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DOI: 10.4018/JNN.2017010103
Journal of Nanotoxicology and NanomedicineVolume 2 • Issue 1 • January-June 2017
Buckminsterfullerene(C60)anditsderivativeshavecurrentlybeenusedaspromisingnanomaterialfor diagnostic and therapeutic agents. They are applied in pharmaceutical industry due to theirnanostructurecharacteristics,stabilityandhydrophobiccharacter.Duetoitssparinglysolublenature,thesolubilityofC60hasbeenofenormousattentionamongcarbonnanostructureinvestigatorsowingto its fundamental importance andpractical interest innanotechnology andmedical industry. InordertostudythediverseroleofC60anditsderivativesthedependenceoffullerene’ssolubilityonmolecularstructureofthesolventmustbeunderstood.CurrentstudywasdedicatedtotheexplorationofthesolubilityoffullereneC60in156organicsolventsusingsimple,interpretableandpredictive1Dand2Ddescriptorsemployingquantitativestructure-propertyrelationship(QSPR) technique.Theauthorsemployedgeneticalgorithmfollowedbymultiplelinearregressionanalysis(GA-MLR)togeneratethecorrelationmodels.Thebestperformanceisaccomplishedbythefour-variableMLRmodelwithinternalandexternalpredictioncoefficientofQ2=0.86andR2
exploring Simple, Interpretable, and Predictive QSPR Model of Fullerene C60 Solubility in organic SolventsLyudvig S. Petrosyan, Department of Physics, Jackson State University, Jackson, MS, USA
Supratik Kar, Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, USA
Jerzy Leszczynski, Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, USA
Bakhtiyor Rasulev, Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND, USA
1. INTRodUCTIoN
Fullerene,ahighlysymmetricalcage-likemoleculehasspecificinteractionwithorganicsolventsanditsknowledgecanprovidesignificantinformationonthemechanismsofsolute-solventinteractions.Thefullereneshavedefinedrigidgeometriesindistinctiontoothersoluteswhoseshapesundergoconformationalchanges.Notonlythatintramolecularvibrationalpartitionfunctionsmayundergosolvent-dependent changes (Prylutskyy et al., 2003). Due to sparingly soluble nature of C60 inmajororganicsolvents,theproductioncostisstillhighforthisnanomaterial(Shunaevetal.,2015).Therefore,understandingoffullerene’ssolubilityprovidessignificantfeatureassistinginpurification,
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Journal of Nanotoxicology and NanomedicineVolume 2 • Issue 1 • January-June 2017
AseriesofinvestigationforpredictingC60solubilityinorganicsolventsemployingQSPRmodelhasbeenreportedinthelast12years.Liuetal.(2005)generatedalinearmodelaswellasaleast-squaressupportvectormachine(LSSVM)modelforpredictingthesolubilityofC60in128and122organicsolvents,respectively.Toropovetal.(2007,2009)demonstratedtwokindsofdescriptorsmethodsforpredictingsolubilityofC60indifferentorganicsolvents.Samedatasetwasusedtobuildone-variablemodeloncewith theoptimaldescriptorscalculatedwithsimplifiedmolecular inputlineentrysystem(SMILES)(Toropovetal.,2007)andinanotherworkwithInternationalChemicalIdentifier(InChI)(Toropovetal.,2009)withhighstatisticalresults.Petrovaetal.(2011)depictedsuccessful application of the GA-MLR technique in combination with quantum-chemical andtopologicaldescriptorsyieldsreliablefour-variablemodelsfor122organicsolvents.OneGA-MLRmodelwasdevelopedtopredictthefullerenesolubilityin36benzenederivativesbyPourbasheeretal.(2011).Ghasemietal.(2013)proposedfirst3D-QSARmodelemployingVolSurfbaseddescriptorswithSPA-SVM(successiveprojection algorithm-support vectormachine)method to predictC60solubilityin132organicsolventswithacceptablestatisticalresults.Inrecenttime,Xuetal.(2016)proposedaQSPRmodelforpredictingthesolubilityoffullereneC60in156diverseorganicsolventswiththenormindexes.
