Exploring enantioselective molecular recognition mechanisms with chemoinformatic techniques

Alberto Del Rio

Dipartimento di ScienzeFarmaceutiche, Universit� diModena e Reggio Emilia, Modena,Italy

Review

Exploring enantioselective molecular recognitionmechanisms with chemoinformatic techniques

A comprehensive review of chemoinformatic techniques and studies applied to thefield of enantioselective molecular recognition is presented. Several approachessuch as enantiophores/pharmacophore modelling, QSPRs, CoMFA and other insight-ful data mining procedures are discussed. The review focuses on the central role ofchemoinformatic approaches on the establishment of connections between avail-able experimental data, mainly HPLC separation data, and these algorithms thatdescribe properties of chiral molecules. The general overview of the aforementionedcalculations account for a use of these techniques as a valuable strategy to achievereliable prediction systems, infer the mechanisms of chiral recognition, generateinsight for the conception of new chiral receptors and corroborate and assist exper-imental techniques such as chiral LC. Moreover, it is pointed out that computermethods in this field promise a wide range of applications for both academia andindustry, ranging from enantioselective reactions, drug discovery and analysis ofhigh-throughput screenings, to analytical and semi-preparative separations or large-scale production of enantiopure compounds.

Keywords: Chemoinformatics / Chiral discrimination / Chirality descriptors / Chiral separation /CoMFA / Computational chemistry / CoMSIA / Data mining / Enantioselective chromatography /Enantioselective recognition mechanisms / LFER / LSER / QSER / QSERR / QSRR /

Received: November 30, 2008; revised: February 25, 2009; accepted: February 26, 2009

DOI 10.1002/jssc.200800693

1 Introduction

Over the last 30 years computational methods as well asimprovement of computer power and capabilities haveceaselessly evolved in all fields of modern science. Whilecomputer technology and improvement in software effi-cacy are far from being at a standstill, the use of com-puters in research, for instance in chemistry and biology,has created an additional set of competence that is nowa-days essential for many scientists. A meaningful exampleis provided by computational chemistry and chemoinfor-matics applications that represents scientific disciplines

still in full bloom [1]. This rapid development has fos-tered the spreading of computer-aided techniques and,as a consequence, the number of effective people whosework is supported by these approaches.

As in other fields of research, the chemistry of chiralcompounds has also captured the interest of theoreti-cians and chemoinformaticians. Since the 1980s therehas been a significant increase in the research and devel-opment of enantiomerically pure compounds [2]. This isprimarily due to the acceptance that enantiomers oftenhave different activities (biologic, metabolic pathways,therapeutic use, etc.). Because the intended fields of usage

Correspondence: Dr. Alberto Del Rio, Dipartimento di ScienzeFarmaceutiche, Universit� di Modena e Reggio Emilia, Via Cam-pi 183. 41100 Modena, ItalyEmail: [email protected]: +39-059-2055161

Abbreviations: ACE, angiotensin converting enzyme; AChE, ace-tylcholinesterase; ANN, artificial neural network; BPG NN, back-propagation neural network; CDCC, conformational dependentchirality code; CICC, conformational independent chiralitycode; CIP, Cahn-Ingold-Prelog; CoMFA, comparative molecularfield analysis; CoMSIA, comparative molecular similarity indi-ces analysis; CPG NN, counterpropagation neural network; CSP,chiral stationary phase; CTA, cellulose triacetate; CTPB, tris(p-methylbenzoyl)cellulose; CV, cross-validation; DACH-DNB, N,N9-

(3,5-dinitrobenzoyl)-1(R),2(R)-diaminocyclohexane; DHFR, dihy-drofolate reductase; 3HPP, N-alkylated 3-(3-hydroxyphenyl) pi-peridines; HPTLC, high pressure TLC; kNN, k-nearest neigh-bours; KNN-SOM, Kohonen neural network – self organizingmaps; LFER, linear free energy relationship; LHO, leave-half-out;LOO, leave-one-out; LSER, linear solvation energy relationship;LTO, leave-two-out; MD, molecular dynamics; MLR, multiple lin-ear regression; MM, molecular mechanics; PLS, partial leastsquares; QSAR, quantitative structure-activity relationship;QSER, quantitative structure-enantioselection relationship;QSPR, quantitative structure-property relationship; QSERR,quantitative structure-enantioselectivity retention relationship;RoC, robust Bayesian classifier; RSA, response surface analysis;SMB, simulated moving bed; SOM, self organizing maps; SVN,support vector machines

i 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.jss-journal.com

1566 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584

J. Sep. Sci. 2009, 32, 1566 –1584 Liquid Chromatography 1567

of chiral compounds include an extensive range ofresearch and development and control applications suchas pharmacokinetics, asymmetric synthesis, enzymaticresolution and simulated moving bed (SMB) technology,we are still assisting in an enormous demand forimproved enantioselective methods within the pharma-ceutical, agrochemical, as well as in the food and biotech-nology industries. Research and industrial involvementsin these fields are considerable and the overcoming ofexpensive and time-consuming trial and error experi-mental procedures is nowadays a truly absorbing con-text. Pharmaceutical research is the most representativeexample [3]. This is testified by the fact that, since the1990s, the development and marketing of racemic drugshave known a progressive drop, being the recentlyapproved drugs mainly enantiopure or achiral com-pounds [4–7].

In this context computer-based approaches are trigger-ing interest in establishing a valid support for both com-panies and academia. Many efforts have been made inthe last two decades through computer-basedapproaches and several computational procedures andalgorithms have been already applied to chiral recogni-tion studies [8–17]. Comprehensive reviews describingall these techniques have been written by Lipkowitz [8, 9,18, 19]. These techniques constitute practical and valua-ble utilities to obtain crucial information about themechanism of resolution of racemates. However, in spiteof reduced computational time, dealing with complexmolecular structures can give rise to technical difficul-ties when attempting to solve problems with molecularmechanics (MM), molecular dynamics (MD) or quantummechanics (QM) techniques. In many cases the detailed3D structure model of the chiral receptor remainsunknown or is rather complex, thus computational limi-tations may arise. Moreover these approaches oftenrequire a large amount of computation time that maynot be available or even justifiable vis-�-vis to the extraor-dinary expanding knowledge generated today by explan-atory experimental techniques such as chromatography,NMR, X-ray and so forth. De facto, while the success ofMM, MD and QM can be fully recognized only when fairlysmall molecular structures are involved, chemoinfor-matics procedures have attained a great importance dueto the availability of an increasing number of experimen-tal data [1, 20]. Chemoinformatic techniques can avoidcomplex calculations and take advantage of the informa-tion on the available data, for instance those obtainedwith chromatographic techniques. These issues are ofgreat importance in the context of the increased interestand availability of chiral separation techniques. In fact, iftoday 80–90% of the enantiomeric mixtures can be sepa-rated with HPLC techniques, the problem of how to trans-form these experimental data into utilizable knowledgeis still a big challenge.

This review will cover all the qualitative and/or quanti-tative chemoinformatic techniques in which ligand-selector interaction forces are not directly calculated. Aswe shall see, these approaches constitute an effectivetool to (i) circumvent difficulties arising from the molec-ular complexity of the chiral ligand and/or receptor; (ii)allow studying extended and diverse dataset of com-pounds; (iii) generate fast and practical predictive mod-els; (iv) infer the mechanisms of enantioselective recogni-tion. While the review principally concerns chiral chro-matographic techniques for the separation of pharmaco-logically active compounds, it is recognized that thesame principles and applications are important for her-bicides, pesticides, aroma/flavour compounds, odorants,dyes, pigments, liquid crystals, nonlinear optical materi-als, polymers and so forth [21].

2 Obtaining enantiopure compounds

Many industrial processes are dependent on the avail-ability of enantiopure compounds. Since the 1990s, thedevelopment and expansion of technologies followedthe rising demand for chiral compounds, not only fromdrug companies but also for agrochemical, food and bev-erage, diagnostic and research industries [2]. The varioustechniques used to obtain and characterize the absolutestereochemistry of enantiopure compounds are nowa-days known as chirotechnologies [3].

Enantiopure compounds may be obtained in severalways but each chirotechnology has its advantages anddrawbacks depending on the context of application [22].Among all the techniques available, often one of themost viable is the enantioselective chromatographythrough chiral selectors [21–23]. Enantioselective chro-matography is excellent for quickly obtaining both enan-tiomers, therefore, in pharmaceutical industry, is per-fectly suitable at the drug discovery or early developmen-tal stage. However, despite the possibility to setup SMBtechniques [24, 25], sometimes it is not competitive foruse on large scale as compared to other techniques. Thesuccess of chromatographic techniques is widely testi-fied by the increasing number of compounds separatedwhose data were published in the literature [21, 26]. Thisfact constitutes the main reason that underlined the pro-liferation of chemoinformatic studies on chiral separa-tion data over the last two decades.

2.1 HPLC techniques

Chiral HPLC separations can be achieved directly byusing chiral stationary phases (CSPs) where the chiralselector is chemically bound, coated or otherwise immo-bilized to the surface of the support material. In eithercase, the formation of transient diastereomeric com-


1568 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584

plexes between the analyte and the chiral discriminatingagent is being pursued. Various interactions, such ascharge transfer, hydrogen bonding interactions, dipolestackings, as well as differences in the stability of thesetransient complexes are decisive for enantiodiscrimina-tion. Different classifications of the CSPs were alreadydescribed in the literature and are still currentlyaccepted and used [8, 27, 28]. As it will be pointed out inSection 3, the possibility to carry out reliable theoreticalcalculations in the field of enantioselective recognitionwith HPLC techniques is mostly related to the complexityof the host selector. In this context a more suitable classi-fication of the chiral selector was provided by Roussel etal. [29]. In their definition CSPs are divided into two cate-gories: molecular selectors that are based on well-definedmolecule that bears in its structure the ensemble ofstructural features allowing the chiral discriminationand polymeric selectors whose structure is based on syn-thetic or naturally chiral macromolecule (Fig. 1). Thisclassification is particularly appropriate for describingcomputational approaches in this field because it canhighlight the major shortcomings of MM techniques ascompared to chemoinformatics. For instance, with amolecular host selector like Whelk-O1 (Fig. 1) one canafford insightful MM or MD calculations in which hostand guest interactions are directly computed [10, 16]. Onthe other hand such approaches would be time-consum-ing and potentially unreliable when polymeric CSPs areconsidered.

Therefore, according to today's computational capabil-ities in terms of hardware power and available algo-rithms, molecular CSPs allow detailed and rigorous cal-culation of the intermolecular host –guest interactionswith good reliability of the results. Conversely, the inter-

actions that can arise with polymeric chiral receptors(such as Chiralcel OD, Fig. 1 right) are too complicated tobe exhaustively calculated with present-day computerfacilities. In this case, a valuable alternative lies in calcu-lations that do not compute directly the host–guestinteractions, i.e. chemoinformatic techniques covered inthis review. From the experimental point of view it mustbe noted also that the cases in which complex polymericCSPs are used is more common than molecular onesbecause most of the chiral columns available to the end-user are nowadays constituted by complicated macromo-lecular chemical structures that offer several advantagesas compared to molecular receptors (e.g. the higher sam-ple loading capacity) [22].

