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Current Topics in Medicinal Chemistry, 2014, 14, 1473-1485 1473

A Rational Workflow for Sequential Virtual Screening of Chemical Libraries on Searching for New Tyrosinase Inhibitors

Huong Le-Thi-Thu1,*, Gerardo M. Casañola-Martín2,3,4, Yovani Marrero-Ponce5, Antonio Rescigno6, Concepción Abad2 and Mahmud Tareq Hassan Khan7

1School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Viet-nam; 2Departament de Bioquímica i Biologia Molecular, Universitat de València, E-46100 Burjassot, Spain; 3Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Spain; 4Centro de Información y Gestión Tecnológica, Ministerio de Ciencia Tecno-logía y Medio Ambiente (CITMA), 65100, Ciego de Avila, Cuba; 5Enviromental and Computational Chemistry Group, Facultad de Química Farmacéutica, Universidad de Cartagena, Cartagena de Indias, Bolivar, Colombia; 6Sezione di Chimica Biologica, Dip. Scienze e Tecnologie Biomediche, Università di Cagliari, Cittadella Universitaria, 09042 Mon-serrato (CA), Italy; 7Present address: Holmboevegen 3B, 9010 Tromso, Norway

Abstract: The tyrosinase is a bifunctional, copper-containing enzyme widely distributed in the phylogenetic tree. This en-zyme is involved in the production of melanin and some other pigments in humans, animals and plants, including skin pigmentations in mammals, and browning process in plants and vegetables. Therefore, enzyme inhibitors has been under the attention of the scientist community, due to its broad applications in food, cosmetic, agricultural and medicinal fields, to avoid the undesirable effects of abnormal melanin overproduction. However, the research of novel chemical with anti-tyrosinase activity demands the use of more efficient tools to speed up the tyrosinase inhibitors discovery process. This chapter is focused in the different components of a predictive modeling workflow for the identification and prioritization of potential new compounds with activity against the tyrosinase enzyme. In this case, two structure chemical libraries Spectrum Collection and Drugbank are used in this attempt to combine different virtual screening data mining tech-niques, in a sequential manner helping to avoid the usually expensive and time consuming traditional methods. Some of the sequential steps summarize here comprise the use of drug-likeness filters, similarity searching, classification and po-tency QSAR multiclassifier systems, modeling molecular interactions systems, and similarity/diversity analysis. Finally, the methodologies showed here provide a rational workflow for virtual screening hit analysis and selection as a promis-sory drug discovery strategy for use in target identification phase.

Keywords: Drug-likeness filtering, molecular docking, QSAR modeling, similarity searching, tyrosinase inhibitor, virtual screening.

1. INTRODUCTION

Tyrosinase (monophenol monooxygenasse; EC 1.1.4.18.1) is a metalloenzyme oxidase widely distributed in the phylogenetic tree. This enzyme catalyze the two first steps of melanin synthesis pathway, by the hydroxylation of the L-tyrosine to 3,4-dihydroxyphenylalanine L-DOPA (mo-nophenolase activity), and the posterior oxidation to do-paquinone (diphenolase activity) [1]. Because its main role in melanogenesis, abnormal tyrosinase regulation it is related with some skin diseases such as hyperpigmentation, melasma (acquired hyperpigmentation), post in ammatory melanoderma, solar lentigo, etc [2-4]. Hence, tyrosinase in-hibitors have been used largely as depigmenting agents for treatment of these pigmentation disorders [5-7]. *Address correspondence to this author at the School of Medicine and Pharmacy, Vietnam National University, Hanoi (VNU) 144 Xuan Thuy, Cau Giay, Hanoi, Vietnam; Tels: 53-33-223066 (Cuba) and 963543156 (València); Faxes: 53-33-223066 (Cuba) and 963543156 (València); E-mails: [email protected] or [email protected]

In recent times, besides QSAR methodologies, there are other data mining techniques introduced in drug discovery with high accuracy levels [8]. This successful data integra-tion is a complex theme in the desktop today´s researchers. In this sense the current drug discovery scenarios are intro-ducing standard workflow for screening structural chemical libraries [9]. This issue presents advantages because com-pounds could be obtained by the direct purchase from the owners avoiding the time-consuming of synthesis or isola-tion process [10]. Moreover, in the last times, the campaigns associated with massive virtual HTS are continuously in-creasing are gaining on accuracy in the prioritization process of chemicals, due to the introduction of several ligand and structure based methodologies [11-12].