In this regard, we aimed to find simple, predictive, computationally time-efficient andmechanisticallyinterpretablemodeltopredictthesolubilityofC60inthesamesetoforganicsolventsconsideredbyXuetal.(2016).Inaddition,thestudyintendstoestimatepredictivepotentialofthesimple1Dand2DdescriptorstomodelthesolubilityofthefullereneC60inalargenumberoforganicsolvents.
2. MATeRIALS ANd MeTHodS
2.1. data SetTheexperimentalsolubility(S)dataofC60in156organicsolvents(Table1)werecollectedfromtwodatasets:BeckandMándi(1997)andSemenovetal.(2010).Asthelogarithmicvaluesofmolarfractionscorrespondedtothefreeenergychangesinthesolvationprocess,theunitofsolubilitywasconsideredaslogS,insteadofweightunits(e.g.,mg/mL).
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2.2. descriptors CalculationSolventstructureswerepreparedinHyperChem8softwarepackage(HyperChem(TM))andsavedas.molextensionfile.Thereafter,DRAGON6(DRAGON,TALETEsrl,Italy)softwareemployedtogenerate apoolofdescriptors to correlatewith logS followedby findingbest featureswhichare responsible for C60 solubility in organic solvents. A total of 319 descriptors generated fromconstitutionalindices,topologicalindices,walk-pathcount,connectivityindices,functionalgroupcounts,ETAindices,Atom-centeredfragments,Atom-typeE-stateindices&molecularpropertieshavebeenconsidered.Detailsaboutthedescriptorsisdiscussedinthefollowinglink:http://www.talete.mi.it/products/dragon_molecular_descriptor_list.pdf
2.3. Model developmentTheinitialdatasetwasdividedintotrainingandtestsetbasedonsortedexperimentalpropertylogSresponsevaluefromlowertohigher.Then,every2ndmoleculefromthefoursolventsconsideredinthetestsetfromthesortedcolumnandtheprocesscarriedoutforthewholedataset.Therefore,datasetdividedinto3:1ratiowith117and39solventsinthetrainingandtestset,respectively.Then,geneticalgorithm(GA)wasemployedasthedescriptorselectionstatisticaltoolimplementedintheGenetic Algorithm 1.4 software package (http://teqip.jdvu.ac.in/QSAR_Tools/). We applied GAtoselectonlythebestcombinationsofdescriptorsforbuildingmodelswiththehighestpredictivepowerofsolubility.Thenthemultiple linearregression(MLR)analysiswasperformedbyMLRPlusValidationGUI1.2software(http://teqip.jdvu.ac.in/QSAR_Tools/),followedbyvalidationofthemodelusingthetestsetcompounds.Overall,thecombinedGA-MLRtechniquewasutilizedtoselecttheappropriatedescriptorsandtogeneratedifferentQSPRmodelsselectingthebestmodelswithvariablesintherangefrom1to6.