2.2 HPLC separation data

In several cases, chiral HPLC is the only effective andrapid procedure to obtain enantiopure compounds with-out the need to set up laborious protocols. Since HPLC iswidely used as a preparative and analytical way to sepa-rate racemates, thousands of articles concerning thetopic are now available in literature. The most strikingexample of the progresses that have been done in thisfield is the amazing amount of chromatographic datathat are collected not only in the literature but alsowithin private companies [3]. A huge work aimed to col-lect all freely available data in the literature is carriedout since 1990 by Roussel and Piras in Marseille. Theirefforts gave rise to a proprietary database (Fig. 2), Chir-Base, which is today the most comprehensive moleculardatabase that gathers all chiral HPLC separations withtheir associated experimental data [28, 30, 31]. Nowadaysthis information system contains: 151000 chiral separa-


Figure 1. CSPs classification following Roussel's definition and field of application of in silico approaches.


tions, 49000 molecular structures and a multitude ofunpublished data.

3 Chemoinformatic approaches toenantioselection

The availability of chromatographic screening units andthe progressive expansion of combinatorial chemistryand high throughput screening (HTS) techniques con-tribute greatly to the data in the literature. In all cases,how to timely analyse this large amount of data rapidlyand convert it into utilizable information remains a con-siderable challenge for theoreticians and chemoinforma-ticians. Moreover, there are still major dilemmas in exist-ing theory concerning stereochemistry and, as we willsee, applications on enantioselective chromatographyare pioneering the research in this direction. As pointedout in the introduction, different computational meth-ods exist but relatively few have been practically appliedto uncover the mechanisms of enantioselective recogni-tion. Several straightforward problems that still need tobe fully understood include:

(i) how the stereoisomerism of ligands affects thehost –guest binding in biological targets such as proteinor enzymes and how these considerations can be pre-dicted in a reliable and comprehensive way.

(ii) the determination of whether a racemic mixturewill be separated in chiral separation techniques such aschromatography in given experimental conditions and

rationales behind the stronger –weaker binding of thevarious stereoisomers towards the chiral selector.

(iii) the test of new chemoinformatic and data miningtechniques for the treatment of large quantities of exper-imental data with consideration about the chirality ofthe compounds.

In this context, learning from experiments is the keystep for the building of models through inductive learn-ing procedures (Fig. 3). Models are then used for predic-tions and elucidation purposes that can be checked withnew experiments to have the models confirmed, rejectedor refined. The various techniques that have beenapplied in the field of enantioselective recognition willbe reviewed in the next paragraphs while an extensive


Figure 2. Screenshot of ChirBasedatabase. Thousands of freely avail-able data are published each yearon chiral HPLC separations and arecollected in ChirBase.

Figure 3. Inductive learning: chemoinformatic approaches toenantioselection.

1570 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584

overview of modern chemoinformatics techniques andtheir major aspects can be found in the book recentlypublished by Gasteiger and Engel [1].

3.1 Quantification of chirality and chiralitydescriptors

Several chirality descriptors and methods to classify chi-ral molecules have been proposed in the past and havebeen reviewed in 1998 by Mislow et al. [32, 36–38] andAvnir et al. [33–35]. A general overview of these descrip-tors that covers also the last period (1998 –2008) is givenin Table 1 and the following discussion will be mainlyfocused on these recent theoretical developments.

The first attempts to represent the chirality contentwere done by recognizing two distinct classes of meas-ures: the first in which a chiral object differs from anachiral reference structure and the second in which thedifference is measured between two chiral structures.These methods found no practical application until 1994when Seri-Levy et al. [39] applied chirality coefficients andsimilarity indices to assess the activity of a small datasetof chiral drugs. That pioneering work left the space inthe following years to the conception of further chiralitysensitive theoretical descriptors with no direct applica-tion to biological activities or enantioselectivities of chi-ral compounds [36, 40, 41]. Most of these methods pro-duce a descriptor in the form of real or integer valuesthat are usually the same for two enantiomeric struc-tures but with different signs. From 1997 the researchworks describing new theoretical development of chiral-ity sensitive descriptors accelerated sensibly and themajority of these studies showed also direct applicationmainly for the prediction of the biological activities andalso enantioselective reactions and chiral separations.Moreau [41] describes the quantitative and continuousmeasure of the chirality of the environment of an atomin a molecule for any given scalar atomic properties.Petitjean introduced root mean square chiral indexes inwhich the computed descriptors take a real valuebetween 0 and 1, zero being the value corresponding toan achiral compound perfectly superposed to its invertedimage [44, 45]. The chirality concept expressed by theseformulations is different from the method by Avnir andcoworkers [33, 34] which measures the minimal move-ment for points of an object to transform it into a shapeof desired symmetry. In particular, in Petitjean's [44, 45]approaches no achiral reference and no symmetryassumptions are needed to compute such indices. Itshould be noted however that no direct application ofthese descriptors appeared in the literature. Varioustopological indices incorporating a chirality sensitivefactor were described by Schultz and coworkers [40].Such a chirality factor was designed to take +1 or –1 inte-ger values following the R/S configurations. Even such

approach was tested on a small set of organic compoundswith no direct application for quantitative modelling.Vice versa Lukovits and Linert [49] applied on a dataset ofchromatographic separated amino acid isomers a topo-logical account of chirality through chiral function ful-filling the condition F(D) = –D(L), where L and D repre-sent the isomeric form of the amino acid. A relationshipbetween chirality content and stereoinduction wasdescribed by Lipkowitz and coworkers [46] while Randic[48] extensively discussed graphs which are theoreticaldescriptors of 2-D and 3-D chirality [48]. Despite the richliterature and the multiple efforts that have been donein the last decade for developing theories and algorithmsrelated to chiral measures, to our knowledge no practicalapplication of the above-mentioned methods wasreported to uncover the mechanism of enantioselectiverecognition or to predict enantioselective outcome inchiral separations or biological activities.

More recently, several studies introduced new con-cepts and chirality descriptors that were mainly appliedto reproduce biological activity but also to enantioselec-tive catalytic reactions. The first study, carried out byJuli�n-Ortiz et al. [42], attempted to correlate pharmaco-logical activity by using chiral indices derived frommolecular topology. Benigni et al. [47] proposed a methodto measure chirality of the molecules in a dataset bymeans of 3-D comparison of a structure with all theothers. Although the result of their study was done on asmall dataset of 16 dihydropyridine calcium channelinhibitors, they proposed a straightforward eudismicratio analysis, thanks to the usage of PCA techniques.Wildman and Crippen [55] extensively validated a newchirality metrics that was implemented in the DAPPER3-D quantitiy structure-activity relationship (QSAR) pro-gram. Their method has analogies with Moreau's contin-uous chirality measures but, as a major difference, theyassign chirality values on the basis of individual atomsrather than to the molecule as whole. Golbraikh et al. [50,51] contributed greatly from 2001 to the introduction ofseveral series of new chirality descriptors based on con-ventional topological descriptors of molecular graphs.These descriptors were extensively tested on differentdataset of biologically active compounds. The above-men-tioned studies try to correlate pharmacological activitieswhile a recent study of Zhang and Aires-de-Sousa [59]showed the application of physicochemical stereode-scriptors (PAS) of chiral centres for the prediction ofenantioselectivity in chemical reactions. In particularPAS approach is based on the reinterpretation of the CIPrules in terms of more meaningful physicochemicalproperties to be used as prioritizing rules. As we will seein Chapter 4 this idea has also found novel applicationsin chiral chromatography.

Only few studies involving chiral sensitive descriptorswere dedicated to the exploration of the enantioselective




Table 1. Overview of the molecular descriptors for the quantification of chirality

General description Numerical form of thedescriptor

Chemoinformatic applicationdescribed by authors (datasets used)

Modelling/Statisticaltechnique used and/orsuggested by authors

References Year

Review of chiral measuresbased on distance betweentwo enantiomorphs or betweenan achiral reference and achiroid.

Both discrete and continuousmeasures

– – [32, 34] 1992, 1995

Similarity indices andchirality coefficients

Real values (14 3HPP derivatives) MLR [39] 1994

Topological chiral moleculargraphs

Molecular graphs with notspecified values

Classification of topologicallychiral molecules

– [36] 1994

Chiral vertex and valenceweighted distance matricesdescriptors

Integer values – – [40] 1995

Atomic chirality and chiralityof the environment of an atom

Real values – – [41] 1997

Local vertex invariant chiral-topological indices

Fixed-length codes of realvalues

Modelling inhibitory activity(14 3HPP derivatives;80 barbiturates)

MLR, LDA [42] 1998

Continuous chiralitymeasures.

Real values Modelling inhibitory activity(6 ammonium inhibitors ofTrypsin; 27 agonist of D2-dopamine receptor)

Linear regression [34, 43] 1998

Root mean square chiral index Real values – – [44, 45] 1999Chiraphore derived fromcontinuous chirality measure

Real values Asymmetric Dies-Alderreaction (10 Lewis acid catalysts)


Chirality measures frommolecular similarity indices

Chiral sensitive principalcomponents with not specifiedvalues

Eudismic QSAR analysis(16 dihydropyridine calciumchannel inhibitors)

PCA [47] 2000

Graph theoretical descriptorsof 2D chirality

Fixed-length codes of integervalues

– – [48] 2001

Polinomial index chiralityfunction

Real values Prediction of chromato-graphic retention indices(9 D and L optical isomers)


Molecular graphs chiralitydescriptors

Several classes all with realvalues codes

Modelling inhibitory activity (78ecdysteroids; 31 steroids data set;66 histamine H1 receptor ligands;49 HIV-1 protease inhibitors)

kNN [50, 51] 2001, 2003

Radial distribution function-likechirality codes


Preference and prediction of enan-tiomeric excess in stereoselective re-actions (52 amino alcohols; reductionproducts of 50 ketones; library of 65enantioselective reactions), HPLCchiral separations (28 compoundsseparated on vancomycin CSP; 25compounds separated on a bianthra-cene-based CSP)

KNN-SOM [52 – 54] 2001, 2002,2005

Molecular conformation chiralitymetrics


Modelling inhibitory activity (68DHFR inhibitors; 30 ACE inhibitors;59 AChE inhibitors)

PLS [55] 2003

3D-Chiral quadratic indices Fixed-length codes of realvalues

Modelling inhibitory activity(32 perindoprilate analogues)

LDA [56] 2004

Atom pair chirality descriptors Several classes with notspecified values

Prediction of sandalwoododour intensities (44 a-campho-lenic derivatives), Prediction of am-bergris scent (98 ambergris fragrancecompounds)

MLR [57] 2005

Chirality-sensitive flexibilitydescriptors for 3+3D-QSAR


Modelling inhibitory activity(37 endomorphin analogues;38 PGF2 (a analogues)

PLS [58] 2006

Physicochemical stereode-scriptors of atomic chiral centres


Prediction of enantioselec-tivity in chemical reaction(48 chiral amino alcohols), Predictionof preferred enantiomer formation incatalysed reaction (86 enantiomericpairs of primary alcohols)

CPG NN, classificationtrees

[59] 2006

Inverse chirality descriptor. Rela-tive chirality of two enantiomers

Integer values Modelling chiral separation data(34 amino acid derivatives)

MLR [60] 2006

Atom-based 3D-chiral bilinear in-dices


Modelling inhibitory activity (32perindoprilate analogues; 14 3HPPderivatives; 31 steroids dataset)

MLR [61] 2007

Chiral enantiophores moleculardescriptors


HPLC chiral separations (51 com-pounds separated on Whelk-O1CSP)

Decision trees, BPG NN,kNN

[62] 2008

1572 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584

mechanism in chiral separations. Aires-de-Sousa and Gas-teiger [52] pioneered this field by introducing the so-called chirality codes that use the following mathematicalfunction to describe the stereochemical behaviour of amolecule:

f ðuÞ ¼XnA

i

XnB

j

XnC

k

XnD

l

sijkl exp �b u� eijkl� �2

h i

where u is the running variable of the function and themost important term Sijkl bear the information that dif-ferentiates two enantiomers following prioritizationrules based on a predefined atomic property. Fixed-length code chirality descriptors can be obtained fromthe above-mentioned formula by selecting in a choseninterval the values of such chirality function (Fig. 4). Aswe will see in Section 4.2 this approach is particularlysuited to build prediction systems by means of neuralnetworks and regression trees techniques, in particularfor the assessment of the absolute configuration to berelated to the order of elution in chiral separations [52,53, 63, 64]. More recently, other authors have also devel-oped chirality sensitive descriptors based on physico-chemical properties and they provided models for data-sets of chiral compounds [57, 62]. The applicative aspectof these studies in chiral separations will be described inchapter 4.

