of
and 1. It should be noted that for a mixture the sum of descriptors, implemented in OCHEM. In this section,
the molar fractions of its components always equal 1. we report the performances of the obtained models and
Therefore, the value of the molar fraction of the second then compare them with models developed in previous
compound in a binary mixture can be easily obtained studies.
whenthemolarfractionofthefirstcompoundisknown.
Userscanalsospecifythenameofthesecondcompound Density
inthemixture.Incaseswherethemolarfactionis<0.5,the Ajmani et al. studied the density of binary mixtures in
firstandsecondcompoundareinterchangedandthecom- [1]. In their study, the QSPR methodology was applied
plement to 1 used as the molar fraction. This procedure to4679data pointsofexperimentallymeasured densities
allowsonetoavoidduplicateswhenuploadingmixtures. of binary liquid mixtures compiled from the literature,
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Figure3ScreenshotoftheOCHEMfeaturesimplementedtoanalyzemixtures.
corresponding to 271 binary mixtures. QSPR models could originate from for instance the preparation of
were developed to predict the deviation of the experi- chemical structures, the machine-learning approaches
mental mixture density from the “ideal” mixture density, used, or the presence of duplicated structures. For ex-
calculated by combining the densities of the single com- ample, we did not optimize structures and simply used
ponents according to their ratio in the mixture. Two 2D to 3D conversion using Corina. However, with the
methods of training/test set creation were used, QMD-1 full set of Dragon descriptors, ASNN calculated an
and QMD-2, which correspond respectively to the accuracy of R2=0.85±0.04, RMSE=0.0099±0.0006,
“points out” and “mixtures out” validation strategies (see which was similar to the aforementioned result reported
section 2.2). In addition, the authors used 39 of the 271 byAjmaniet al.
mixtures(fortheQMD-2strategy)forexternalvalidation. Similar performances for the prediction of the test set
We found that the authors did not notice eight mix- compounds were calculated for different methods and
ture duplicates (in total 144 data points), which are algorithms using a Comprehensive Modeling (CM) fea-
showninTableS1(seeAdditionalfile1:TableS1).Some ture of OCHEM. With a few clicks, CM enables users to
of these duplicates were between training and test sets. explore data using different descriptors and machine-
Such duplicates could bias the statistical results of the learning approaches. The models with the highest accur-
models for both strategies reported in the study. After acyaccordingto“compoundsout”10-foldcross-validation
eliminating duplicates, the training set contained 3734 aresummarizedinTable1.Both“mixturesout”and“com-
data points (we also included 124 pure compounds) and poundsout”10-foldcross-validationresultsaregiven.
672forthetest set. As expected, the prediction performance estimated
Our first analysis was to attempt to reproduce the using the “compounds out” protocol is generally lower
authors’ model developed using seven Dragon descrip- than that using “mixtures out” validation. Cross-
tors, namely EEig04d, BEHv3, Mor15m, H0e, GATS1e, validation results calculated using both protocols are
E2e, and Mor10e. In their original work the authors cal- lower (sometimes significantly so) compared to those
culate these descriptors using E-Dragon, which is sup- calculated for the test set, which corresponds to the
ported by the Virtual Computational Chemistry “mixtures out” validation. As stated by the authors, the
Laboratory (VCCLAB [10]). The VCCLAB used Dragon test set of mixtures was generated using the Sphere ex-
version 5.4 [11], which is also available on OCHEM. The clusion method.Thus,itwasselected toprovide thebest
authors used a simple averaging of descriptors according possible coverage of the space of analyzed mixtures. In
to their molar fraction. The same descriptors were used practice, the developed models are expected to be ap-
to develop a neural network model using the ASNN plicable to arbitrary mixtures. Thus, it is likely that the
method with all default parameters as provided by the performance of models for new data will be similar to
OCHEM website. The developed model predicted test that of n-fold cross-validation rather than to those
set molecules with a squared Pearson correlation coeffi- obtainedforthe specifically designed validationset.