2.4. Model ValidationAsetofstringentstatisticalmetricswereutilizedtomakesurethefitnessofthein silicomodelsthrough internalandexternalvalidationmethodologies.Thegoodness-of-fitof theequationwascheckedbyregressioncoefficient(R2),aswellasbyusingthefollowinginternalvalidationmetrics:theleave-one-outcrossvalidation(Q2
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3. ReSULTS ANd dISCUSSIoN
3.1. Statistical Performance and Validation Quality of QSAR ModelsTofindstatisticallyacceptedaswellasreliablemodels,wecalculateddifferentmodelsbyincreasingnumberofdescriptorsuntilthemodelattainthepeakofcorrelationcoefficientforinternalaswellasexternalvalidation.Thefour-variablemodel(Equation(4))yieldsthebestpredictivepotentialfor the solubilityofC60 inorganic solvents,which is strongly supportedby comparativeplot ofcorrelationcoefficientforindividualmodel(Figure1).Figure1showsthatexternalpredictionqualitydroppeddownfor5and6descriptorsmodelsincomparingto4-descriptormodel.Onthecontrary,theinternalvalidationstatisticsarealmostcomparableforallthreemodels.Thus,similarinternalpredictionqualityandbetterexternalpredictioncanbeachievedwith4-descriptormodel than5and6descriptorsmodels.Inaddition,asthenumberofdescriptorsincreases,thecomplexityofthemodelincreasesalongwiththeinterpretationcapability.Therefore,wehavechosenequation(4)asthebestmodeloverequations(5)and(6).Collinearityischeckedamongmodeleddescriptorsthroughpearsoncorrelation.Statisticaloutcomesofonetosix-variablemodelsareillustratedinTable2.Thedevelopedmodelequationsareasfollow:
ID. CAS no. Solvent name logS (Exp) logS (Cal) Residual
137 584-02-1 Pentan-3-ol -5.36 -5.50 0.14
139 110-63-4 1,4-Butanediol -6.57 -5.87 -0.70
140 111-29-5 1,5-Pentanediol -6.19 -5.49 -0.70
142 104-92-7 p-Bromoanisole -2.54 -3.03 0.49
149 64-19-7 Aceticacid -6.27 -6.63 0.36
155 124-07-2 Octanoicacid -4.98 -4.40 -0.58
Table 1. Continued
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log( ) - . ( . ) - . ( . )
. ( . )
S BBI = ± ± ×
+ ± ×
13 648 0 646 0 011 0 025
5 539 0 497α∑∑
∑
± ×
+ ± × + ±N
SAaccV
s
- . ( . )
. ( . ) . ( . )'
0 016 0 003
4 061 0 876 2 205 0 233
β ××piPC 01
(5)
Six-variablemodel:
log( ) - . ( . ) . ( . )
. ( . )
S AMW
pi
= ± + ± ×+ ± ×
8 591 0 235 0 063 0 005
1 001 0 117 PPC Cl
NsssN
02 0 681 0 188 086
0 329 0 315 0 015 0 00
+ ± ×+ ± × ±
. ( . ) -
. ( . ) - . ( . 33
0 653 0 093 03
)
. ( . )
×+ ± ×
SAacc
MPC
(6)
As the trainingsetcomposition is fixed, theremaybeabias inselectionof thedescriptors.Therefore,wehavealsoperformeddoublecross-validationstrategy(Roy&Ambure,2016)tomakesureour4descriptorsmodelisbestorwecangetanyotherbettermodel.Incaseofdouble-crossvalidation,bestmodelobtainedbyConsensusModelPredictionsamong threemethods (methodparametersandresultareprovidedinthesupplementarymaterial)underdoublecrossvalidationinsoftwarementionedunderhttp://teqip.jdvu.ac.in/QSAR_Tools/.Now,ifwecompareourpreviousbestmodelandbestmodelevolvedfromthedoublecrossvalidation,ourprevious4descriptorsGA-MLR
Figure 1. Comparative plot of correlation coefficient values for individual model with one to six variables
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modelevolvedasthebestmodelbasedonregression-basedmetricsaswellaserrorbasedmetrics.Followedbybias inprediction (Roy,Ambure&Aher,2017) isalsochecked for thebestmodel(Equation(4))andacceptedresultsareasfollowed:Variance:0.0087,Bias2:0.160,Variance+Bias2:0.169,Meansquareerror(MSE):0.159.
Figure 2. Scatter plot for the best four-variable model
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Tabl
e 2. S
tatis
tical
outc
ome f
or th
e sele
cted
mod
els w
ith o
ne to
six d
escr
ipto
rs
Mod
elIn
tern
al v
alid
atio
nEx
tern
al v
alid
atio
nM
AE
base
d cr
iteri
a
Ran
dom
izat
ion
GTC
R2
Q2 LO
OR
MSE
cr m
(LO
O)S
cale
d2
”rm
(LO
O)S
cale
d2
Rpred2
RM
SEp
r m(t
est)
Scal
ed2
”rm
(tes
t)Sc
aled
2c R
p2
10.