Therefore, to manage larger compounds libraries and cover the expectations of the drug discovery process, in silico virtual screening and computer-aided drug design have become increasingly important [13]. In our research group, this approach has been applied in the discovery of novel ty-rosinase inhibitors (TIs) as cycloartane [14-15], diterpenoi-dal alkaloids [16], tetraketones [17], coumarin [18], and so

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1474 Current Topics in Medicinal Chemistry, 2014, Vol. 14, No. 12 Le-Thi-Thu et al.

on. In these studies, the classification QSAR-based virtual screening (VS) has been employed for in-house congeneric chemical libraries of different laboratories to identify TIs from inactives. Latter, new class of QSAR models named potency models [19-20] were developed. The last models could be used as a cascade system together with the classifi-cation models for more complete description of tyrosinase inhibitory activity. Besides, this type of models helps to identify true positives and make an adequate process of pri-oritization of compounds. In recent work, all these models were assembled in different multi-classifier systems (MCSs) that improved the performance of QSAR methods [21].

By this means, in this chapter we present a procedure of combining these and others different VS strategies in the computational research for the selection/identification of novel tyrosinase inhibitors. This framework was employed with efficacy to discover new chemical entities with anti-tyrosinase activity. Finally, is important to stand out that the different virtual screening approaches mentioned comprises: drug-likeness filters, similarity searching, classification and potency QSAR multi-classifier systems, molecular docking studies, and post-processing procedures as strategies; that were assessed in a sequential manner over the Spectrum

Collection and Drugbank databases.

2. TWO STRUCTURE CHEMICAL LIBRARIES: SPECTRUM COLLECTION AND DRUGBANK

The ascending grown of computational resources have brought a rapid increasing of structural chemical databases either online or repository company. The more interesting examples are the huge ZINC and ChemSpider databases, comprising 13 million and 26 million of compounds, respec-tively. This two chemical data sources are included between the main sixty-four free databases nowadays [22]. Therefore, the huge structural chemical database screening is becoming one of the hotter topics in compound retrieve using any QSAR, ligand or structure data mining procedures. In this sense, some authors have included interesting updated re-views about this topic [23-24]. By this mean here we pre-sented the results obtained over the Spectrum collection (http://www.msdiscovery.com/spectrum.html) and Drug-bank (www.drugbank.ca), which consists of 2 000 and 6 827

compounds, respectively, that were screened using a sequen-tial strategy for virtual screening looking for potential thera-peutic chemicals for the treatment of hyper-pigmentation disorders. A owchart depicting the various steps of virtual screening including database ltration, similarity searching, QSAR modeling, docking and clustering studies to prioritize the virtual hits is shown in (Fig. 1).

This Fig. (1) displays the virtual screening stepwise workflow which resulted in the discovery of novel scaffolds against the tyrosinase enzyme. The protocol was based in the computational hierarchy of each filter, the consuming CPU time and the complexity of input information for each step. This hierarchical procedure allows reducing the number of selected compounds (retrieved as novel TIs) gradually after each filter. This mentioned strategy was employed to screen virtually two databases (Spectrum Collection and Drug-

bank). By other way, many of the chemical libraries as the case

of PubChem [25] on-line database are web-based systems with well recognize facilities to do some pre-processing tasks in an easy way. This is the also the case Spectrum

collection and Drugbank were some drug-likeness filters or similarity searching methods are implemented as tools for search and retrieving. Therefore some of these services were used in these studies.

3. DRUG-LIKENESS FILTERS

The term “druglike” [26-29] is used for pharmaceutical research to describe molecules with properties that fall within the boundaries delineated by the wide majority of pharmaceutical agents. This process is associated with the many possible molecular properties that most directly influ-ence the drug-like properties of a molecule in some specific type of research. Lipinski et al.[30] defined the so-called “rule of five” (sometimes abbreviated as RoF) in an effort to solve this question. The main steps of this concept is the ex-amination of different parameters such as the number of ro-tatable bonds (nRotB), polar surface area (PSA), log D, and counts of nitrogen and oxygen atoms in an effort to define easily calculated properties that will be predictive of a favor-able outcome and established mayor cutoff for these physi-

Fig. (1). Sequential virtual screening workflow used in the identification of promissory TIs and the filtering of compounds involved in each one of different steps from the Spectrum Collection and DrugBank databases.

