DOI: 10.1002/minf.201000055

In Silico Receptorome Screening of Antipsychotic DrugsDavid Vidal[a] and Jordi Mestres*[b]

1 Introduction

Antipsychotic drugs are a group of psychoactive small mol-ecules used mainly in the treatment of schizophrenia butapplied also in other conditions, such as bipolar disordersand Tourette’s syndrome.[1–4] Chlorpromazine was the firstdrug developed with specific antipsychotic action fromwhich a first generation of antipsychotics (FGAs), also re-ferred to as typical antipsychotics, followed.[5] However,they were soon revealed to be ineffective against the nega-tive symptoms of psychosis and to cause extrapyramidalside-effects (EPS), including dyskinesias and Parkinsonism.[6]

This motivated the development of a second generation ofantipsychotics (SGAs), also named atypical antipsychotics.[7]

Unfortunately, it has been shown that some SGAs can stillcause EPS, although to a lower degree and at higher dosesthan FGAs,[8] and may give rise to other side effects, includ-ing weight gain, diabetes mellitus, hyperglycemia, dyslipi-demia and possible cardiovascular complications.[9]

It seems thus clear that after sixty years of antipsychoticdrug discovery the underlying pharmacology and mecha-nism of action leading to therapeutic efficacy, but also re-sponsible for the wide spectrum of adverse drug reactionsobserved, remain to be fully understood.[10] In this respect,the development of antipsychotics has been traditionallybased on blocking the action of dopamine at its receptors,thus decreasing the chemical signal that drives psychoticbehaviour. Accordingly, both FGAs and SGAs are dopamineantagonists and are thus effective in treating the positivesymptoms of psychosis. However, it was recently observedthat the affinity profile of antipsychotics extends farbeyond dopaminergic targets and encompass many mono-amine G protein-coupled receptors (GPCRs).[11] The mainmessage emerging from these data is that therapeutic effi-cacy, and contextual adverse drug reactions, are often the

direct consequence of complex pharmacologies more thanspecific and selective interactions with isolated targets.[12]

Under this scenario, if one would have had the capacityto anticipate in silico the target profile of antipsychotics inits full extent, one could have influenced the developmentof many of them. With the increasing availability of bothpharmacological data for ligands and structural data forproteins, substantial progress has been made in recentyears towards predicting in silico the biological targetsagainst which small molecules might interact.[13] Indeed,several studies have demonstrated the applicability ofligand-based approaches to identify novel targets forknown drugs that were later confirmed experimentally.[14–17]

Likewise, structure-based approaches allowed also for de-tecting the target for some compounds in a focused li-brary[18] and to discover three targets associated with con-stituents of a medicinal plant.[19] More recently, in silicotarget screening was used to suggest the targets againstwhich selected compounds from a chemical library shouldbe tested, leading to the identification of novel antagonistsfor all members of the adenosine receptor family.[20]

Abstract : The recent availability of a complete interactionmatrix between 13 antipsychotic drugs and 34 protein tar-gets (Roth et al. Nat. Rev. Drug Discov. 2004, 3, 353–359)allows to assess the performance of computational meth-ods on their ability to anticipate the entire affinity profile ofdrugs across multiple targets. The analyses reveal that ourcurrent implementations, based on the similarity of drugsagainst a reference set of small molecules for which phar-

macological data is available in the public domain, are ableto predict 65 % of the 442 affinities within 1-log unit error,with a level of precision above 92 %. In spite of the relative-ly small scale of this validation study, the results are indica-tive that in silico receptorome screening of drugs offers anefficient and cost-effective complement to in vitro screen-ing.

Keywords: Chemogenomics · Polypharmacology · Chemical biology · Drug repurposing · High-throughput screening

[a] D. VidalChemotargets S. L.Passeig de Circumval.laci� 8, 08003 Barcelona, Catalonia, Spain

[b] J. MestresChemogenomics Laboratory, Research Program on Biomedical In-formatics (GRIB), Institut Municipal d’Investigaci� M�dica & Uni-versity Pompeu FabraParc de Recerca Biom�dica, Doctor Aiguader 88, 08003 Barcelo-na, Catalonia, Spainfax: + 34 93 3160550*e-mail : jmestres@imim.es

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In spite of these successful examples, a comprehensiveassessment of the true ability of those approaches to antici-pate the entire profile of target affinities for a given com-pound is still missing. The recent publication of the com-plete profile of affinities between a set of antipsychoticdrugs and a panel of receptors offers now the possibility toreach this level of validation.[21] Accordingly, the aim of thiscontribution is to evaluate some of our own implementa-tions for predicting the affinity profile of compounds overmultiple targets on its potential to reproduce the resultsobtained from the in vitro receptorome screening of anti-psychotic drugs.