640.
630.
762
0.49
0.27
0.68
0.68
40.
510.
26M
oder
ate
0.64
Pass
20.
790.
780.
579
0.69
0.17
0.81
0.52
60.
660.
18G
ood
0.79
Pass
30.
840.
830.
507
0.76
0.14
0.89
0.39
80.
810.
10G
ood
0.83
Pass
40.
870.
860.
461
0.79
0.12
0.89
0.40
00.
820.
10G
ood
0.85
Pass
50.
880.
860.
447
0.80
0.11
0.87
0.43
90.
820.
00G
ood
0.85
Pass
60.
890.
870.
422
0.82
0.10
0.86
0.41
60.
840.
01G
ood
0.86
Pass
a Good
pred
iction
s: MA
E ≤
0.1 ×
train
ing se
t ran
ge an
d MAE
+ 3σ
≤0.2
× tr
aining
set r
ange
. Bad
pred
iction
s: MA
E >
0.15 ×
train
ing se
t ran
ge or
MAE
+ 3σ
> 0.
25 ×
train
ing se
t ran
ge. H
ere,
MAE
is the
aver
age a
bsolu
te er
ror a
nd th
e σ
value
deno
tes th
e stan
dard
devia
tion o
f the a
bsolu
te er
ror v
alues
for t
he te
st se
t data
. GTC
-Golb
raikh
and T
rops
ha’s
criter
ia
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3.2. Mechanistic Interpretation of the Best ModelThesuccessandacceptabilityofanyQSPRmodelreliesonitsmechanisticinterpretationassuggestedinOECDprinciple5.Thebestfour-variablemodelisconsideredforinterpretationinthepresentsection.Thecontributionofthedescriptors(Figure4)tothesolubilityofC60accordingtotheequation(4)ismaintainedthefollowingorder:PDI>X0sol>X4sol>VAR.
Figure 4. Descriptors’ contribution plot for the four-variable model
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3.3. Comparison with developed ModelInordertoevaluateourmodel’spredictiveability,acomparativeanalysiswasperformedwithexistingmodelsforC60solubilityinorganicsolvents(Liuetal.,2005;Toropovetal.,2007;Toropovetal.,2009;Petrovaetal.,2011;Pourbasheeretal.,2011;Ghasemi,Salahinejad,&Rofouei,2013;Xuetal.2016),andresultswerelistedinTable3.Comparingwithallmodels,Xuetal.(2016)andourpresentstudyconsideredhighestnumberofsolventsasdatapointsandlinearmethodisusedformodelgenerationinbothcases.Thoughalmostsimilarstatisticaldataobtainedfrombothmodels,currentstudyoutperformthemodelofXuetal.(2016)byusingmuchlessnumberofdescriptors.Thepresentmodelexplainswellthesolubilitycorrelationwithonlyfourinterpretabledescriptorsforsuchalargeanddiversedatasetwithhighacceptablestatisticalresult.
4. CoNCLUSIoN
ThisstudyshowsthatanapplicationoftheGA-MLRtechniqueemployingsimpleandtransparentdescriptorsyields reliable,predictiveand interpretablemodels.Althoughwehavecompared theoutcomeofonetosixvariablemodels,thebestperformanceisaccomplishedbythefour-variablemodel.Interestingly,onetothree-variablemodelscanbeexplainedbyinformationprovidedbysmallnumberofvariablesinthemodel,andforfivetosix-variablemodelsitisduetoattainsaturationofthecorrelationandpredictionpointalready.AmongallthedevelopedmodelsforsolubilitypredictionofC60 todate,currentmodelemployedhighestnumberoforganicsolventswithleastnumberofdescriptors providing satisfactory prediction results along with mechanistic interpretation. ThedevelopedmodelscanbeemployedproficientlyforfuturepredictionsoffullereneC60solubilityinvariousorganicsolventsalongwithdeepunderstandingofthisphenomenon.