cient (R2) of 0.73±0.04 and a root mean squared error So far we have used descriptors calculated as mole-
(RMSE) of 0.014±0.001. These coefficients are lower weighted sums of individual mixtures. OCHEM also cal-
than those (R2=0.86, RMSE=0.0091) reported in culates the second type of descriptors, that is, the
Ajmani et al. for the same descriptors. The difference weighted absolute difference of the descriptors. Using
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Table1StatisticalparametersofOnlineChemicalModelingEnvironment(OCHEM,http://ochem.eu)modelswiththe
lowestRMSEaccordingtothe“compounds-out”cross-validationprotocolforthepredictionofbinarymixturedensities
“Compoundsout” “Mixturesout” Testsetprediction
validation validation (“compoundsout”validation)
Method/descriptors R2 RMSE R2 RMSE R2 RMSE
LibSVM/Dragon 0.69±0.05* 0.014±0.001 0.81±0.09 0.011±0.002 0.88±0.04 0.0089±0.001
ASNN/Inductivedescriptors[12] 0.68±0.04 0.014±0.0008 0.72±0.04 0.0131±0.0009 0.81±0.06 0.011±0.001
LibSVM/Inductivedescriptors 0.71±0.05 0.014±0.001 0.81±0.03 0.0109±0.0005 0.88±0.04 0.0084±0.001
ASNN/Dragon[11] 0.56±0.06 0.016±0.001 0.69±0.04 0.014±0.001 0.85±0.04 0.0099±0.001
ASNN/ChemAxon[13] 0.55±0.06 0.017±0.001 0.69±0.04 0.0137±0.0009 0.88±0.03 0.0088±0.001
*95%confidenceintervalswerecalculatedusingabootstrapprocedurebasedon1,000replicas(implementedforallmodelsinOCHEM).RMSE–RootMean
SquaredError;R2–squareofthePearsoncorrelationcoefficient.
these descriptors, the results for the test set prediction using nonlinear Support Vector Machine (SVM), Asso-
calculated by LibSVM with ChemAxon descriptors were ciative Neural Networks (ASNN), and Random Forest
R2=0.94±0.02, RMSE=0.006±0.001 – the highest pre- (RF) approaches. For SVM and ASNN calculations, the
diction accuracy achieved for this test. However, the ISIDA fragment descriptors were used, whereas Simplex
results of the same method, as well as of other methods descriptors wereemployedinRFmodels.
for the “mixture-out” and “compound-out” validation The same data were modeled with OCHEM using
protocols, are not significantly different from those cal- LibSVM machine-learning and the same Dragon
culated usingthe weighted sum ofindividualmixtures. descriptors. Table 2 shows that the performances of the
developed models are similar to or higher than those
from the previous work, which demonstrates the useful-
Bubbletemperature
ness of the developed tool. Further, the results obtained
Vapor–liquid equilibrium (VLE) data are one of the most
inthepreviousstudy(ref[3])were basedonaconsensus
importanttypesofinformationforevaluationofthephase
prediction of three models, namely ASNN, SVM, and
behaviorofbinaryliquidmixtures,whichiscrucialforthe
RF. The LibSVM method achieved similar performance
design of separation processes [14]. Vapor–liquid equilib-
withoutneedingtobuild aconsensusmodel.
riumis a statewhere aliquidand itsvaporareinequilib-
rium with one another, i.e., when the rate of evaporation
equals the rate of condensation. A VLE curve shows the Azeotropicbehavior
variationoftheequilibriumcompositionoftheliquidmix- Azeotropic data are very important in the design of dis-
ture with temperature, at fixed pressure. The dew-point tillation processes, and their theoretical assessment
curve represents the temperature at which the saturated could significantly reduce the costs of selection of
vapor starts to condense, whereas the bubble point is the proper agents for industrial processes. An azeotrope is a
temperatureatwhichtheliquidstartstoboil. liquid mixture that boils at a constant temperature,
Bubble points of 167 mixtures containing 3232 data keeping its composition fixed. When an azeotrope boils,
points were modeled by Oprisiu et al. [3]. An external the resulting vapor has the same component ratio as the
test set of 94 mixtures containing almost 2000 data liquid phasewith which itisinequilibrium.
points was also used. Two versions of this set were used. Classification models were developed using 400 mix-
For “compound-out” validation we used 67 mixtures, tures (200 azeotropes/200 zeotropes) and validated on a
which included 34 new compounds (in total 1309 mea- data set of 95 mixtures containing only pure compounds
surements); for “mixture-out” validation we used a set of alreadyincludedinthetrainingset.