2 Databases and Methods

2.1 Complete Interaction Matrix

Complete affinity (Ki) profiles of 13 antipsychotic drugs for34 protein targets were recently made publicly available.[21]

The list of drugs included six representatives of first genera-tion antipsychotics (namely, chlorpromazine, fluphenazine,haloperidol, loxapine, thioridazine, and thiothixene) andseven representatives of second generation antipsychotics(namely, clozapine, olanzapine, quetiapine, risperidone, zo-tepine, ziprasidone, and aripiprazole). The list of targetscontained 10 serotonin receptors (5-HT1A, 5-HT1B, 5-HT1D, 5-HT1E, 5-HT2A, 5-HT2C, 5-HT3, 5-HT5, 5-HT6, and 5-HT7), 7 adre-noceptors (a1A, a1B, a2A, a2B, a2C, b1, and b2), 5 dopamine re-ceptors (D1, D2, D3, D4, and D5), 5 muscarinic receptors (M1,M2, M3, M4, and M5), 4 histamine receptors (H1, H2, H3, andH4), and 3 neurotransmitter transporters (SERT, NET, andDAT). The distribution of affinity values for the 442 drug-target interactions across the different affinity ranges is pre-sented in Figure 1.

2.2 Annotated Chemical Libraries

Four public databases of chemical structures annotatedwith bioactivity data were used in this work: ChEMBLdb,[22]

PDSP,[23] BindingDB,[24] and IUPHARdb.[25] Every small mole-cule present in these databases with affinity data (Ki) to anyof the 34 protein targets listed above was extracted. If asmall molecule had different pKi (�log Ki) values for exactlythe same target interaction (either within the same data-base or across databases), an average pKi value was as-signed. In this respect, it is worth stressing that a systemat-ic analysis of the variations found in compounds with mul-tiple affinity data for the same target revealed an averagestandard deviation of 0.5 log units, irrespective of the affini-ty range. In total, 21,100 unique compounds and 59,720unique compound-target interactions were extracted fromthose public sources. The distribution of the total numberof compounds with affinity data for each one of the 34 tar-gets is presented in Figure 2. As can be observed, all tar-gets are represented at least by 200 compounds, with 10targets having more than 2,000 compounds and 6 targetscontaining less than 500 compounds.

2.3 Molecular Descriptors and Similarity

Our in silico target screening approach relies on the as-sumption that the set of bioactive ligands collected for agiven target provides a complementary description of thetarget from a ligand perspective. In order to be able to pro-cess this information efficiently, chemical structures need tobe encoded using some sort of mathematical descriptors.In this work, three types of two-dimensional descriptorswere used, namely, PHRAG,[26] FPD,[26] and SHED,[26, 27] eachone of them characterising chemical structures with a dif-ferent degree of fuzziness and thus complementing each

Figure 1. Distribution of the affinity values for all drug-target interactions across different affinity ranges.

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Full Paper D. Vidal, J. Mestres

other in terms of structural congenericity and hopping abil-ities. Detailed definitions can be found elsewhere.[26, 27]

Pharmacophoric fragments (PHRAG) are all possiblefixed-length segments of five atom-features that can be ex-tracted from the topology of a molecule. PHRAG are ob-tained through direct fragmentation of the chemical graphafter mapping each non-hydrogen atom onto one of thefollowing seven pharmacophoric features, namely, hydro-phobic, aromatic, hydrogen-bond donor, hydrogen-bondacceptor, amphoteric, positively charged or negativelycharged. Each PHRAG is also given a score that depends onthe relative presence of polar and apolar atom-features inthe PHRAG and its weight relative to the total number ofPHRAG in the chemical graph. In the end, each chemicalstructure is described by the list of constituting PHRAG andtheir respective scores.[26]