Virtual Screening Workflow for the Identification of New Tyrosinase Inhibitors Current Topics in Medicinal Chemistry, 2014, Vol. 14, No. 12 1475

cal-chemical properties and others [31-35]. However the threshold of Lipinski seems very rigid in occasions. Hence some scientist in this field have stand out and proposed other diverse boundaries and criteria of drug-likeness filters [36]. Taking this into consideration, in our work we applied supe-rior limits of all these filters. In our case, a compound was not taken into consideration in the next steps if it has the molecular weight (MW) above 700 g/mol; the computed octanol–water partition coefficient CLogP higher than 7; the number of hydrogen bond donors (nHBDon) above 5 and acceptors (nHBAc) above 10; the number of rotatable bonds (nRotB) higher than 10 and a polar surface area (PSA) above 140 Å2. All these descriptors were calculated with our in house TOMOCOMD-CARDD (acronym for TOpological MOlecular COMputational Design - Com-puted-Aided ‘Rational’ Drug Design) software. These mo-lecular descriptors are implemented in a new module (DE-SPOOLs, acronym of DEScriptor POOLs) of our program [37] that offers calculations of the several 0-3D indices, which are calculated mainly using The Chemistry Devel-opment Kit [38]. By using the defined way above we pro-ceed to the first filtering consisting in the application of the criteria describe above on the Spectrum collection data-base (http://www.msdiscovery.com/spectrum.html). This first step also consists of reducing the number of chemicals (negative design) employing the Drug-likeness filters. These are simple, fast and also allow “optimizing” in some way simultaneously the potency and the pharmacokinetic [39]. So, we further sorted these 2 000 compounds using the supe-rior boundaries of all filters reported in the literature and nally 1 394 compounds were further considered for the next step.

4. SIMILARITY SEARCHING

Similarity searching identifies those database molecules that are most similar to reference structures, using some quantitative definition of intermolecular structural similarity. The reference structures and the molecules in the database are characterized by one or more molecular descriptors. Their comparisons allow the calculation of a similarity measurement between the reference structure and each of the database structures, and the latter ones are then sorted into order of decreasing similarity with the target. The output from the search is a ranked list in which the structures that are calculated to be most similar to the reference structure are located at the top of the list. These chemicals will be those that have the greatest probability of being of interest to the user, given an appropriate measure of intermolecular structural similarity. The similarity methods are extremely useful at the beginning of a drug discovery project, because it needs little information about the target and only few known active compounds. Moreover, the implementations of similarity methods are generally computationally inexpen-sive, so searching large databases can be routinely per-formed. The result of this step is a focused library, since all included compounds present common features a reference compounds. In our case the data fusion method [40] was applied. . In Table 1, the structures of reference compounds were given. A hierarchical cluster analysis, k-NNCA, was executed to visualize the distribution of reference com-pounds in different groups. In Fig. (2), a dendogram for

these compounds is shown. It can be seen, there is great structural diversity among these chemicals, which represent different molecular subsystems important for the tyrosinase activity.

The set of 15 strong tyrosinase inhibitors of diverse struc-tures was selected as reference compounds. The molecular structures of these chemicals are given in Table 1.

The MACCS fingerprints [41] were calculated to charac-terize reference structures and the ones of the database em-ploying the program TOMOCOMD-CARDD software. These fingerprint are implemented in a new module (MOLFIP, acronym of MOLecular FIngerPrints) of our program [37] that offers calculations of the several finger-prints, which are calculated mainly using The Chemistry Development Kit [38]. The Tanimoto coefficient [42], com-monly used for binary data, was computed to establish the metrics of intermolecular comparison (each compound with every other in its activity class). A specific database mole-cule appears at rank position rij (1 i n, 1 j 15) with a similarity measurement (scores), sj against every reference structure. We used the fusion rule MAX for combining the similarity scores, so the final fused score was established as shown in Equation 1.

sf = Maximum {s1, s2…s15} (1) Later, each molecule of the database was sorted by its

fused score, sf. The similarly active compounds in the top 30% of highest ranked data set compounds were retrieved for the next step. For the case of the Spectrum collection database structures this procedure was applied resulting in the elimination of 1285 molecules. The remaining 109 com-pounds are similar in some way to one of 15 reference com-pounds (positive design).

The Drugbank database (www.drugbank.ca) of 6 827 drugs was also screened using a similar procedure as above. In this case, first the similarity searching (data fusion by maximum score using 15 strong TIs as reference structures) was applied, because DrugBank offers this option in the management of its search database. By this procedure were eliminated 6659 compounds representing the 97.54% of the chemicals in the database. The repeated or reported against the tyrosinase compounds were removed and the remaining ones were ltered using Druglikeness criteria mentioned in the section above. From this, 131 compounds were selected and considered for the next step.