3.2 QSPR, QSER, QSRR, LSER and LFERapproaches

While classical quantitative structure–activity relation-ship (QSAR) approaches are mostly related to the applica-tion on biological activity data, the same principles havebeen extensively applied also in the field of chiral separa-tions. As outlined in the next chapter, several studiesmake use of different kinds of molecular descriptorswith the aim to build quantitative relationships with thedependent variable. From the chemoinformatic point of

view, various applications can be distinguished depend-ing on the predicted variable (Fig. 5). In chiral separationtechniques for instance one can assess:

(i) retention times (Fig. 5a) by carrying out the so calledquantitative structure–retention relationships (QSRR)

(ii) separation factor most commonly referred as a (Fig.5b) by carrying out the so called quantitative structure–enantioselectivity relationships (QSER)

(iii) absolute configurations related to the order of elu-tion of the stereoisomers (Fig. 5c).

Several model-building techniques have beenemployed to establish the above-mentioned relation-ships. Firstly and still widely used are multiple linearregressions (MLR) that allow to linearly relate the valuesof the molecular descriptors to the dependent variableby assigning the optimal coefficient to each independentvariable so that the response on the dependent variableis the one that better fits the data on a straight line.Many other different modelling, statistical and data min-ing techniques have been used to establish quantitativestructure-property relationships (QSPRs). Most notably,partial least squares (PLS), support vector machines(SVN), decision trees and more prominently, artificialneural network (ANN) [65, 66]. Different from MLR manyof these techniques are capable for modelling the data ina nonlinear fashion but sometimes this advantage isdown-compensated by the loss in interpretability of themodels and ANNs are a clear example of such behaviour[1, 65–67].

Another approach used concerns for linear solvationenergy relationships (LSER) [68]. This technique can beseen as a special case of QSPR in which a general relation-ship linking analyte retention factors or partition coeffi-cients to numerical measures of properties of the ana-lyte, mobile phase and stationary phase is pursued.Among the independent variables we can distinguish sol-ute descriptors such as molecular volume or the H-bondsolute acidity and system parameters that pertain to theretentive or chromatographic system, e.g. nature of the


Figure 4. Exemplification of the chirality code descriptors calculated for two enantiomers. The function f(u) is plotted against therunning variable u.


organic modifier or the tendency of the mobile and sta-tionary phase to interact with the solute through p elec-tron pairs [69–71]. Since the above-mentioned descrip-tors are not entirely the results of a purely theoretical cal-culation [72] it is convenient to distinguish LSER fromQSER or QSRR approaches in which the descriptors areoften fully retrieved through computational algorithms.In the context of enantioselective recognition linear freeenergy relationships (LFER) share the same principlesand assume the same signification of LSERs being theaim of LFER to gain insight into the molecular interac-tions that affect chiral separations and to elucidate dif-ferences in specific analyte–selector interactions thatare most important for retention or enantioselectivity[73].

3.3 CoMFA and CoMSIA

Comparative molecular field analysis (CoMFA) and com-parative molecular similarity index analysis (CoMSIA)are 3-D QSAR methods that revealed to be attractive tech-niques to study the behaviour of chiral molecules espe-cially when congeneric datasets of ligands have to bestudied [74–76]. The main idea beyond classical form ofCoMFA is to align the molecules of the dataset to aselected template structure or a predefined pharmaco-phore model. This alignment is generally done by match-ing the common scaffold moieties of the molecules ofthe dataset with the template that shows the same scaf-fold features. Then a grid is built around the aligned mol-ecules and van der Waals and Coulomb field potentialsare calculated for each point of the grid and for each mol-ecule of the dataset. The potentials calculated at the gridpoints of the space constitute the independent variablesthat are used in combination with statistical techniquessuch as PLS [67] to model the dependent variable that, inmost applications is the biological activity. The main dif-ference of CoMSIA techniques with respect to CoMFA isthat steric, electrostatic, hydrophobic, hydrogen bonddonor and hydrogen bond acceptor potentials aredescribed by using Gaussian functions [75–78]. There-fore CoMSIA plots in general highlight areas of the

ligands that favour or disfavour particular propertieswhile CoMFA plots indicate where ligands interact witha putative environment [76, 79]. Although a methodolog-ical comparison of CoMFA and CoMSIA reveals markedsimilarities, it should be recognized that the computa-tional models obtained in both cases show a certainamount of complementarities in terms of informationthat can be extracted.

Despite the popularity of these techniques, it has beenpointed out that the original method has several short-comings. For instance, the increasing challenges associ-ated with alignment as the molecular training setbecome larger or also the sensitivity of the results to thecharacteristics of the grid such as translations, orienta-tion and size [80, 81]. To circumvent these problems sev-eral approaches have been proposed to improve the reli-ability of the standard procedure [82–83] (GOLPE, Multi-variate Infometric Analysis, http://www.miasrl.com/gol-pe.htm). Since 3-D molecular fields of different stereo-isomers are different, the application of this methodol-ogy and its improvements, as we will see in Section 4.2,have been extensively and successfully established forthe analysis of chromatographic data of chiral com-pounds.

3.4 Pharmacophore modelling

A pharmacophore is a 3-D representation of a molecule'sfeatures required for binding a ligand to a receptor. Itprovides a mechanism by which these important fea-tures of the molecule (e.g. those used in drug–receptorinteractions) can be represented without the clutter ofthe other atoms in the molecule [84]. This notion pro-vides a powerful way to identify and compare the struc-tural features across a set of molecules. In this context,the term centre is often used to define the site pointswhich compose the pharmacophore. The concept ofpharmacophore is most commonly related to the poten-tial interaction that a ligand undergoes to a biologicaltarget. Because stereoisomers often show profound dif-ferences not only in terms of biological activities but alsofor metabolic fate and therapeutic use, several


Figure 5. Exemplification of the chromatographic data that can be used with chemoinformatic approaches: (a) retention times,(b) separation factors, (c) orders of elution.

1574 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584

approaches to incorporate chirality within pharmaco-phore description have recently appeared [7, 85–87].

Beyond these insightful studies some attempts havebeen done to readapt such concept to the enantioselec-tive activities. A chiraphore concept was established by Lip-kowitz and coworkers [46]. In their definition the chira-phore is the collection of atoms embedded in a moleculewhich manifests a chirality content according to the defi-nition of Avnir and coworkers [34, 43]. More recentlyRoussel et al. [28, 29] introduced the enantiophore concept(Fig. 6) as the basic combination of the common struc-tural features which are shared by a given group of mole-cules and assumed to be important for the enantioselec-tivity observed in chiral HPLC separations. As it will bedescribed in the next paragraph, several studies involv-ing the enantiophore concept were carried out recently[62, 90, 91].

3.5 Data mining approaches

Machine learning techniques provide the basis of datamining and are used to extract information from theraw data (e.g. in databases) so that it can be rationalizedand used for a variety of purposes. In general these tech-niques can be defined as the processes of exploration of

large amounts of data in search for consistent patterns,correlations and other systematic relationships [1, 66]. Sofar, and unexpectedly, scientists have been slightly inter-ested in such techniques in which chiral considerationswere taken into account. Some studies show data miningapplication with chiral libraries in drug discovery [7, 87],pharmacophore-based queries [90, 91], virtual screeningprotocols and molecular fingerprints [28, 92]. However itseems that the inclusion of chiral sensitive propertiesapplied to data mining techniques is still a rather newarea of research that is likely to have a major expansionin the forthcoming years.

Because of the success of chiral separation techniquessuch as HPLC and the subsequently increased availabilityof experimental data, some intelligent systemapproaches were applied in the field of enantioselectiverecognition. The first application to chiral chromatogra-phy was done by Stauffer and Dessy [93] by combiningsimilarity searching and expert system application topredict the best CSP to be used for a given chiral separa-tion. Bryant et al. [94, 95] extended this work by attempt-ing to automate the acquisition of the knowledge neededfor an expert system for HPLC enantioseparationobtained with Pirkle-like CSPs. Del Rio et al. [88] describeda data mining approach based on ligand enantiophoresinformation that was used to classify the CSPs in terms ofthe mechanism of enantioselective recognition (Fig. 7).Piras and coworkers [28, 96, 97] illustrated and evaluateddecision trees for CSP prediction by using ChirBase and,lately, they presented a screening study to explore theextended chiral pool derived from application of chiralchromatography [98].

4 Chemoinformatic applications onenantioselective chromatography

4.1 Assumptions and approximations

While several thermodynamic aspect should be takeninto account when one wants to fully understand themechanisms of chiral recognition with HPLC [99], mostof the assumptions and approximations that are cur-rently done with chemoinformatic techniques are alsothose applied in molecular modelling calculations. As anexample, the chemoinformatic prediction of enantio-selectivities assumes that absolute free energies are notcomputed, but rather determined as differential freeenergies. In this context also polar effects, solvationeffects and entropy differences are assumed to be equalfor the diastereomeric complexations equilibria. Lipko-witz' and Schurig' reviews offer valid overviews on theseaspects [8, 99]. In any case, these approximations andassumptions can be reasonably generalized also for che-moinformatic approaches. Nevertheless, a certain cau-tion should be used since the method, the model builder


Figure 6. (a) Graphical example of the enantiophore con-cept. A, D, L, R represent H-bond acceptor, H-bond donor,lipophilic region and aromatic region, respectively. Distancesare in �. (b, c) Example molecule in 2-D and 3-D that complywith the enantiophores description (Marvin, Chemaxon,http://www.chemaxon.com).


and more prominently the experimental technique aswell as the dataset of analytes and the conditions usedcan have an impact on these issues. For all these reasonsit is important to note that the collection of experimen-tal data for any chemoinformatic treatment shouldalways insure a high degree of consistency.