In contrast, feature-pair distributions (FPD) capture theoverall spreading of pairs of atom-centred features at differ-ent predefined bond lengths.[28, 29] Previously, every non-hy-drogen atom in the molecule is assigned to one or more ofthe following six pharmacophoric features, namely, hydro-phobic, aromatic, hydrogen-bond donor, hydrogen-bondacceptor, positively charged and negatively charged. Then,in our topological implementation, the shortest bond pathbetween all possible pairs of atom-features is obtained andits occurrence at 1, 2, 3, 4, 5, 6, 7–9, 10–12, 13–15, 16–20bonds stored. Therefore, with FPD, every molecule is de-scribed as a vector composed of 210 integer number ele-ments.[26]

Among the three descriptors used, Shannon entropy de-scriptors (SHED) provide the simplest and fuzziest descrip-tion. SHED are derived from simplified FPD.[27] In this case,each atom in a molecule is assigned to one or more of fouratom-centred features, namely, hydrophobic, aromatic, hy-

drogen-bond donor and hydrogen-bond acceptor, fromwhich FPD are then derived. However, instead of using theactual feature-pair counts at each path length, the variabili-ty within all possible feature-pair distributions is quantifiedusing the concept of Shannon entropy.[30] With SHED, eachchemical structure is ultimately described as a vector com-posed of just 10 real number elements.[27]

When using PHRAG and FPD, the similarity between twomolecules corresponds to the overlapping fraction of theirrespective profiles,[26] whereas with SHED, Euclidean distan-ces are calculated instead.[27] The biological relevance ofthe similarity and distance values obtained with these de-scriptors was assessed on their ability to discriminate activefrom random compounds for all targets chemically repre-sented in publicly available sources. As a result of this vali-dation analysis, compounds below similarity values of 0.76and 0.87 for PHRAG and FPD, respectively, and above a dis-tance value of 0.52 for SHED were considered to be outsidethe applicability domain (OAD) of these descriptors.

2.4 In Silico Receptorome Screening

For each of the 34 targets under study, the ensemble ofPHRAG, FPD, and SHED descriptors capturing the structuraland pharmacophoric features of all molecules for which af-finity data is available represents a mathematical descrip-tion of the target from a chemical perspective. On thisbasis, the affinity of a compound for a given target can beestimated from the affinity landscape defined by all neigh-bouring molecules according to the descriptors and similar-ity/distance metrics used.[28, 31] Therefore, after obtaining thedescriptors for the set of 13 antipsychotic drugs, PHRAGand FPD similarities and SHED distance between each drugand all molecules defining each ligand-based target model

Figure 2. Distribution of the number of compounds extracted from public sources with affinity data for the various targets.

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In Silico Receptorome Screening of Antipsychotic Drugs

are calculated. For each target, the number of referencemolecules around a drug within the applicability domain ofeach descriptor is stored (excluding the drug itself, if pres-ent). If for a given target no reference neighbours arefound around a drug with any of the three descriptors, thedrug-target interaction is annotated to be OAD. Instead, ifdescriptor neighbours are identified, the affinity of thedrug for that target is predicted by inverse distance weight-ing (IDW) interpolation. IDW methods are based on the as-sumption that more distant (less similar) observationsshould have their contributions diminished according tohow far away from the interpolation point they are. In afirst step, a neighbourhood around the interpolated point(test compound) is identified and, then, a weight is as-signed to each scatter point (reference compounds) thatdecreases with increasing distances from the interpolationpoint to the neighbour point. The simplest form of IDW in-terpolation is given by

F ¼Xn

i¼1

wifi; wi ¼d�p

i

Pn

j¼1

d�pj

ð1Þ

where F is the interpolated value of the function (in thiscase pKi), n is the number of scatter plots in the neighbour-hood of the interpolated point, fi is the value of the func-tion in point i and wi is its assigned weight function.[32] Inthe weight function calculation, d are the distances be-tween the interpolated and the scatter points and p is anarbitrary positive real number called the power parameterwhich typically is assigned to 2. In the case of PHRAG andFPD, distances are obtained by subtracting similarities fromunity.

In addition to performing affinity predictions from eachindividual descriptor, an ensemble approach taking intoconsideration the predictions from all three descriptors wasalso implemented. The SFP (SHED-FPD-PHRAG) ensembleapproach takes directly the predicted affinity value, if theprediction could only be made by one descriptor, or theaverage predicted affinity values, if predictions were madeby multiple descriptors. This strategy increases the numberof compounds for which an affinity prediction can bemade.