631 measurements, which included 27 new mixtures. In The same data sets were used in OCHEM. The pure
our previous work, we developed a consensus model compounds (n=65) of the mixtures were considered
Table2ComparisonofperformancesofOCHEMmodelswiththeconsensusmodelofOprisiuetal.[3]forthe
predictionofbubbletemperaturesofmixtures
“Mixturesout”validation “Compoundsout”
OCHEM Ref[3] OCHEM Ref[3]
Trainingset Q2 0.93±0.01 0.95 0.92±0.03 0.9
RMSE 6.2±0.6 5.2 6.5±0.5 7.0
Testset Q2 0.92±0.01 0.88 0.56±0.06 0.4
RMSE 5.7±0.3 5.9 19±1 21.4
Q2–coefficientofdetermination.
Oprisiuetal.JournalofCheminformatics2013,5:4 Page6of7
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Table3ComparisonofclassificationresultsfortheazeotropicbehaviorofmixturescalculatedusingOCHEMand
Oprisiu[4]models
5-FoldCrossValidation(400mixtures)* Testset(95mixtures)
OCHEM OCHEM Oprisiu[4] OCHEM Oprisiu[4]
compoundout mixtureout mixtureout mixtureout mixtureout
BalancedAccuracy 0.80±0.04 0.78±0.04 0.82 0.85±0.07 0.82
Recallofzeotrops 0.77 0.77 0.78 0.74 0.73
Recallofazeotropes 0.83 0.80 0.85 0.95 0.91
*OCHEMmodelsweredevelopedusing465mixtures,whichalsoincludedpurecompounds.
zeotropes and were included in the training set. Thus accuracy, use of a wide spectrum of machine-learning
465 mixtures were used as the training set and 95 for algorithms and descriptors, and storage, publishing, and
thetest set. application ofmodels.
Table 3 shows the classification results obtained with Inaddition,whileitwasintuitivetoproposedescriptors
OCHEM, using the Weka Random Forest algorithm for mixtures based on fragmental and simplex descriptors
and Adriana descriptors. The performances of the [1,3,4], the extension of this methodology to arbitrary
obtained models are similar to those from previous descriptors was not obvious. We have shown that the
work. OCHEM results calculated for mixture-out and samemethodologycanbeappliedsuccessfullywithdiffer-
compound-out protocols have similar accuracy. ent types of descriptors and different machine-learning
methods,thusgeneralizingthepreviousfindings.
Conclusion Theapproachdevelopedherecanbeusedtomodelany
OCHEM was extended with tools to store and model other non-additive properties of binary mixtures, such as
binary mixtures. The theoretical part of the modeling viscosity, toxicity, or antiviral activity. The methodology
approach was based on the PhD research of I.O. [3,4] as can be straightforwardly extended to multiple compound
wellasonpreviousstudies[1]. mixturescontainingmorethantwocompounds.
Wehavedevelopedanoriginal way ofstoring andpro-
cessing mixtures in the database. New descriptors for Availability and requirements
mixtures were used, based on classical (single com- Thedevelopedsoftwareispubliclyavailableon-linefrom
pound) descriptors derived from the mixture compo- On-line CHEmical database and Modelling environment
nents. In addition, specific cross-validation protocols for (OCHEM) platform http://ochem.eu. It can be accessed
mixtures were implemented: “mixtures out” and “com- with any modern web browser, which supports Java-
pounds out.” To validate our implementation, qualitative script. We recommend the latest version of Firefox
and quantitative models were developed with good pre- http://www.mozilla.org/firefox.
dictive performance, using different learning methods
and different sets of descriptors. The developed models Additional file
are available at http://ochem.eu/article/23416 and can be
usedtopredict properties ofnewmixtures. Additionalfile1:TableS1.MixtureduplicatesfoundintheAjmani[1]
Themain purpose ofthis studywasto contribute pub- dataset.Thefollowingadditionaldataareincludedwiththeonlineversion
ofthispaper.TableS1listseightmixtureduplicates(intotal144datapoints)
liclyavailable,web-basedtoolsforthemodelingandpre-
foundintheAjmanietal.densitydataforbinarymixtures[1].
diction of mixtures of chemical compounds. The
availability of such tools will stimulate developments in
Competinginterests
this research area. Indeed, preparation of data, calcula- ThepublicationcostswerecoveredwithMCITN“Environmental
tion of mixture descriptors, and application of the cor- ChemOinformatics”(ECO)projecthttp://www.eco-itn.eu,whichis
scientificallycoordinatedbyIVT.IOwasECOfellow.SNandIVTare
rect validation protocol require considerable effort and
shareholdersandemployeesofeADMETGmbHwhichlicencesOCHEM
can be prone to error. The developed tools allow users http://ochem.euplatform.