4.2 Discussion

In this section a discussion of the chemoinformatic stud-ies that have been carried out in the field of chiral separa-tion is presented. A summary of applications on enantio-selective separations is given in Table 2. When applica-ble, we report explicit indication of which kinds of pre-dictions are involved in the models, namely (i) retentionindices (RI); (ii) separation factors (SF); (iii) order of elu-tion (OE). It should be noted beforehand that several com-putational techniques are capable for predicting morethan one of the above-mentioned dependent variables ata time. Molecular modelling calculations are a clearexample of such capability while in the field of chemoin-formatics CoMFA-like approaches, since they intrinsi-cally model the chirality of each stereoisomer, are capa-ble of predicting both separation factors as well as theorder of elution.

Retention factors have been used extensively to carryout QSRRs. The usage of retention factors has been oftencoupled with the study of the enantioselectivity since, inchiral chromatography, the separation factor a is theratio between the retention factors of two enantiomers.In fact, a number of applications appeared in the litera-ture describing combined models in which both reten-tions and enantioselectivities were predicted. Manyauthors refer to these models to the QSERR standing forquantitative structure-enantioselectivity retention rela-tionships.

The idea of using retention factors to build QSRR wasfirstly applied in late 1992 by Kaliszan et al. [100, 101,152]. Their studies showed the usage of retention factorsto uncover the mechanisms of enantioselective recogni-tion for a series of benzodiazepines separated on a HSA-based CSP. These studies promoted an intense researchover all the last decade and resulted in a multiplicationof research that was somewhat depending on the experi-mental conditions used to build the dataset of com-pounds for the QSRR [105, 107, 108, 110–112, 153, 154].Reviews that cover QSRR and QSER applications in HPLCtechniques were written by Kaliszan in [155] andH�berger [156]. More recently, new studies have alsoappeared in this field [119, 132, 135, 157]. It should bementioned also that, despite not being fully consideredas chemoinformatic applications, several examples inthe literature describes approaches to model, interpretand optimize separation of enantiomers with HPLC andCLEC techniques through the usage of thermodynamicequilibria [138].

A common problem in the field of chiral separation isthat it can be very cost- and time-consuming to screenand select the most appropriate chiral selectors and,more importantly, understand whether a chiral discrim-ination can be achieved with given experimental condi-tions. With the aim to address such problems experimen-tal enantioselectivities, and in particular the separationfactor a obtained from chiral HPLC separations, havemore recently attracted scientists for use as a dependentvariable in chemoinformatic models. The increase ofinterest towards enantioselectivity has been fosteredwith no doubt by the intensification of studies thatinvolve chirality not only in the field of chiral separationbut, more prominently, in drug design and towards bio-logical targets. Starting from the year 2000, an intenseperiod of studies hallmarked the interest of the scientific


Figure 7. Qualitative classification of the CSPs fol-lowing the approach of Del Rio et al. by using ligandenantiophores information.

1576 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584


Table 2. Summary of chemoinformatic applications on enantioselective chromatography

Size and nature of theanalyte dataset

Chiral selector(chromatographictechnique)

Chemoinformaticapproach

Molecular descriptors/modelling technique

Predicted variables Validation ofthe model

Refe-rence

Year

RI SF OE CVs Externaltest set

21 1,4-benzodiazepinederivatives

HAS CSP (HPLC) QSERR Polarity, geometric andelectronic descriptors/MLR

Q Q [100,101]

1992

Racemic alkyl arylsulphoxides

DACH-DNB CSP(HPLC)

QSERR and CoMFA Electronic and stericparameters/PLS

Q Q [102] 1993

20 atropisomers CTA (HPLC); CTPB(HPLC)

QSPR: factorial designLiphophilicity parameter Q [103] 1994

19 a-substituted a-aryloxyacetic acid methyl esters

DACH-DNB CSP(HPLC)

LFER and CoMFA Liphophilicity, electronicand steric parameters/PLS

Q Q LOO [104] 1995

17 hydantoinscompounds; 15 a-aminoacids derivatives; 14 aryl-amides compounds

b-CD; a-CD; 1-(a-naphthyl)ethylamine(all with HPLC)

QSERR Connectivity, charge andgeometrical indices/MLR

Q Q [106] 1996

29 oxadiazolines com-pounds

DACH-DNB CSP(HPLC)

QSERR, LFER, CoMFA Steric, lihophilicity, molarrefractivity, Hammet con-stant and NMR derivedparameters/PLS

Q Q LOO [107] 1996

28a-alkyl arylcarboxylicacids

amylase tris(3,5-di-methylphenylcarba-mate) (HPLC)

QSRR Various nonempirical de-scriptors/MLR

Q [108] 1996

12 mexiletine derivatives Chiralpak AD (HPLC) QSRR Fragmental hydropho-bicity, dipole and othernonempirical descriptors/MLR

Q [109] 1996

24 atropisomers Chiralcel OJ (HPLC) QSPR: factorial designLiphophilicity parameter Q [100] 199629 aromatic acids andamides

Chiralpak AD; Chiral-pak AS; Chiralpak AR(all with HPLC)

QSERR Electronic, lipophilicity,electrostatic and dipole de-scriptors/MLR and ANN

Q Q LOO [110] 1997

5000 solutes 25 different CSPs(HPLC)

Data mining 15 empirical molecular de-scriptors/correspondenceanalysis

[96] 1997

12 chiral arylcarboxylicacids

HSA CSP (HPLC) QSERR Hydrophobicity and stericvolume/MLR

Q Q [111] 1997

17 enantiomeric amidesderivatives

Chiralpak AD; Chiral-pak AS; Chiralpak AR(all with HPLC)

QSERR Molecular connectivity,heat of formation and mo-lecular electrostatic poten-tial/MLR

Q Q T [112] 1997

9 1,4-disubstituted pipera-zione

Chiralcel OJ, AGP(all with HPLC)

QSRR Liphophilicity parameter Q [113] 1997

8 malathion derivatives Chiralcel OD-H(HPLC)

QSRR/QSAR Biological activity of thecompounds

Q [114] 1998

35 oxybutynin chloridecompounds

Pirkle-type CSP(HPLC)

QSRR Mass (m/z)/PLS and ANN Q [115] 1998

25 phenylalanine deriv-atives

a-Burke 2; ChiralpakAS (all with HPLC)

LSER Mobile phase modifiersproperties/MLR

Q [116] 1999

3,5-dinitrobenzoyl leucine 18 quinine-basedCSPs (HPLC)

CoMFA and dockingcalculations

Steric and electrostaticprobes/PLS

Q LOO [117,118]

2000

10 hydroxypropionic acidderivatives

HSA-based CSP(HPLC)

QSRR Hydrophobic, electronicand steric descriptors/MLRand PLS

Q LOO [119] 2000

13 b-blockers and trypto-phan derivatives

k- and sulphobutyl-ether k-carrageenanselectors (CE)

QSPR Mobility differences/linearregression

Q [120] 2000

23 chiral sulphoxides Cellulose and amy-lose tris-phenylcarba-mates CSPs (HPLC)

QSRR Connectivity, similarityand WHIM descriptors/MLR

Q LOO [121] 2000

2363 chiral compounds Chiralcel OD (HPLC) Data mining: mobilephase optimization

166 ISIS key descrip-tors/RoC and decision trees

[97] 2001

6 a-methyl-benzylamines Whelk-O CSP (HPLC) LSER Solvent modifiers proper-ties/MLR

Q [69] 2001

22 arylpropionic acids b-CD and two deri-vatized forms (CE)

QSER Size, shape, electronic,surface and thermo-dynamic descriptors/MLRand different ANNs

Q Q [122] 2001

14 phosphoroamido-thioates compounds

Sumichiral OA4700(HPLC)

QSRR Electronic, liphophilicityand p – p interactions/MLR

Q [123] 2001

42 chiral arylalkyl-carbinols

Four Pirkle-like CSPs(HPLC)

CoMFA and QSERR 2-D, 3-D, electronic, hydro-phobicity, steric and molec-ular paramenter descrip-tors/MLR, PLS and ANN

Q Q LOO,LHO,-L7O

[124] 2001

3000 molecular structures 18 different CSPs(HPLC)

Data mining 166 ISIS key descriptors/de-cision trees

[28] 2001



Table 2. Continued






Refe-rence

Year


14 alkyl substituted N-ar-ylthiazoline-2-(thi)oneatropisomers

Chiralcel OD-R;Chiralpak AD-RH(all with HPLC)

QSRR Lipophilicity index/MLR Q T [125] 2001

28 compounds; 25 com-pounds

Teicoplanin CSP;bianthracene-basedCSP (all with HPLC)

QSPR CICC and CDCC/KNN-SOM C [53] 2002

10 hydroxypropionic acidderivatives

HSA-based CSP(HPLC)

QSRR Hydrophobic, electronicand steric descriptors/MLRand PLS

Q T [126] 2002

51 drugs and phenoxypropionic acids

Riboflavin bindingprotein (HPLC)

QSERR Structural and liphophilic-ity descriptors/MLR

Q [127] 2002

30 aryl- and hetaryl-carbi-nols

ULMO (HPLC) CoMFA, CoMSIA,QSER

3D and normal coordinateeigenvalue descriptors/PLS

Q Q LOO 8 cmpds [128] 2003

11 N-terminal protectinggroups with oligoalaninepeptide

Three cinchonaalkaloid-derivedCSPs (HPLC)

QSER Peptide length, protectinggroup

T [129] 2003

Series of diastereomersand enantiomers

CDe derivatives(GC)

QSPR MD parameters C, T [130] 2003

15 phosphonate com-pounds

Chiralcel OD(HPLC)

QSER Electronic, volume, consti-tutional and dipole descrip-tors/MLR

Q [131] 2003

15 phosphonate com-pounds

N-(3,5-dinitroben-zoyl)-L-leucine CSP(HPLC)

QSRR Various thermodynamic,geometrical and electronicdescriptors/MLR

Q C [132] 2004

12 amino acids and alkylesters

18-crown-6-tetracar-boxylic acid selector(CE)

CoMFA Steric field energy andpartial positive surfacearea/PLS

Q LOO [133] 2004

11 amino acids N,N-dimethyl-S-phe-nylalanine-Cu(II)(CLEC)

QSPR Surface area descriptors C, T [13] 2004

15 organophosphonatecompounds

N-(3,5-dinitrobenzo-yl)-L-leucine CSP(HPLC)

QSRR Thermodynamic, geo-metrical and electronicdescriptors/MLR

Q T [132] 2004

72 000 chiral selector-selectand pairs

Different CSPs(HPLC)

Data mining study Exponential decay for chiraldifferential free energies

Q [134] 2004

Different compounds datasets

Chiral AGP;Chiralpak AD; Chiro-biotic T; Chiralcel OB;Chiralcel OD; Chiral-cel OJ; Whelk-O1(all with HPLC)

Data mining study Enantiophore descriptors C, T [88] 2005

18 chiral compounds (HPTLC) QSPR Chiral topological index Q LOO [135] 200519 solutes Three teicoplain-

based CSPs (HPLC)LFER Solute descriptors/MLR Q [136] 2005

28 compounds; 25 com-pounds

Teicoplanin CSP;bianthracene-basedCSP (all with HPLC)

QSPR CICC and CDCC/KNN-SOM T C [64] 2005

23 chiral sulphoxides Three amylose basedCSPs (HPLC)

QSER GRID descriptors/PLS andPCA

Q Q LOO,LTO

[137] 2006

Amino acid enantiomers Nucleosil Chiral-1(CLEC)

Modelling and inter-preting multiplechemical equilibria

[138] 2006

38 chiral drugs Chiralcel OD-RH;Chiralpak AD-RH;Chiralpak AS-RH;Chiralcel OJ-RH(all with HPLC)

Data mining study Chromatographic data [139] 2006

Fictive analyte Fictive CSP Theoretical investiga-tion with equilibriumdispersive model

CSP spacer length T T [140] 2006

15 N-3,5-dinitrobenzoylamino acids

6 quinine carba-mate-based CSP(HPLC)

QSERR Steric, electronic and li-phophilicity parameters/linear and nonlinearregressions

Q Q [141] 2006

50 hydantoins compounds Whelk-O1 (HPLC) QSER and HPLC reso-lution modelling

Several classes of descrip-tors/PCA, MLR, PLS and re-gression trees

Q LOO [142] 2006

176 compounds Whelk-O1 (HPLC) QSER Enantiophore descriptors/PLS

Q LOO 33 cmpds [89] 2006

16 thioisostere derivatives Penicillin G AcylaseCSP (GC)

QSER Steric and charge-transferdescriptors/MLR

T T C, T [143] 2006

1578 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584

community in the enantioselective recognition mecha-nisms. This, as seen above, was the result of several yearsof research on this topic that flowed in the year 1997 tothe major debate about the minimum requirement forchiral recognition and the discussion on the so calledthree-point interaction model [158].