2.5 Similarity Ensemble Approach

The goodness of the results from our in silico target screen-ing implementations will be contrasted against those ob-tained using the Similarity Ensemble Approach (SEA).[14] SEAis taken here as the method of reference, first and foremostfor its proven ability to identify novel targets for knowndrugs,[14, 17] but also for its public accessibility (http://sea.bkslab.org/). In essence, SEA annotates targets to com-pounds on the basis of a statistical measure (E-value) derivedfrom cumulative Tanimoto similarities (ECFP4 fingerprints

were selected)[33] against a set of bioactive compounds pres-ent in an annotated database (WOMBAT was selected).[34] Inthis work, all drug-target annotations with E-values <10were considered. In this respect, collisions (MaxTC=1.00)with E-values �10 were disregarded. Since no affinity predic-tions are provided at present by SEA, comparisons will beperformed solely on a binary active/inactive basis. More de-tails on the SEA methodology can be found elsewhere.[14, 17]

3 Results and Discussion

The performance of in silico screening a set of 13 antipsy-chotic drugs against 34 protein targets will be assessed onthe following grounds: i) scope of predictability : percent-age of drug-target interactions for which the method isable to make a prediction of affinity; ii) recall : percentageof drug-target interactions correctly predicted within cer-tain boundaries; and iii) precision: percentage of drug-target interactions correctly predicted among all predictedwithin certain boundaries. The results obtained in thesethree aspects are presented and discussed in the followingsections.

3.1 Scope of Predictability

A feature of key importance to any similarity-based methodfor predicting biological affinities is its ability to detect mol-ecules of known affinity within the applicability domain ofa query drug. Therefore, the first aspect that needs to beinvestigated is whether the different descriptors used inthis work display reasonable neighbourhood behaviour.[28, 30]

Accordingly, Table 1 compiles the percentage of com-pounds for which an affinity prediction can be made withSHED, FPD, PHRAG, and the SFP ensemble approach acrossdifferent affinity ranges. For example, SHED can provide anestimate of affinity for 55 % of the 105 drug-target interac-tions with pKi = [7,8), meaning that this descriptor is stillunable to find neighbours for the drugs involved in the re-maining 47 drug-target interactions within this affinityrange. In comparison, PHRAG is able to give predictions for86 % of the drug-target interactions within the same affinityrange, thus showing an improved neighbourhood behav-iour.

Most interestingly, it is observed that the scope of pre-dictability increases consistently for all descriptors whenmoving towards more potent affinity ranges. In this re-spect, it is worth stressing the relatively low percentagevalues obtained for the inactive drug-target interactions(pKi<5). Different explanations could be hypothesised toexplain this behaviour. For example, one could considerthat molecules populating high affinity ranges have beenoptimised and their pharmacophoric features are betterrepresented in annotated databases, where a large percent-age of the molecules stored are indeed bioactive. It canalso be argued that higher affinities usually require tighter

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and stricter pharmacophoric arrangements and thus moreaccessible to predictions if reference molecules in that af-finity range are known. Along the same argument, require-ments for inactivity and the lower activities are far more di-verse and relaxed, the majority of them being unlikely tobe properly represented in current annotated chemical li-braries that have historically focussed on extracting andstoring bioactive compounds.

Overall, SHED, FPD, and PHRAG are able to give an affini-ty prediction for 45 %, 59 %, and 67 %, respectively, of thetotal number of 442 drug-target interactions. The SFP en-semble approach allows to push the scope of predictabilityslightly above 70 %. For the sake of comparison, the per-centage of statistically significant drug-target interactionspredicted by SEA has been also added in Table 1. In agree-ment with the behaviour observed for the other descrip-

Table 1. Percentage of compounds for which a statistically significant target annotation is given (SEA) or an affinity prediction is made(SHED, FPD, PHRAG, and SFP) across different affinity ranges.

Scope of predictability

pKi<5 5�pKi<6 6�pKi<7 7�pKi<8 8�pKi<9 pKi�9 TOTAL

SEA – 5.6 11.7 26.7 25.4 42.9 17.4[a]

SHED 25.3 41.6 39.8 55.2 62.7 71.4 44.8[b]

FPD 25.3 50.6 58.3 79.0 79.7 100.0 59.3[b]

PHRAG 46.8 50.6 66.0 85.7 81.4 100.0 66.7[b]

SFP 50.6 52.8 71.8 86.7 86.4 100.0 70.1[b]

[a] This percentage is calculated over the number of bioactive drug-target interactions (363). [b] This percentage is calculated over thetotal number of drug-target interactions (442).