5. MULTICLASSIFIERS GUIDED BY QSAR MODELS

In recent times, Quantitative Structure-Activity Relation-ships (QSARs) are the most widely used approach in drug design and have been applied successfully in the discovery of novel tyrosinase inhibitors [18, 43-49]. Hence, this method could constitute the principal “switch” for sequential workflow aiding to new lead compounds identification. The binary QSARs for tyrosinase inhibitors are described in pre-vious reports [18, 43-49], therefore a brief approximation will be discussed here. A first training set of 1072 com-pounds was collected with 526 chemicals classi ed as “ac-tive” (TIs) and 546 compounds as “inactive” (non-TIs). The molecular structures and properties were correlated with biological activity using TOMOCOMD-CARDD descriptors,


Table 1. Structures of reference compounds in similarity study.

O

OHO

1 Kojic acid

NH2

NHO

O O OH

2 L-mimosine

O

OH

3 L-Tropolone

N

OH

N O

4 N-cyclopenthyl-N-

nitrosohydroxyl-amine

HO

OH

O

O

5 Methyl ester of gentisic acid

O

O

HO

O O

HO OH

6 Kurarinone

HN

S

H2N

7 Phenyl-thiourea

N

N

8 BP4

OO

O

O

OHO

H

O

HO

9 8´-epi-cleomiscosin A

O

O 10

HO

OH

HO

HO

11 4-Prenyloxy-

resveratrol

HN

S

O

12 Alkyl-thiocarbamate E

NH

O

OH

HO

OH

HO

13

Benzylbenzamide 15

O OH

N

NH

S

NH2

14 3-Hydroxy -4-methoxy benzaldehyde

thiosemicarbazone

O

O

O

O

NH2 15 TK21

and different classification models were generated. These models enable the identification of TIs from inactive ones. In second place, the potency models were obtained using a learning set of 257 strong TIs and 141 moderate-to-weak compounds [19-20]. The last ones would be used hierarchi-cally with the models adjusted on the first database, for more complete description of tyrosinase inhibitory activity.

Afterward, we introduced other statistical techniques [quadratic discriminant analysis (QDA), binary logistic re-gression (BLR) and classification tree (CT) [20]] and many machine learning approaches [support vector machine (SVM), artificial neural network (ANN), Bayesian networks (BNs), k-nearest neighbors (KNN) [19]], which enhanced the performance of previous LDA-QSAR models in both database. Theses single classifiers can be used to make ty-rosinase inhibitory activity depictions for new chemicals. However, many factors can affect the performance of those

classifiers. Selecting the best available classifier is an option, but because the distribution of new chemicals that the classi-fier may meet during operation may vary (slightly or signifi-cantly depending on the application), this approach does not provide the best solution in all cases. Furthermore, because many classifiers are generally tried before a single classifier is selected, this approach also discards valuable information by ignoring the performance of all the other classifiers [50].

By this aim, the combination of multiple classifiers has been proposed in the field of machine learning to improve the performance of the single classifier approaches [51-53]. These multiple classifier systems (MCS) are based on the combination of several classifiers such that their union achieves higher performance than the stand-alone classifiers. Hence, an ensemble of classifiers is a set of classifiers, whose individual classification decisions are combined in some way [54]. Many studies have demonstrated that


Fig. (2). Dendrogram illustrating the results of the hierarchical k-NNCA of strong TIs used as reference compounds. ensembles often outperform their base models (the compo-nent models of the ensemble) if the base models perform well on novel examples and tend to make errors on different examples [55].

In the case of tyrosinase inhibitor QSAR equations, to in-crease performance demands in modeling tyrosinase activity the individual models obtained were assembled in different multi-classifier systems (MCSs) to improve their perform-ance classifiers for tyrosinase inhibitory activity prediction [21].

For the Spectrum Collection, the compounds found by similarity searching were screened by QSAR (another posi-tive design approach) using classification MCS based on average probability (AP) [21] to identify new TIs. Thus, 65/109 compounds were identified as active against ty-rosinase enzyme. It is important to highlight that most of inactive compounds identified by QSAR were the last ones of the list of 109 compounds. The same occurs in the case of the Drugbank database were 119/131 were identified as ac-tives. This justifies the selection of the cutoff value of simi-larity searching. The compounds identified by classification QSAR were sequentially screened by potency QSAR using boosting ensemble based on support vector machine [21]. This potency MCS identified 25 and 107 compounds from Spectrum Collection and Drugbank, respectively as strong TIs.