Beyond QSERs, the first chemoinformatic techniquethat was extensively used in this field was the CoMFAthat we already introduced in Section 3.3. Altomare et al.[102, 104] pioneered such calculation in the field. Suzukiet al. [124, 159] and Fabian et al. [128] carried out differentstudies on Pirkle-like CSPs that also combined QSERRs byusing molecular descriptors derived from semi-empiricalAM1 calculations. Schefzick et al. [117, 118] appliedCoMFA technique in an original way to study the behav-iour of a series of quinine-based CSPs that separated ananalyte of 3,5-dinitrobenzoyl leucine. Two derived

approaches of CoMFA were carried out first by Montanariet al. [121, 160] by using molecular interaction field on adataset of chiral sulphoxides separated on three differ-ent amylose-carbamate-based CSPs and secondly by DelRio et al. [89] describing an application of the enantio-phore concept in 3-D-QSAR to the separation of 176 com-pounds on the Whelk-O1 CSP [161]. It is worth to notethat CoMFA has been also widely used to uncover themechanism of enantioselective catalysed reactions [162,163]. Beyond CoMFA techniques, other studies showedthe use of quantum-mechanical, physicochemical andatomic count molecular descriptors to predict thenumerical value of the separation factors or, as a discretepropriety, the outcome of the separation for given exper-imental conditions [124, 131, 147, 150]. Scientists havealso applied qualitative and data mining procedures byusing the enantioselectivities taken from chromato-


Table 2. Continued






Refe-rence

Year


34 solutes Three teicoplain-based CSPs (HPLC)

LFER Solute descriptors/MLR Q [144] 2006

67 hydantoins compounds Three urea-linkeda-arylalkilaminineCSPs (HPLC)

QSER Several classes of descrip-tors/PLS, MI, SVN regression

Q LOO [145] 2007

27 amino acids (S)-( – )a,a-di(2-naph-tyl)-2-pyrrolidineme-thanol CSP (CLEC)

QSPR Molecular surface area de-scriptors/classification mod-el

C 3 cmpds [146] 2007

64 compounds Primesep D; Supel-cosil LC-DP; Jordi Geldivinylbenzene; All-spere SAX; Chirobiot-ic TAG; Octadecyl(all with HPLC)

LSER Solute and systemparameters/MLR

Q [70] 2007

64 compounds Chirobiotic TAG;Chirobiotic V; Chiro-biotic T; ChirobioticR; Astec ODS (all withHPLC)

LSER Solute and systemparameters/MLR

Q [71] 2007

175 compounds Whelk-O1 (HPLC) QSER Atomic count descriptors/decision trees

C LOO,10-fold,5-fold

18 cmpds [147] 2008

32 enantiomeric mixtures S-benzyl-(R)-cysteine;S-trityl-(R)-cysteine(CLEC)

QSPR: descriptivestructure-separationrelationship

Properties and shape ofthe molecular surface area/PCA

C 8 cmpds [148] 2008

28 000 compounds Various CSPs (HPLC) Data mining study Drug and lead-like proper-ties, molecular fragments

[98] 2008

51 compounds Whelk-O1 (HPLC) QSPR Chiral enantiophores/deci-sion trees, kNN and BPG NN

C LOO,10-fold,5-fold

6 cmpds [62] 2008

32 amino acids derivatives S-benzyl-(R)-cysteine;S-diphenylmethyl-(R)-cysteine; S-trityl-(R)-cysteine (CLEC)

QSPR: descriptivestructure-separationrelationship

Atomic partition coefficientand solvent accessible area/partition tree or classifica-tion model

C [149] 2008

4 arylphenoxy acidsherbicides

Chirobiotic T (HPLC) QSER and modellingof the resolution

pKa and log P descriptors/PCA and RSA

Q [150] 2008

10 imidazo-quinazoline-dione derivatives

Quinine tert-butyl-carbamate-type CSP(HPLC)

QSERR Polarity of the mobilephase, temperature/linearregression

Q Q [151] 2009

C, classification model; Q, quantitative model; T, qualitative model; RI, retention indices; SF, separation factors; OE, order of elution


graphic experiments. Lindner and coworkers [129]described qualitative structure-enantioselectivity rela-tionships by studying the influence of different protect-ing groups on the separation of peptide enantiomers byHPLC using cinchona alkaloid-based CSP. The authors dis-cussed the evident relationships between the observedenantioselectivities and the structural element playing arole in intermolecular hydrogen-bonding between theanalyte and the chiral selector. A data mining applica-tion was carried out by Piras et al. [97] by using ChirBasedatabase (ChirBase: A molecular database for chiral chro-matography, Universit� “Paul C�zanne” Aix-Marseille III,http://chirbase.u-3mrs.fr). In their work they analysed alarge dataset of compounds well separated on ChiralcelOD CSP (a F 1.1) with the main purpose to evaluate differ-ent data analysis tools for mobile phase prediction usingmolecular key descriptors. Another example of data min-ing approach was carried out in the same group by DelRio et al. [88] in which the concept of enantiophore intro-duced in Section 3.4 was semi-quantitatively used to gaininsight into the molecular interacting fragments whichare supposed to play an important role in chiral recogni-tion processes for different commercially available CSPs(Fig. 7). These two latter works introduced also the idea ofdiscretizing the continuous values of the separation fac-tor so that different classes of compounds, e.g. separatedor nonseparated racemates, can be easily individuated.This concept has been recently adapted by Natalini et al.[148, 149] that carried out a descriptive structure-separa-tion relationship (DSSR) in which some set of physico-chemical descriptors are directly used with a partitiontree or a classification model towards the experimentalenantioselectivities. In a quantitative form also Del Rioand Gasteiger [147] applied this idea to devise a simplemethod based on the atomic counts around the chiralcentre to assess whether a compound will be separatedor not on a Whelk-O1 CSP. An interesting study by Kafriand Lancet [134] reported a probability rule for chiral rec-ognition in which the authors critically discussed thethree-point interaction model and depicted a more real-istic representation of the molecular contacts that mightmediate chiral recognition in terms of large number ofelementary interactions. In fact, despite the strictly geo-metric idea behind the three-point interaction modelwould allow one to describe the minimum requirementfor the enantioselective recognition, many of the above-mentioned chemoinformatic studies as well as molecu-lar modelling calculations show that the chiral discrimi-nation occurs with a complexity of mechanisms evenwhen relatively simple CSPs are considered [10, 134, 164,165]. This is especially true when protein receptor ormore complex chiral selectors are used [166 –168].Finally, it is important also to note that several authorsdemonstrated that the information encoding the chiral-ity of the various stereoisomers is not strictly needed to

understand whether a racemic mixture can be separatedor not with HPLC techniques. However such informationis essential for the assignment of the absolute configura-tion to the order of elution of the columns.

The assignment of the absolute configurations is a keyaspect that is not only important in the field of chiralchromatography but also constitutes a major challengewhen for example one wants to understand which stereo-isomer undergoes the strongest binding towards a pro-tein or an enzyme. While the Cahn-Ingold-Prelog (CIP)nomenclature is necessary to unambiguously identifythe stereochemistry of a product, no obvious connectionwith experiments such as the order of elution or the bio-logical activities can be confidently outlined. As seen inTable 2, many chemoinformatic efforts explain themechanism of enantioselective recognition in insightfuland original ways but lack consideration about the pref-erential binding of a particular enantiomer by usingsome chiral sensitive molecular descriptors. Unexpect-edly few studies have been done to explore the enantiose-lectivity of given compounds in chiral HPLC with consid-erations about the stereochemistry of the ligands. Themajor reason is undoubtedly the relative low availabilityof molecular descriptors that are able to encode chiralityof a molecule as compared to the vast diversity and topol-ogy of all the other chemical descriptors available to che-moinformaticians that are not chirality sensitive. Sec-ondly, it should be pointed out that the collection of con-sistent and reliable datasets for chemoinformatic model-ling can be a difficult task since experimental techniquessuch as chromatography may lead to non-negligible dis-crepancies even with slight changes of laboratory proto-cols are made or when the technique itself is not prop-erly used [169]. To our knowledge, the first attempt inthis direction was done by Booth et al. [112] in 1997 bystudying in a qualitative manner the elution order ofenantiomeric amides. Later on, Wolbach et al. [122] triedto correlate quantitatively the migration order in CE fora series of chiral arylproprionic acids. In that study theauthors concluded that it was not possible to calculatemeaningful enantiospecific descriptors and that,because of the inability to extract physical informationfrom the model equations, it was not clear how success-fully the regression approach made could be applied to aset of compounds with a high degree of structural diver-sity. As introduced in Section 3.1, from the year 2001Aires-de-Sousa et al. [52–54, 63] contributed greatly to thedevelopment of new chirality descriptors that could finda direct application in the field. The chirality codes con-ceived by them were successfully applied not only in chi-ral separations but also for the prediction of stereoselec-tive reactions. In particular they introduced two mainkinds of chirality codes that are either dependent or non-dependent on the conformation of the ligand. Bothapproaches were successfully applied on two different