Figure 3. Comparison between in vitro (left ; adapted from [21]) and in silico (right) receptorome screening of a set of antipsychotic drugs.OAD: outside the applicability domain.

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In Silico Receptorome Screening of Antipsychotic Drugs

tors, the scope of predictability augments steadily with in-creased affinities, from 6 % at pKi = [5,6) to 43 % at pKi�9).In total, SEA is ultimately able to annotate 17 % of the 363active drug-target interactions.

In Figure 3, the results of the in silico receptoromescreening of antipsychotic drugs obtained with the SFP en-semble approach can be visually compared with the actualin vitro receptorome screening, adapted from [21]. As canbe observed, complete affinity profiles are predicted for 7out of the 13 drugs, namely, chlorpromazine, loxapine, thio-ridazine, clozapine, olanzapine, risperidone, and zotepine.In contrast, affinities for only 1, 5, and 8 targets can be pre-dicted for quetiapine, thiothixene, and ziprasidone, respec-tively, reflecting the fact that currently the surroundings ofthe chemical space they occupy seem not to be well repre-sented in annotated chemical libraries from public sources.The light blue cells are all the drug-target interactions forwhich no affinity prediction can be made because refer-ence neighbours around drugs for those targets (if any) arefound outside the applicability domain of any of the threedescriptors used.

3.2 Recall and Precision

Beyond the visual impression provided by Figure 3, a morequantitative analysis of the overall concordance betweenthe in vitro and in silico receptorome screenings is necessa-ry to assess the levels of recall and precision achieved inthe affinity predictions. Accordingly, Table 2 contains theconfusion matrix between known (in columns) and predict-ed (in rows) affinities across different affinity ranges. Thevalues in the diagonal are the number of concordant drug-target interactions within each affinity range. For example,46 drug-target interactions with known affinities in the

range of pKi = [6,7] are indeed predicted in that affinityrange. In terms of recall (REC-0), this represents 45 % (46/103) of all known drug-target interactions in that affinityrange; in terms of precision (PREC-0), this represents 51 %(46/91) of all predicted drug-target interactions in that af-finity range. Extending the confidence boundaries to theneighbouring ranges, that is accepting a 1-log unit error inpredictions, the level of recall (REC-1) increases to 72 % (74/103) and achieves the maximum possible value given thescope of predictability within that affinity range (seeTable 1); the level of precision (PREC-1) increases also to90 % (82/91). Remarkably, note that high levels of precisionare mainly concentrated in diagonal and adjacent cellsacross all affinity ranges. Overall, the SFP ensemble ap-proach is able to recover 40 % and 65 % of all drug-targetinteractions within the same affinity range and within 1-logunit error, respectively; the corresponding levels of preci-sion are 57 % and 93 %, respectively.

The distribution of errors produced in the prediction ofthe 34 target affinities for three reference antipsychoticdrugs, namely, clozapine, risperidone, and chlorpromazine,are shown in Figure 4. For clozapine, in 33 out of the 34targets (97 %) the predicted affinities have an absoluteerror equal to or below 1-log unit. The highest errors arefound for the a1B adrenoceptor (�1.00) and for the H3 andH4 histamine receptors (0.97 and 1.50, respectively).Among the 31 targets for which clozapine is known to beactive (pKi�5), SEA retrieves only 2 statistically significanttargets, namely, 5-HT2A and D2 with E-values of 2.62(MaxTC = 0.35) and 8.56 (MaxTC = 0.33), respectively, a dis-appointing outcome for this gold standard of antipsychoticdrugs. In the case of risperidone, in 32 out of the 34 targets(94 %) the predicted affinities have an absolute error equalto or below 1-log unit. The targets with the highest predic-

Table 2. Confusion matrix between known and predicted affinities for drug-target interactions. Gray scale reflects percentage of com-pounds from total predicted within each affinity range: <10 %, = [10 %,25 %], = [25 %,50 %], >50 %. REC-0 and PREC-0 refer to recalland precision, respectively, within the same affinity range; REC-1 and PREC-1 refer to recall and precision, respectively, within the affinitywindow that includes the neighbouring ranges (see test for details).