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ReFeReNCeS
AntipinI.S.,ArslanovN.A.,PalyulinV.A.,KonovalovA.I.,&ZefirovN.S.(1991)Solvationtopologicalindex.Topologicaldescriptionofdispersioninteraction(inRussian).Doklady Akademii Nauk SSSR, 316,925–927.
Beck,M.T.,&Mándi,G.(1997).SolubilityofC.Fullerenes, Nanotubes and Carbon Nanostructures,5(2),291–310.doi:10.1080/15363839708011993
Bogatu,C.,&Leszczynska,D.(2016).TransformationofNanomaterialsinEnvironment:SurfaceActivationofSWCNTsduringDisinfectionofWaterwithChlorine.Journal of Nanotoxicology and Nanomedicine,1(1),45–57.doi:10.4018/JNN.2016010104
Cook,S.M.,Aker,W.G.,Rasulev,B.,Hwang,H.M.,Leszczynski,J.,Jenkins,J.J.,&Shockley,V.(2010).ChoosingsafedispersingmediaforC60fullerenesbyusingcytotoxicitytestsonthebacteriumEscherichiacoli.Journal of Hazardous Materials,176(1-3),367–373.doi:10.1016/j.jhazmat.2009.11.039PMID:19962827
Gharagheizi, F. R., & Alamdari, F. (2008). A molecular-basedmodel for prediction of solubility ofC60 fullerene in various solvents. Fullerenes, Nanotubes and Carbon Nanostructures, 16(1), 40–57.doi:10.1080/15363830701779315
Labute,P.A.(2000).Widelyapplicablesetofdescriptors.Journal of Molecular Graphics & Modelling,18(4-5),464–477.doi:10.1016/S1093-3263(00)00068-1PMID:11143563
Liu, H., Yao, X., Zhang, R., Liu, M., Hu, Z., & Fan, B. (2005). Accurate quantitative structure-propertyrelationshipmodeltopredictthesolubilityofC60invarioussolventsbasedonanovelapproachusingaleast-squaressupportvectormachine.The Journal of Physical Chemistry B,109(43),20565–20571.doi:10.1021/jp052223nPMID:16853662
McGowan,J.C.(1978).EstimatesofthePropertiesofliquids.Journal of Applied Chemistry and Biotechnology,28,599–607.doi:10.1002/jctb.5700280902
Petrova,T.,Rasulev,B.F.,Toropov,A.A.,Leszczynska,D.,&Leszczynski,J.(2011).ImprovedmodelforfullereneC60solubilityinorganicsolventsbasedonquantum-chemicalandtopologicaldescriptors.Journal of Nanoparticle Research,13(8),3235–3247.doi:10.1007/s11051-011-0238-x
Pourbasheer,E.,Riahi,S.,Ganjali,M.,&Norouzi,R.P.(2011).PredictionofsolubilityoffullereneC60invariousorganicsolventsbygeneticalgorithm-multiplelinearregression.Fullerenes, Nanotubes and Carbon Nanostructures,19(7),585–598.doi:10.1080/1536383X.2010.504952
Prylutskyy, Y. I., Yashchuk, V. M., Kushnir, K. M., Golub, A. A., Kudrenko, V. A., Prylutska, S. V., &Matyshevska, O. P. et al. (2003). Biophysical studies of fullerene-based composite for bionanotechnology.Materials Science and Engineering: C, 23(1-2),109–111.doi:10.1016/S0928-4931(02)00244-8
Roy, K., & Ambure, P. (2016). The double cross-validation tool for MLR QSAR model development.Chemometrics and Intelligent Laboratory Systems,159,108–126.doi:10.1016/j.chemolab.2016.10.009
Roy,K.,Ambure,P.,&Aher,R.(2017).HowimportantistodetectsystematicerrorinpredictionsandunderstandstatisticalapplicabilitydomainofQSARmodels?Chemometrics and Intelligent Laboratory Systems,162,44–54.doi:10.1016/j.chemolab.2017.01.010
Roy,K.,Das,R.N.,Ambure,P.,&Aher,R.B.(2016).Beawareoferrormeasures.FurtherstudiesonvalidationofpredictiveQSARmodels.Chemometrics and Intelligent Laboratory Systems,152, 18–33.doi:10.1016/j.chemolab.2016.01.008
Roy,K.,&Kar,S.(2015a).HowtoJudgePredictiveQualityofClassificationandRegressionBasedQSARModels.InZ.U.Haq&J.Madura(Eds.),Frontiers of Computational Chemistry(pp.71–120).Bentham;doi:10.2174/9781608059782115020005
Journal of Nanotoxicology and NanomedicineVolume 2 • Issue 1 • January-June 2017