1580 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584

datasets, the first constituted of chiral compounds sepa-rated on a teicoplanin CSP and the second on a bianthra-cene-based CSP for which, interestingly, some of the com-pounds of the dataset showed atropisomerism. The workon chirality codes was also extended with application ofthese descriptors with classification and regression trees[64]. We have also used such approach to test the predic-tions on a larger and chemically diverse dataset of struc-tures. We extracted from ChirBase the chromatographicdata for 212 compounds separated on Chiralcel-OD CSPwith a mobile phase of 90:10 hexane/2-propanol and inwhich the assignment of the absolute configuration wasknown experimentally (ChirBase: A molecular databasefor chiral chromatography, Universit� ,Paul C�zanne'Aix-Marseille III, http://chirbase.u-3mrs.fr). The trainingof a self-organizing map (SOM) neural network could beachieved with chirality codes so as the model could bereasonably used to discriminate the order of elution (Fig.8) also for large dataset and for polymeric CSPs chroma-tographic data [65] (SONNIA, Molecular Networks, http://www.molecular-networks.com/software/sonnia). In fact,as can be seen from the mapping of Fig. 8, neurons whosechirality codes yield preferentially first eluted enantio-mers are displayed in dark grey while in light grey arethose yielding preferentially second eluted enantiomers.Nevertheless, it should be acknowledged that by meansof a more straightforward statistical technique apt togenerate quantitative models such as logistic regression,we experienced difficulties in modelling the order of elu-tion with chirality codes. Zhang and Aires-de-Sousa [59]also introduced chirality descriptors by simple modifica-tion of the CIP rules. More recently Del Rio and Gasteiger[62] proposed a similar approach through the conceptionof descriptors based on topological distance around thestereogenic centre that counted R- and S-like configura-

tions of special atoms. These atoms, that constitute thepotential interacting points towards the chiral selector,are prioritized following an appropriate physicochemi-cal property that is used to assess R- or S-like handedness.A fixed length code of integer descriptors is obtained.The authors could successfully and quantitatively relatethese chiral sensitive descriptors with the experimentalorders of elution through a data modelling techniquesuch as decision trees. It should be noted here that, sincethe experimental assignment of the absolute configura-tions is in most of the cases a complicated and very frus-trating task because of time-consuming procedures, e.g.the creation of a crystallographic structure, a fast predic-tion of the order of elution related to the absolute config-uration of enantiomers is still much sought. We expectthat such approaches that aim to correlate the absoluteconfiguration with experimental data will constitute amajor endeavour of the research in the forthcomingyears not only in the field of chiral separations but alsoto predict the preference of binding towards biologicaltargets in drug discovery [3, 22].

4.3 Some suggestions for future chemoinformaticstudies

Chemoinformatic studies are often affected by severalmisleading practices that can mine the credibility of theresults. As can be seen from Table 2, in the field of enan-tioselective recognition earlier chemoinformatic modelsespecially on classical QSERR techniques were often notproperly validated. Here as follows we want to highlightsome major points that, in the authors' opinions, shouldbe followed when new chemoinformatic approaches inthis field need to be carried out:

4.3.1 Use of internal cross-validation (CV)procedures

When performing a K-fold CV, the original sample of ninstances is partitioned in K subsamples. Of the K sub-samples, a single subsample is retained as the validationdata while the remaining K–1 subsamples are used tobuild the model. The process is repeated K times so aseach of the K subsamples is used exactly once as a valida-tion set [66]. As an example, in leave-one-out CV the num-ber of K subsamples is set equal to the number of instan-ces n. An optimal CV scheme would contemplate forinstance the test of different CVs procedures, e.g. LOO,ten-fold, five-fold and so forth. The results of the CVs areessential to determine whether the models generated are(i) the results of a chance combination of independentvariables; (ii) overfitted towards the dependent variable;(iii) robust for predictions on future as-yet-unseen data. Itis commonly accepted that, for quantitative purposes, across-validated correlation coefficient q2 should be morethan 0.4 to generate predictive models. It should be


Figure 8. SOM obtained with chirality codes on a dataset of212 compounds separated on Chiralcel-OD CSP. Dark greysquares represent the activated neurons of the first elutedenantiomers while light grey those of the second eluted.


noted here that some of the studies in Table 2 providedpoor or no CV results.

4.3.2 Use of external validation dataset

An external validation dataset consists in data that in noway contribute for the model building (neither for thetraining set nor for the CV). The external test set shouldbe seen as a ,real test case’ in which the models that werebuilt on the basis of training set compounds will betested with not-yet-seen data. The size of the external val-idation set in respect to the training one can be judi-ciously set to 20% for small dataset, e.g. 25 compoundsfor the training set and 5 for the external test set, while itcan be lowered up to 10% when larger datasets are used,e.g. 150 compounds for the training set and 15 for theexternal test set.

4.3.3 Physicochemical interpretation of themodels

If the molecular descriptors are not able to explain ordescribe with clear evidence a physicochemical behav-iour then even a well correlated and validated model haslittle interest. This consideration especially applies whenthe model builder intrinsically gives few possibilities forthe interpretation of the results. For instance, ANN tech-niques have much less explanation capabilities on therole of the molecular descriptors used as compared toMLRs. In these cases, such loss of interpretability has tobe compensated by the increased physicochemical mean-ing of the molecular descriptors that feed these builders.

4.3.4 Dimensionality of the models

A MLR that is built by using ten molecular descriptors ona data set of 20 compounds cannot be seen as a valuablemodel. Conversely, a straightforward model is build onthe basis of few highly interpretable molecular descrip-tors. A personal opinion of the authors is that it is morereasonable to have a model built with few descriptorswith acceptable results, e.g. A0.7 for r2 and A0.4 for q2,instead of having a model built on the basis of huge set ofdescriptors with near-to-one r2 and low q2. In fact, this lat-ter behaviour is often symptomatic of overfitted modelswith poor prediction capabilities.

4.3.5 Quantitative versus qualitative models

A common prejudice is that a quantitative model shouldalways be preferred as respect to a qualitative one. This isnot forcedly true. A well-devised qualitative model in cer-tain cases can give much more effective and insightfulresults in respect to a risky quantitative model. Moreoverqualitative models are often the only way to retrieve use-ful information on chiral recognition mechanisms onlarge dataset of chemically diverse compounds.

4.3.6 Discrete versus continuous dependentvariable

A continuous dependent variable such as the separationfactor a of a chiral HPLC separation can be convenientlyconverted to a discrete value by setting a cut-off value.With this practice the arising chemoinformaticapproach becomes a classification problem instead of aprediction of a continuous variable. The use of discre-tized dependent variables should be seen as an interest-ing practice especially if one considers that potential dis-crepancies on the experimental data may induce lowerrobustness of the models in the case of continuousdependent variable. It is important not to confuse the dis-crete or continuous nature of the dependent variablewith the building of quantitative or qualitative models.For instance, a classification problem can be both quali-tative (see Natalini et al. [148]) or quantitative (see Del Rioand Gasteiger [147]) as well.

5 Concluding remarks

In the context of chiral separations the principal aim ofchemoinformatic techniques is to address concrete prob-lems that are at a time crucial for academia and indus-tries. The main objectives pursued in this direction are:

(i) inferring the mechanisms of enantioselective recog-nition by circumventing difficult and time-consum-ing calculations

(ii) build reliable prediction systems based on intuitiveand straightforward description of the moleculesinvolved

(iii) corroborate experimental procedures with ligand-based descriptors by probing the molecular patternsthat are believed to be responsible for enantioselec-tive behaviour

(iv) develop computational utilities, e.g. new chiralitydescriptors, that may be useful for the extraction ofthe experimental information from molecular data-bases.

The separation of racemates, in particular with chro-matographic techniques, seems to pioneer the chemoin-formatic research in chiral recognition mechanisms. Infact, while the experimentalists are critically faced withproblems such as understanding which absolute configu-ration of the enantiomers is to be related to a particularpeak of the chromatogram, the requirement for chemo-informatic tools that may be both predictive and alsoeasily interpretable has emerged. In particular it is argu-able that chemoinformatic applications to uncover theabsolute configuration will open new perspectives notonly in the field of chiral HPLC separations but also toboost the research towards effective and relevant chiral-


1582 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584

ity descriptors that are likely to be important in manyareas of chemistry and drug design where experimentaltechniques such as HTS and combinatorial chemistrygenerate a considerable amount of data that need to beinterpreted. Indeed, all the works done for exploring chi-ral recognition mechanisms in separation techniquesdescribed in this review, appear to open up new view-points for the understanding of chiral involvements alsoin other fields of chemistry and biology. Enantioselectivecatalysed reactions and, most importantly, the inclusionof chiral considerations for the development of chiraldrugs that act towards biological targets are just fewexamples of the large margin of applicability of chemoin-formatic techniques in these fields.

Part of the work described herein was carried out with the finan-cial support of the Alexander von Humboldt foundation and theItalian Association for Cancer Research (AIRC). ADR gratefullythanks Prof. C. Roussel, Dr. P. Piras, Prof. J. Gasteiger and theanonymous referees for the useful discussions and the constructiveremarks. Molecular Networks GmbH is also acknowledged for pro-viding software.

The authors declared no conflict of interest.

6 References

[1] Gasteiger, J., Engel, T., Chemoinformatics, Wiley-VCH, Weinheim2003.

[2] Maureen Rouhi, A., Chem. Eng. News 2002, 80, 43 – 50.

[3] McConnell, O., Bach, A., II, Balibar, C., Byrne, N., Cai, Y., Carter,G., Chlenov, M., Di, L., Fan, K., Goljer, I., He, Y., Herold, D., Kagan,M., Kerns, E., Koehn, F., Kraml, C., Marathias, V., Marquez, B.,McDonald, L., Nogle, L., Petucci, C., Schlingmann, G., Tawa, G.,Tischler, M., Williamson, R. T., Sutherland, A., Watts, W., Young,M., Zhang, M. Y., Zhang, Y., Zhou, D., Ho, D., Chirality 2007, 19,658 – 682.

[4] Caner, H., Groner, E., Levy, L., Agranat, I., Drug Discov. Today 2004,9, 105 – 110.

[5] Agranat, I. I., Caner, H., Drug Discov. Today 1999, 4, 313 – 321.

[6] Zhang, Y., Wu, D. R., Wang-Iverson, D. B., Tymiak, A. A., Drug Dis-cov. Today 2005, 10, 571 – 577.

[7] Beroza, P., Suto, M. J., Drug Discov. Today 2000, 5, 364 – 372.

[8] Lipkowitz, K. B., J. Chromatogr., A 2001, 906, 417 – 442.

[9] Lipkowitz, K. B., Acc. Chem. Res. 2000, 33, 555 – 562.

[10] Del Rio, A., Hayes, J. M., Stein, M., Piras, P., Roussel, C., Chirality2004, 16, S1 – S11.

[11] Roussel, C., Rafii, E., Del Rio, A., Vanthuyne, N., Biomed. Chroma-togr. 2005, 19, 434 – 438.

[12] Ragusa, A., Hayes, J., Light, M., Kilburn, J., Eur. J. Org. Chem. 2006,2006, 3545 – 3549.

[13] Natalini, B., Marinozzi, M., Sardella, R., Macchiarulo, A., Pellic-ciari, R., J. Chromatogr., A 2004, 1033, 363 – 367.

[14] Zhao, C., Cann, N. M., J. Chromatogr., A 2007, 1149, 197 – 218.

[15] Zhao, C., Cann, N. M., J. Chromatogr., A 2006, 1131, 110 – 129.

[16] Zhao, C. F., Cann, N. M., Anal. Chem. 2008, 80, 2426 – 2438.

[17] Alcaro, S., Gasparrini, F., Incani, O., Caglioti, L., Pierini, M., Vil-lani, C., J. Comput. Chem. 2007, 28, 1119 – 1128.



[20] Gasteiger, J., Chemom. Intell. Lab. Syst. 2006, 82, 200 – 209.

[21] Maier, N. M., Franco, P., Lindner, W., J. Chromatogr., A 2001, 906,3 – 33.