6. MODELING MOLECULAR INTERACTIONS SYS-TEMS

The next step was to use the molecular docking, that con-sists of posing each ligand into the binding site of the target. This gives a predicted binding mode for each database com-

pound, together with a measure of the quality of the fit of the compound in the target binding site. This information is used to rank the compounds with a view to selecting and experi-mentally testing a small subset for biological activity [56]. The docking calculations of strong TIs identified by QSARs in the mentioned above studies were performed using the ICM™ docking module with the default setup as earlier mentioned [57-59].

6.1. Preparations of the Inhibitors and Target Molecules

The 2D structure of the compound (in mol file format) was converted to 3D and energy minimized at the 3D space of ICM environment. The atom types using local chemical environment, Merck Molecular Force Field (MMFF) [60-66] formal charges and 3D topology were assigned. The lowest energy conformers of the compounds were then docked into the 3D space of the active site of the three dimensional struc-ture of Tyrosinase (PDB ID: 3NQ1).

All the docking calculations were performed using the “interactive docking” menu at the ICM environment. After docking the stack of docking poses were checked visually. Multiple stack conformations were selected based on their docking energies, rmsd values (compared between the docked model and x-ray conformation) and similarities to closely related x-ray crystal structures from PDB. Then the best conformations for the compound were finally chosen, and then the binding energy was calculated using ICM script.

For each of the individual docked complexes the free en-ergies of binding ( Gcal) between the protein and ligand was calculated using ICM script utilizing Equations 2 and 3[67].

Gcal. = GH + Gel. + Gs + C (2)


Gcal. = GH + Gcol. + Gdes-sol. + Gs + C (3)

Here, GH is the hydrophobic or cavity term, which ac-counts for the variation of water/non-water interface area.

Gel is the electrostatic term composed of coulombic ( Gcol) interactions and desolvation ( Gdes-sol) of partial charges transferred from an aqueous medium to a protein core envi-ronment. The Gs is the entropic term which results from the decrease in the conformational freedom of functional groups buried upon complexation; and finally the C is a constant accounts for the change of entropy of the system due to the decrease of free molecules concentration (cratic factor), and loss of rotational/translational degrees of freedom [67].

After preparation of docking process the strong TIs iden-tified by QSARs were subsequently docked in the active site of the tyrosinase (PDB ID: 3NQ1) using the ICM program [57]. In this study, only the chemicals selected of the Spec-trum Collection were used on the structure-based study. Docking is an effective method for prioritizing ligands with favorable interactions with the receptor and can also be seen as a positive design. For each case, the binding energy (BE) was achieved and used as score to binding mode prediction of the compounds. First, we calculated the BR for a set of strong, moderate-weak and inactive compounds with known activity against the tyrosinase. The docking molecular inter-action process revealed that only one compound of the total of 25 selected by QSAR did not complement favorably the protein binding site. This result showed a good correspon-dence between QSAR and docking approaches.

7. POST-PROCESSING ANALYSIS

Some methodologies for post-processing after sequential virtual screening were assessed. In our case, we selected the k-NNCA (k-nearest neighbors cluster analysis) and k-MCA (k-means cluster analysis) algorithms [68-69] to study the similarity/diversity among the retrieved active compounds and these latter ones with active compounds. This two types of Cluster Analysis (CA) were chosen because are a group of methods capable to recognize similarities among cases (ob-jects) or among variables and single out some categories as a set of similar cases (or variables). Therefore it enables the selection of novel scaffold for tyrosinase inhibitors. Before carrying out the cluster processes, all the variables were standardized. In standardization, all values of selected vari-ables (molecular descriptors) were replaced by standardized values, which are computed as follows: Std. score = (raw score - mean)/Std. deviation.

Finally, by a CA of the database active compounds and the retrieved ones plus a detailed visual inspection, for the case of Spectrum Collection, 19 out of 24 compounds were selected to be evaluated experimentally. It is important to note that within the six compounds removed, some have been reported in the literature activity against tyrosinase, such as hinokitiol [70] and angolensine [71], while the Spec-trum does not report itself. This fact confirmed the applica-bility of our protocol in the discovery of novel lead com-pounds anti-tyrosinase from large databases. Table 2 shows traditional uses and values of different "in silico" studies, the molecular structures of these compounds are given in Fig. (3).