42
Roy,K.,&Kar,S.(2015b).Importance of applicability domain of QSAR models. Quantitative Structure–Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment (pp.180–211).PA: IGIGlobal;doi:10.4018/978-1-4666-8136-1.ch005
Roy,K.,Kar,S.,&Ambure,P.(2015b).OnasimpleapproachfordeterminingapplicabilitydomainofQSARmodels.Chemometrics and Intelligent Laboratory Systems,145,22–29.doi:10.1016/j.chemolab.2015.04.013
Roy,K.,Kar,S.,&Das,R.N.(2015a).Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment.AcademicPress.
Roy,K.,Mitra, I.,Kar,S.,Ojha,P.,Das,R.N.,&Kabir,H. (2012).Comparativestudiesonsomemetricsfor external validation of QSPR models. Journal of Chemical Information and Modeling, 52(2), 396–408.doi:10.1021/ci200520gPMID:22201416
Semenov,K.N.,Charykov,N.A.,Keskinov,V.A.,Piartman,A.K.,Blokhin,A.A.,&Kopyrin,A.A.(2010).Solubility of light fullerenes in organic solvents. Journal of Chemical & Engineering Data, 55(1), 13–36.doi:10.1021/je900296s
Shunaev,V.V.,Savostyanov,G.V.,Slepchenkov,M.M.,&Glukhova,O.E.(2015).Phenomenonofcurrentoccurrence during themotion of aC60 fullerene on substrate-supported graphene. RSC Advances, 5(105),86337–86346.doi:10.1039/C5RA12202C
Sivaraman,N.,Srinivasan,T.,VasudevaRao,P.,&Natarajan,R. (2001).QSPRmodeling for solubilityoffullerene(C60)inorganicsolvents.Journal of Chemical Information and Computer Sciences,41(4),1067–1074.doi:10.1021/ci010003aPMID:11500126
Toropov,A.A.,Toropova,A.P.,Benfenati,E.,Leszczynska,D.,&Leszczynski,J.(2009).AdditiveInChI-basedoptimaldescriptors:QSPRmodelingoffullereneC60solubilityinorganicsolvents.Journal of Mathematical Chemistry,46(4),1232–1251.doi:10.1007/s10910-008-9514-0
Toropova,A.P.,Achary,P.G.R.,&Toropov,A.A. (2016).Quasi-SMILESfornano-QSARpredictionoftoxiceffectofAl2O3nanoparticles.Journal of Nanotoxicology and Nanomedicine,1(1),17–28.doi:10.4018/JNN.2016010102
Xu,X.,Yan,L.,Jia,Q.,Wang,Q.,&Peisheng,M.(2016).PredictingsolubilityoffullereneC60indiverseorganicsolventsusingnormindexes.Journal of Molecular Liquids,223,603–610.doi:10.1016/j.molliq.2016.08.085
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Lyudvig Petrosyan is theoretical physicist in condensed matter and quantum physics. His current position is a post-doctoral research associate and adjunct professor at the Physics department at Jackson State University, Mississippi, USA. He received his PhD degree in Condensed matter physics in 2002 from Yerevan State University, Armenia. Dr. Petrosyan has a diverse range of research experience in condensed matter physics, however his main topics of interest are, from a broad point of view, electronic and optical properties of low dimensional nanostructures and the understanding of resonant tunneling effects in quantum nanostructures. He has over 15 years of educator’s experience in the US and Armenia. He has published more than 40 research and review articles, including 2 textbooks. During last 2 years one of Dr. Petrosyan’s scientific interests is computational statistics and machine learning methods, particularly materials informatics and cheminformatics, including structure-activity relationship studies, dealing with biological activity and physico-chemical properties prediction, including nanoparticles and polymers. In computational statistics Dr. Petrosyan has close collaboration with North Dakota State University (USA), Ernest Mario School of Pharmacy at Rutgers University (USA) and The Focus Foundation (USA).