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tion errors are the 5-HT1D and 5-HT2C serotonin receptors(�1.00 and �2.20, respectively) and the H4 histamine re-ceptor (�1.10). Among the 22 targets for which risperidoneis known to be active (pKi�5), SEA returned 6 target anno-tations with E-values <10, all of them being nonethelessidentified as collisions (MaxTC = 1.00). Finally, the predictedaffinity profile of chlorpromazine contained errors below 1-log unit in 24 of the 34 targets (71 %). The largest errorsare produced in predicting the affinities for dopamine re-ceptors (particularly, D1, D2, and D3) and all three neuro-transmitter transporters. For this drug, SEA recovers, with E-values <10, 3 of the 32 targets at which chlorpromazine isactive (pKi�5), namely, 5-HT2A, D1, and D2, although theyare also recognised as collisions (MaxTC = 1.00).

A summary picture of the levels of recall and precisionachieved by the SFP ensemble approach above different af-finity thresholds is presented in Figure 5. Only the recalltrend is plotted for SEA, since affinity predictions are notprovided with this approach. Focusing on the performanceof the methods to predict the 274 drug-target interactions

with biologically relevant affinities (pKi�6), it is observedthat SFP recovers 45 % of them within the same affinityrange (REC-0), with a level of precision (PREC-0) of 53 %,which gives a F-measure (defined as the harmonic mean ofrecall and precision) of 0.486. However, allowing a 1-logunit error in the affinity prediction, the level of recall (REC-1) increases to 78 %, with a precision (PREC-1) of 92 %, re-sulting in a F-measure of 0.841. Finally, irrespective of theerror, if one would consider valid any active prediction(pKi�5) of a drug-target interaction with pKi�6, the recall(REC-A) achieved would reach 81 % and the precision(PREC-A) would go up to 98 %, leading to a F-measure of0.889. For comparison, the corresponding recall obtainedfrom SEA (REC-A (SEA)) is of 21 %.

4 Conclusions

In spite of its relatively low dimension (13 drugs � 34 tar-gets) and limited target diversity (31 aminergic GPCRs and

Figure 4. Error in pKi predictions (pKiexp�pKi

pred) for three classical antipsychotic drugs, namely, clozapine, risperidone and chlorpromazine.SEA predictions are marked with an asterisk.

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In Silico Receptorome Screening of Antipsychotic Drugs

3 neurotransmitter transporters) this is, to the best of ourknowledge, the first time that in silico target screening ap-proaches are validated against a complete drug-target in-teraction matrix. Using an ensemble approach that com-bines three independent descriptors, namely, PHRAG, FPD,and SHED, we were able to give an affinity prediction for70 % of the total number of drug-target interactions. Com-paratively, SEA produced statistically significant annotationsfor 17 % of the active drug-target interactions. A quantita-tive analysis of the predictions made revealed that the SFPensemble approach was able to recover 65 % of all affinitieswithin 1-log unit error, with a level of precision of 93 %. Al-together, these results confirm the use of in silico receptor-ome screening as an efficient and cost-effective strategy toin vitro screening.

An increasing amount of pharmacological data on manyprotein targets is becoming available for large numbers ofsmall molecules. The systematic generation and collectionof data in annotated chemical libraries allows for describingproteins from the perspective of ligands, descriptions thatdevelop into a more complete picture as data on new li-gands are obtained and the chemical space explored be-comes more saturated. Ligand-based representations ofproteins offer a simple, yet efficient, means to anticipatethe pharmacological profile of small molecules. In this re-spect, the in silico receptorome screening of antipsychoticdrugs presented in this work can be easily extended to alltherapeutically relevant proteins for which pharmacologicaldata is available, thus opening an avenue for designingcompounds with customised target profiles, identifyingplausible targets responsible for adverse drug reactions orundesired toxicities, providing a better understanding of

bioactive molecules with unknown mechanisms of action,or repurposing of known drugs.

Acknowledgements

This research was supported by the Spanish Instituto deSalud Carlos III and the Ministerio de Ciencia e Innovaci�n(Project BIO2008-02329). Funding was received from theEuropean Community’s 7th Framework Programme (FP7/2007-2013), under Grant Agreement No. 215847 (EU-ADRproject), and the Innovative Medicines Initiative, underGrant agreement No. 115002 (eTOX project).

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Received: May 11, 2010Accepted: June 7, 2010

Published online: July 9, 2010

Mol. Inf. 2010, 29, 543 – 551 � 2010 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim www.molinf.com 551

In Silico Receptorome Screening of Antipsychotic Drugs