to significantly simplify this procedure and all analyses
Authors’contributions
can be performed with a few clicks. The mixtures data-
IOprogrammedmethodsformixturedescriptorcalculations,collected,
base also inherits all the general features of OCHEM, uploadedandcuratedthemixturedata,performedcalculationsanddrafted
namely strict control of the information source, storage thearticle.SNdevelopedframeworkforstorageandanalysisofmixtures.IVT
programmedcross-validationalgorithmsandsupervisedtheproject.All
of records in original units (and automatic conversion to
authorsreadandapprovedthefinalmanuscript.
units required for modeling), tracking of changes, public
and hidden records, storage of experiment conditions, Acknowledgements
ThisstudywaspartiallysupportedbytheFP7MCINTproject“Environmental
and automatic detection of duplicates. The modeling of Chemoinformatics”(ECO),grantagreementnumber238701,andtheGO-Bio
mixtures provides automatic calculation of prediction 1BBMBFprojectiPRIOR,grantagreementnumber315647.Wewouldliketo
Oprisiuetal.JournalofCheminformatics2013,5:4 Page7of7
http://www.jcheminf.com/content/5/1/4
thankmembersofeADMETGmbHhttp://www.eadmet.comfortheirhelp
withprogramminganddiscussionofresults.
Received:18October2012Accepted:8January2013
Published:15January2013
References
1. AjmaniS,RogersSC,etal:ApplicationofQSPRtomixtures.JChemInf
Model2006,46:2043–2055.
2. AjmaniS,RogersSC,etal:CharacterizationofMixturesPart1:Prediction
ofInfinite-DilutionActivityCoefficientsUsingNeuralNetwork-Based
QSPRModels.QSAR&CombSci2008,27:1346–1361.
3. OprisiuI,VarlamovaE,etal:QSPRApproachtoPredictNonadditive
PropertiesofMixtures.ApplicationtoBubblePointTemperaturesof
BinaryMixturesofLiquids.MolInform2012,6–7:491–502.
4. OprisiuI:ModélisationQSPRdemélangesbinairesnon-additifs.Applicationau
comportementazéotropique,PhDThesis.UniversitédeStrasbourg,Faculté
deChimie;2012.
5. AjmaniS,RogersSC,etal:CharacterizationofMixtures.Part2:QSPR
ModelsforPredictionofExcessMolarVolumeandLiquidDensityUsing
NeuralNetworks.MolInform2010,29:645–653.
6. MuratovEN,VarlamovaEV,etal:ExistingandDevelopingApproachesfor
QSARAnalysisofMixtures.MolInform2012,31:202–221.
7. VarnekA,FourchesD,etal:ISIDA-Platformforvirtualscreeningbased
onfragmentandpharmacophoricdescriptors.CurrComput-AidDrug
Design2008,4:191–198.
8. Kuz'minVE,ArtemenkoAG,MuratovEN:HierarchicalQSARtechnology
basedontheSimplexrepresentationofmolecularstructure.JComput
AidedMolDes2008,22:403–421.
9. SushkoI,NovotarskyiS,etal:Onlinechemicalmodelingenvironment
(OCHEM):webplatformfordatastorage,modeldevelopmentand
publishingofchemicalinformation.JComputAidMolDes2011,25:533–554.
10. TetkoI,GasteigerJ,etal:Virtualcomputationalchemistrylaboratory-
designanddescription.JComputAidMolDes2005,19:453–463.
11. TodeschiniR,ConsonniV:HandbookofMolecularDescriptors.Weinheim,
NewYork,Chichester,Brisbane,Singapore,Toronto:Wiley-VCH;2008.
12. CherkasovA,BanF,etal:Anupdatedsteroidbenchmarksetandits
applicationinthediscoveryofnovelnanomolarligandsofsex
hormone-bindingglobulin.JMedChem2008,51:2047–2056.
13. Chemaxon.http://www.chemaxon.com/.
14. GmehlingJ,BoltsR:Azeotropicdataforbinaryandternarysystemsat
moderatepressures.JChemEngData1996,41:202–209.
doi:10.1186/1758-2946-5-4
Citethisarticleas:Oprisiuetal.:Modelingofnon-additivemixture
propertiesusingtheOnlineCHEmicaldatabaseandModeling
environment(OCHEM).JournalofCheminformatics20135:4.
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