Supratik Kar has been a post-doctoral research associate at the Interdisciplinary Center for Nanotoxicity at Jackson State University, Mississippi, USA in Prof. Jerzy Leszczynski research group since April 2015. He has completed his BPharm. (Gold Medallist) (2008) and MPharm. (Gold Medallist) (2010) degree from Jadavpur University securing first position in both degrees. He has earned his PhD (2015) from the Department of Pharmaceutical Technology, Jadavpur University (Kolkata, India) under the guidance of Prof. Kunal Roy. He is a former visiting researcher at the University of Gdańsk (Gdansk, Poland) under the Marie Curie International Research Staff Exchange Scheme in Prof. Tomsz Puzyn’s group (http://nanobridges.eu/supratik-kar-ju-in-university-of-gdansk-ug-in-gdansk/). He has eight years of experience in QSAR and chemometric modeling studies. He researches a range of topics in structure-activity relationship studies, dealing with biological activity prediction of natural compounds, organic compounds and toxicity prediction of various chemicals, including nanoparticles. He is actively associated with modeling of power conversion efficient solar cell design through molecular modeling and quantum studies. He has published 41 research and review articles, 5 book chapters till date (http://orcid.org/0000-0002-9411-2091). He has also coauthored QSAR related books entitled “Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment” (Elsevier, 2015) and “A Primer on QSAR/QSPR Modeling: Fundamental Concepts” (Springer, 2015). His current h-index is 16 and i-10 index is 18 according to Scopus. He serves as an associate editor of the International Journal of Quantitative Structure-Property Relationships (IJQSPR) [IGI-Global publishers]. He has acted as a reviewer for many reputed journals like Molecular diversity (Springer), Nanoscale (RSC), Science of the Total Environment (Elsevier), Structural Chemistry (Springer), Energies (MDPI), Journal of Nanotoxicology and Nanomedicine (IGI).
Bakhtiyor Rasulev is a professor at Department of Coatings and Polymeric Materials (North Dakota State University). He received his PhD degree in Chemistry in 2002 from Uzbek Academy of Sciences. Dr. Rasulev’s scientific interests are in cheminformatics and structure-activity relationship studies, dealing with biological activity prediction, physico-chemical and toxicity prediction of chemicals, including nanoparticles and polymers. He is an author of many contributions devoted to QSAR modeling and quantum-chemical applications. Dr. Rasulev has close collaboration with the Instituto di Ricerche Farmacologiche Mario Negri (Italy), Jackson State University (USA), University of Zagreb (Croatia), Johns Hopkins University (USA) and etc. His accomplishments have been widely recognized. He is a permanent reviewer of more than 20 peer-reviewed journals. Dr. Rasulev has received many scholarships and awards, including Scholarship of Drew University (Residential School of Medicinal Chemistry, Madison, NJ), Young Investigators Award from Toxicological Division of ACS, award from CRDF Foundation, UNESCO Scholarship to visit the Institute of Desert Study of Ben-Gurion University, Israel.