[22] Francotte, E. R., J. Chromatogr., A 2001, 906, 379 – 397.

[23] Ward, T. J., Baker, B. A., Anal. Chem. 2008, 80, 4363 – 4372.

[24] Fanali, S., J. Chromatogr., A 2009, 1216, 708 – 708.

[25] Rajendran, A., Paredes, G., Mazzotti, M., J. Chromatogr., A 2009,1216, 709 – 738.

[26] Roussel, C., Piras, P., Pure Appl. Chem. 1993, 65, 235 – 244.

[27] Wainer, I. W., Trends Anal. Chem. 1987, 6, 125 – 134.

[28] Roussel, C., Pierrot-Sanders, J., Heitmann, I., Piras, P., in: Subra-manian, G. (Ed.), Chiral Separation Techniques, Wiley-VCH, Wein-heim 2001, pp. 95 – 125.

[29] Roussel, C., Del Rio, A., Pierrot-Sanders, J., Piras, P., Vanthuyne,N., J. Chromatogr., A 2004, 1037, 311 – 328.

[30] Koppenhoefer, B., Graf, R., Holzschuh, H., Nothdurft, A., Trettin,U., Piras, P., Roussel, C., J. Chromatogr., A 1994, 666, 557 – 563.

[31] Koppenhoefer, B., Nothdurft, A., Pierrotsanders, J., Piras, P.,Popescu, C., Roussel, C., Stiebler, M., Trettin, U., Chirality 1993, 5,213 – 219.

[32] Buda, A. B., Heyde, T., Mislow, K., Angew. Chem., Int. Ed. 1992, 31,989 – 1007.

[33] Avnir, D., Hel-Or, P. G., Mezey, P. G., Symmetry and Chirality: Contin-uous Measures, Wiley, Chichester 1998.

[34] Zabrodsky, H., Avnir, D., J. Am. Chem. Soc. 1995, 117, 462 – 473.

[35] Zabrodsky, H., Peleg, S., Avnir, D., IEEE Trans. Pattern Anal. Mach.Intell. 1995, 17, 1154 – 1166.

[36] Liang, C. Z., Mislow, K., J. Math. Chem. 1994, 15, 245 – 260.

[37] Liang, C. Z., Mislow, K., J. Math. Chem. 1995, 18, 1 – 24.

[38] Weinberg, N., Mislow, K., J. Math. Chem. 1995, 17, 35 – 53.

[39] Seri-Levy, A., West, S., Richards, W. G., J. Med. Chem. 1994, 37,1727 – 1732.

[40] Schultz, H. P., Schultz, E. B., Schultz, T. P., J. Chem. Inf. Comput. Sci.1995, 35, 864 – 870.

[41] Moreau, G., J. Chem. Inf. Comput. Sci. 1997, 37, 929 – 938.

[42] de Julian-Ortiz, J. V., Alapont, C. D., Rios-Santamarina, I., Garcia-Domenech, R., Galvez, J., J. Mol. Graph. 1998, 16, 14 – 18.

[43] Keinan, S., Avnir, D., J. Am. Chem. Soc. 1998, 120, 6152 – 6159.

[44] Petitjean, M., J. Math. Phys. 1999, 40, 4587 – 4595.

[45] Petitjean, M., C. R. l'Academie. Sci., Ser. II Univers. 1999, 2, 25 – 28.


Alberto Del Rio studied chem-istry at the University of Mod-ena and Reggio Emilia andreceived a Master degree incomputer science and theoreti-cal chemistry at the Universityof Rennes I in 2002. Hereceived his PhD from the Uni-versity of Aix-Marseille III in2005 under the direction ofProf. Christian Roussel and Dr.Patrick Piras. He then moved tothe University of Erlangen-N�rnberg where he did post-

doctoral researches in the laboratory of Prof. Johann Gas-teiger. In 2007 he was shortly appointed as a research scien-tist at Molecular Networks in Erlangen. Since 2008 he isresearcher at the Department of Pharmaceutical Sciences ofthe University of Modena and Reggio Emilia.


[46] Gao, D. Q., Schefzick, S., Lipkowitz, K. B., J. Am. Chem. Soc. 1999,121, 9481 – 9482.

[47] Benigni, R., Cotta-Ramusino, M., Gallo, G., Giorgi, F., Giuliani,A., Vari, M. R., J. Med. Chem. 2000, 43, 3699 – 3703.

[48] Randic, M., J. Chem. Inf. Comput. Sci. 2001, 41, 639 – 649.

[49] Lukovits, I., Linert, W., J. Chem. Inf. Comput. Sci. 2001, 41, 1517 –1520.

[50] Golbraikh, A., Bonchev, D., Tropsha, A., J. Chem. Inf. Comput. Sci.2001, 41, 147 – 158.

[51] Golbraikh, A., Tropsha, A., J. Chem. Inf. Comput. Sci. 2003, 43, 144 –154.

[52] Aires-de-Sousa, J., Gasteiger, J., J. Chem. Inf. Comput. Sci. 2001, 41,369 – 375.

[53] Aires-de-Sousa, J., Gasteiger, J., J. Mol. Graph. 2002, 20, 373 – 388.

[54] Aires-de-Sousa, J., Gasteiger, J., J. Comb. Chem. 2005, 7, 298 – 301.

[55] Wildman, S. A., Crippen, G. M., J. Chem. Inf. Comput. Sci. 2003, 43,629 – 636.

[56] Ponce, Y. M., Diaz, H. G., Zaldivar, V. R., Torrens, F., Castro, E. A.,Bioorg. Med. Chem. 2004, 12, 5331 – 5342.

[57] Kovatcheva, A., Golbraikh, A., Oloff, S., Feng, J., Zheng, W., Trop-sha, A., SAR QSAR Environ. Res. 2005, 16, 93 – 102.

[58] Dervarics, M., Otvos, F., Martinek, T. A., J. Chem. Inf. Model. 2006,46, 1431 – 1438.

[59] Zhang, Q. Y., Aires-de-Sousa, J., J. Chem. Inf. Model. 2006, 46, 2278 –2287.

[60] Bauvais, C., Barbault, F., Zhu, Y., Petitjean, M., Fan, B. T., SARQSAR Environ. Res. 2006, 17, 253 – 264.

[61] Castillo-Garit, J. A., Marrero-Ponce, Y., Torrens, F., Rotondo, R., J.Mol. Graphics Modell. 2007, 26, 32 – 47.

[62] Del Rio, A., Gasteiger, J., QSAR Comb. Sci. 2008, 27, 1326 – 1336.

[63] Aires-de-Sousa, J., Gasteiger, J., Gutman, I., Vidovic, D., J. Chem.Inf. Comput. Sci. 2004, 44, 831 – 836.

[64] Caetano, S., Aires-De-Sousa, J., Daszykowski, A., Heyden, Y., Anal.Chim. Acta 2005, 544, 315 – 326.

[65] Gasteiger, J., Teckentrup, A., Terfloth, L., Spycher, S., J. Phys. Org.Chem. 2003, 16, 232 – 245.

[66] Witten, I. H., Frank, E., Data Mining: Practical Machine Learning Toolsand Techniques, Morgan Kaufmann, San Francisco, CA 2005.

[67] Wold, S., Sjostrom, M., Eriksson, L., Partial Least Squares Projectionsto latent Structures (PLS), Wiley, Chichester 1998.

[68] Vitha, M., Carr, P. W., J. Chromatogr., A 2006, 1126, 143 – 194.

[69] Blackwell, J. A., Waltermire, R. E., Stringham, R. W., Enantiomer2001, 6, 353 – 367.

[70] Berthod, A., Mitchell, C. R., Armstrong, D. W., J. Chromatogr., A2007, 1166, 61 – 69.

[71] Mitchell, C. R., Armstrong, D. W., Berthod, A., J. Chromatogr., A2007, 1166, 70 – 78.

[72] Abraham, M. H., Ibrahim, A., Zissimos, A. M., J. Chromatogr., A2004, 1037, 29 – 47.

[73] Famini, G. R., Wilson, L. Y., in: Lipkowitz, K. B., Boyd, D. B. (Eds.),Reviews in Computational Chemistry, Vol. 18 2003, pp. 211 – 255.

[74] Kubinyi, H., in: Schleyer, P. v. R., Allinger, N. L., Clark, T., Gas-teiger, J., Kollmann, P. A., Schaefer III, H. F., Schreiner, P. R.(Eds.), The Encyclopedia of Computational Chemistry, Vol. 1, JohnWiley & Sons, Chichester 1998, pp. 448 – 460.

[75] Cramer, R. D., Patterson, D. E., Bunce, J. D., J. Am. Chem. Soc. 1988,110, 5959 – 5967.

[76] Klebe, G., in: Kubinyi, H., Folkers, G., Martin, Y. C. (Eds.), 3D QSARin Drug Design, Springer, The Netherlands 2006, pp. 87 – 104.

[77] Klebe, G., Abraham, U., J. Comput. Aided Mol. Des. 1999, 13, 1 – 10.

[78] Klebe, G., Abraham, U., Mietzner, T., J. Med. Chem. 1994, 37,4130 – 4146.

[79] Bohm, M., Sturzebecher, J., Klebe, G., J. Med. Chem. 1999, 42, 458 –477.

[80] Brusniak, M. Y. K., Pearlman, R. S., Neve, K. A., Wilcox, R. E., J.Med. Chem. 1996, 39, 850 – 859.

[81] Cho, S. J., Tropsha, A., J. Med. Chem. 1995, 38, 1060 – 1066.

[82] Kubinyi, H., Quant. Struct. Act. Relat. 1994, 13, 285 – 294.

[83] Kubinyi, H., Quant. Struct. Act. Relat. 1994, 13, 393 – 401.

[84] G�ner, F. O., Pharmacophore Perception, Development, and Use inDrug Design, Internat'l University Line, La Jolla, CA 1999.

[85] Hopfinger, A. J., Wang, S., Tokarski, J. S., Jin, B. Q., Albuquerque,M., Madhav, P. J., Duraiswami, C., J. Am. Chem. Soc. 1997, 119,10509 – 10524.

[86] Langer, T., Hoffmann, R. D., Pharmacophores and PharmacophoreSearches, Wiley-VCH, La Jolla, CA 2006.

[87] Mason, J. S., Morize, I., Menard, P. R., Cheney, D. L., Hulme, C.,Labaudiniere, R. F., J. Med. Chem. 1999, 42, 3251 – 3264.

[88] Del Rio, A., Piras, P., Roussel, C., Chirality 2005, 17, S74 – S83.

[89] Del Rio, A., Piras, P., Roussel, C., Chirality 2006, 18, 498 – 508.

[90] Bremner, J. B., Castle, K., Griffith, R., Keller, P. A., Ridley, D. D., J.Mol. Graph. 2002, 21, 185 – 194.

[91] Chen, X., Rusinko, A., III, Tropsha, A., Young, S. S., J. Chem. Inf.Comput. Sci. 1999, 39, 887 – 896.

[92] Good, A. C., Cheney, D. L., J. Mol. Graph. 2003, 22, 23 – 30.

[93] Stauffer, S. T., Dessy, R. E., J. Chromatogr. Sci. 1994, 32, 228 – 235.

[94] Bryant, C. H., Adam, A. E., Taylor, D. R., Rowe, R. C., Chemom.Intell. Lab. Syst. 1996, 34, 21 – 40.

[95] Bryant, C. H., Lect. Notes Comput. Sci. 1997, 1297, 85 – 92.

[96] Roussel, C., Piras, P., Heitmann, I., Biomed. Chromatogr. 1997, 11,311 – 316.

[97] Piras, P., Roussel, C., Pierrot-Sanders, J., J. Chromatogr., A 2001,906, 443 – 458.

[98] Piras, P., Roussel, C., J. Pharm. Biomed. Anal. 2008, 46, 839 – 847.

[99] Schurig, V., Chirality 2005, 17, S205 – S226.

[100] Kaliszan, R., Noctor, T., Wainer, I., Chromatographia 1992, 33,546 – 550.

[101] Kaliszan, R., Kaliszan, A., Noctor, T. A. G., Purcell, W. P., Wainer,I. W., J. Chromatogr., A 1992, 609, 69 – 81.

[102] Altomare, C., Carotti, A., Cellamare, S., Fanelli, F., Gasparrini,F., Villani, C., Carrupt, P. A., Testa, B., Chirality 1993, 5, 527 –537.

[103] Roussel, C., Popescu, C., Chirality 1994, 6, 251 – 260.

[104] Carotti, A., Altomare, C., Cellamare, S., Monforte, A. M., Bet-toni, G., Loiodice, F., Tangari, N., Tortorella, V., J. Comput. AidedMol. Des. 1995, 9, 131 – 138.

[105] de Julian-Ortiz, J. V., Garcia-Domenech, R., Alvarez, J. G., Roca,R. S., Garcia-March, F. J., Anton-Fos, G. M., J. Chromatogr., A 1996,719, 37 – 44.

[106] Altomare, C., Cellamare, S., Carotti, A., Barreca, M. L., Chimirri,A., Monforte, A. M., Gasparrini, F., Villani, V., Cirilli, M., Mazza,F., Chirality 1996, 8, 556 – 566.

[107] Booth, T. D., Wainer, I. W., J. Chromatogr., A 1996, 737, 157 – 169.

[108] Booth, T. D., Wainer, I. W., J. Chromatogr., A 1996, 741, 205 – 211.

[109] Roussel, C., Popescu, C., Shibata, T., J. Chromatogr., A 1996, 722,177 – 188.

[110] Booth, T. D., Azzaoui, K., Wainer, I. W., Anal. Chem. 1997, 69,3879 – 3883.

[111] Andrisano, V., Booth, T. D., Cavrini, V., Wainer, I. W., Chirality1997, 9, 178 – 183.

[112] Booth, T. D., Lough, W. J., Saeed, M., Noctor, T. A. G., Wainer, I.W., Chirality 1997, 9, 173 – 177.

[113] Chilmonczyk, Z., Ksycinska, H., Mazgajska, M., Cybulski, J.,Kaliszan, R., J. Chromatogr., A 1997, 788, 81 – 85.

[114] Chilmonczyk, Z., Ksycinska, H., Polec, I., J. Chromatogr., B 1998,720, 65 – 69.


1584 A. Del Rio J. Sep. Sci. 2009, 32, 1566 – 1584

[115] Nord, L. I., Jacobsson, S. P., Chemom. Intell. Lab. Syst. 1998, 44,153 – 160.

[116] Blackwell, J. A., Stringham, R. W., Xiang, D., Waltermire, R. E., J.Chromatogr., A 1999, 852, 383 – 394.

[117] Schefzick, S., Lammerhofer, M., Lindner, W., Lipkowitz, K. B.,Jalaie, M., Chirality 2000, 12, 742 – 750.

[118] Schefzick, S., Lindner, W., Lipkowitz, K. B., Jalaie, M., Chirality2000, 12, 7 – 15.

[119] Andrisano, V., Bertucci, C., Cavrini, V., Recanatini, M., Cavalli,A., Varoli, L., Felix, G., Wainer, I. W., J. Chromatogr., A 2000, 876,75 – 86.

[120] Beck, G. M., Neau, S. H., Holder, A. J., Hemenway, J. N., Chirality2000, 12, 688 – 696.

[121] Montanari, C. A., Cass, Q. B., Tiritan, M. E., de Souza, A. L. S.,Anal. Chim. Acta 2000, 419, 93 – 100.

[122] Wolbach, J. P., Lloyd, D. K., Wainer, I. W., J. Chromatogr., A 2001,914, 299 – 314.

[123] Huang, J., Chen, H., Gao, R., Wang, Q., Chen, R., Sci. China Ser. B:Chem. 2001, 44, 147 – 153.

[124] Suzuki, T., Timofei, S., Iuoras, B. E., Uray, G., Verdino, P.,Fabian, W. M. F., J. Chromatogr., A 2001, 922, 13 – 23.

[125] Roussel, C., Bonnet, B., Piederriere, A., Suteu, C., Chirality 2001,13, 56 – 61.

[126] Andrisano, V., Gotti, R., Recanatini, M., Cavalli, A., Varoli, L.,Bertucci, C., J. Chromatogr., B 2002, 768, 137 – 145.

[127] Calleri, E., De Lorenzi, E., Siluk, D., Markuszewski, M., Kaliszan,R., Massolini, G., Chromatographia 2002, 55, 651 – 658.

[128] Fabian, W. M., Stampfer, W., Mazur, M., Uray, G., Chirality 2003,15, 271 – 275.

[129] Czerwenka, C., Lammerhofer, M., Lindner, W., J. Sep. Sci. 2003,26, 1499 – 1508.

[130] Zenkevich, I. G., Kostikov, R. R., Russ. J. Org. Chem. 2003, 39,1057 – 1063.

[131] Du, W., Yang, G. S., Wang, X. Q., Yuan, S. L., Zhou, L., Xu, D., Liu,C. P., Talanta 2003, 60, 1187 – 1195.

[132] Yang, G. S., Yuan, S. L., Lin, X. J., Qi, Z. N., Liu, C. B., Aboul-Enein,H. Y., Felix, G., Talanta 2004, 64, 320 – 325.

[133] Park, H., Choi, Y., Lee, W., Kim, K., Electrophoresis 2004, 25,2755 – 2760.

[134] Kafri, R., Lancet, D., Chirality 2004, 16, 369 – 378.

[135] Yang, C., Zhong, C., QSAR Comb. Sci. 2005, 24, 1047 – 1055.

[136] Lokajova, J., Tesarova, E., Armstrong, D. W., J. Chromatogr., A2005, 1088, 57 – 66.

[137] Montanari, M. L. C., Cass, Q. B., Leit¼o, A., Andricopulo, A. D.,Montanari, C. A., J. Chromatogr., A 2006, 1121, 64 – 75.

[138] Sanaie, N., Haynes, C. A., J. Chromatogr., A 2006, 1132, 39 – 50.

[139] Matthijs, N., Maftouh, M., Heyden, Y. V., J. Chromatogr., A 2006,1111, 48 – 61.

[140] Szabelski, P., Kaczmarski, K., J. Chromatogr., A 2006, 1113, 74 – 83.

[141] L�mmerhofer, M., Franco, P., Lindner, W., J. Sep. Sci. 2006, 29,1486 – 1496.

[142] Caetano, S., Vander Heyden, Y., Chemom. Intell. Lab. Syst. 2006, 84,46 – 55.

[143] Massolini, G., Fracchiolla, G., Calleri, E., Carbonara, G., Tempo-rini, C., Lavecchia, A., Cosconati, S., Novellino, E., Loiodice, F.,Chirality 2006, 18, 633 – 643.

[144] Kalikova, K., Lokajova, J., Tesarova, E., J. Sep. Sci. 2006, 29, 1476 –1485.

[145] Caetano, S., Krier, C., Verleysen, M., Heyden, Y. V., Anal. Chim.Acta 2007, 602, 37 – 46.

[146] Natalini, B., Sardella, R., Macchiarulo, A., Natalini, S., Pellic-ciari, R., J. Sep. Sci. 2007, 30, 21 – 27.

[147] Del Rio, A., Gasteiger, J., J. Chromatogr. A 2008, 1185, 49 – 58.

[148] Natalini, B., Macchiarulo, A., Sardella, R., Massarotti, A., Pellic-ciari, R., J. Sep. Sci. 2008, 31, 2395 – 2403.

[149] Natalini, B., Sardella, R., Macchiarulo, A., Pellicciari, R., J. Chro-matogr., B 2008, 875, 108 – 117.

[150] Rosales-Conrado, N., Le�n-Gonz�lez, M., P�rez-Arribas, L., Polo-D�ez, L., D'Orazio, G., Fanali, S., Chromatographia 2008, 67, 527 –533.

[151] Gyimesi-Forr�s, K., Maier, N. M., K�k�si, J., Gergely, A., Lindner,W., Chirality 2009, 21, 199 – 207.

[152] Kaliszan, R., Noctor, T. A., Wainer, I. W., Mol. Pharmacol. 1992,42, 512 – 517.

[153] Azzaoui, K., Morin-Allory, L., Chromatographia 1996, 42, 389 –395.

[154] Azzaoui, K., Morin-Allory, L., Chromatographia 1995, 40, 690 –696.

[155] Kaliszan, R., J. Chromatogr., B 1998, 715, 229 – 244.

[156] H�berger, K., J. Chromatogr., A 2007, 1158, 273 – 305.

[157] Mitchell, C. R., Benz, N. J., Zhang, S., J. Chromatogr., B 2008, 875,65 – 71.

[158] Booth, T. D., Wahnon, D., Wainer, I. W., Chirality 1997, 9, 96 –98.

[159] Suzuki, T., Ishida, M., Fabian, W. M. F., J. Comput. Aided Mol. Des.2000, 14, 669 – 678.

[160] Montanari, M. L. C., Cass, Q. B., Montanari, C. A., Chromatogra-phia 2000, 51, 722 – 726.

[161] Pirkle, W. H., Welch, C. J., Lamm, B., J. Org. Chem. 1992, 57,3854 – 3860.

[162] Funar-Timofei, S., Suzuki, T., Paier, J. A., Steinreiber, A., Faber,K., Fabian, W. M. F., J. Chem. Inf. Comput. Sci. 2003, 43, 934 – 940.

[163] Paier, J., Stockner, T., Steinreiber, A., Faber, K., Fabian, W. M., J.Comput. Aided Mol. Des. 2003, 17, 1 – 11.

[164] Topiol, S., Sabio, M., J. Am. Chem. Soc. 1989, 111, 4109 – 4110.

[165] Topiol, S., Sabio, M., Enantiomer 1996, 1, 251 – 265.

[166] Sundaresan, V., Abrol, R., Protein Sci. 2002, 11, 1330 – 1339.

[167] Okamoto, Y., Yashima, E., Angew. Chem., Int. Ed. 1998, 37, 1020 –1043.

[168] Kano, K., J. Phys. Org. Chem. 1997, 10, 286 – 291.

[169] Roussel, C., Vanthuyne, N., Serradeil-Albalat, M., Vallejos, J. C.,J. Chromatogr., A 2003, 995, 79 – 85.


Exploring enantioselective molecular recognition mechanisms with chemoinformatic techniques

Documents

Transcript of Exploring enantioselective molecular recognition mechanisms with chemoinformatic techniques