On the case of Drugbank database, after cluster analysis and visual inspection we decided to select 32 compounds of the for enzyme assays. The structures of these compounds are shown in Table 3 and Table 4 shows traditional uses and values of different "in silico" studies for these drugs.

The flowchart in Fig. (1) is a schematic representation of the rational workflow sequential VS process with the number of hits reduced for each screening step in both databases. Using the sequential workflow, a total of fifty one putative novel TIs were successfully identified, which can be pur-chased and further evaluated in enzymatic experimental cor-roborations.

As it can be seen in both cases, many compounds identi-fied as new TIs are already known drugs because and this avoids time-consuming to bring new drugs to market be-cause re-discovered drugs that are already in use and its pharmacokinetic and toxicological properties are well-known [72]. This novel discovered drugs could be introduced into the market in the shortest time possible, thus accelerating the speed of discovery of new drugs for treating disorders of hyperpigmentation.

8. FUTURE TRENDS ON WORKFLOWS FOR BIO-ACTIVITY PREDICTION

Bioactivity or any type of property prediction has always be one of the challenges on data mining fields. In the case of selection of adequate anti-target activity the main arduous task are mainly focused in the correct identification of lead compounds or promissory high activity chemicals that could lead to drug-like compounds after examining its ADMET properties. Many predictive workflows has been showed in literature most of them focused on the use of 3D-QSAR COMFA, COMSIA and pharmacophore approaches together with docking studies [73-74]. Because the drug discovery is a highly complex and costly process, the integration in inno-vation, knowledge, information, technologies, expertise, in-vestments and management skills is required. In this way, the multistep VS can help identify bioactive substances from a large screening compound pool with limited experimental effort enabling to focus rapidly on the most promising can-didate structures.

In the case of the specific workflow scenarios, some questions that remains unsolved were derived during writing this chapter, like the use of sub-workflows integrated by sev-eral Multi-Sequential Workflow responsible of each step for adequate drug-like properties, that is ADMET. Moreover the consideration of other aspects concerning to workflow like the accuracy, sequence of combination, the most better quan-tity of sub-workflows to be used, and thresholds established for any workflow should be examined.

Finally, these results offers a suitable alternative to the new era of open on-line chemical databases encouraging its use together with the ascending approaches based on the new technologies development such as massive computer calcula-tions algorithms and cloud computing could have a over-whelming impact on virtual screening procedures based on ensemble workflows to solve several questions that are still in the route of drug discovery pipeline.


Table 2. Results of different in silico filters of VS protocol on the Spectrum Collection database.

ID* Compound Bioactivity MW

a LogP

b nHBDon

c nHBA

d nRotB

e PSA

f Sf

g

Simi-

larity

Ranking

Pre-

dicted

classh

Esti-

mated

Potencyi

Docking

BEk

(Kcal/

mol)

1500485 Phenytoin

sodium Anticonvulsant,

antieleptic 251.08 4.89 1.00 4.00 2.00 46.17 0.825 7 Act P -3.7

1503801 Naproxol Antinflamma-tor, analgesic,

antipyretic

468.21 2.35 0.00 6.00 5.00 95.34 0.816 10 Act P -2.2

1505130 3,4-Dimethoxy-cinnamic acid

- 208.07 1.75 1.00 2.00 4.00 55.76 0.786 19 Act P -3.1

200798 Dalbergione - 270.09 2.36 1.00 3.00 4.00 63.60 0.767 31 Act P -5.2

1505311 Diben-

zoylmethane Antineoplastic 224.08 5.72 0.00 2.00 4.00 34.14 0.756 38 Act P -4.1

200090 Obtusaquinone - 254.09 5.57 1.00 3.00 3.00 46.53 0.750 39 Act P -4.4

1505140

2',4-Dihydroxy-3,4',6'-

trimethoxy-chalcone

- 330.11 2.18 2.00 1.00 6.00 85.22 0.742 49 Act P -6.6

1504152 Nilutamide Antiandrogen 317.06 3.22 1.00 4.00 3.00 92.55 0.735 59 Act P -3.3

100308 Rockogenin - 432.32 5.43 2.00 4.00 0.00 58.92 0.729 63 Act P -2.9

1503032 Dipyrocetyl Antirheumatic,

analgesic 238.05 1.59 1.00 4.00 5.00 89.90 0.724 67 Act P -0.7

300610 Acetosyringone Insect

attractant, plant hormone

196.07 0.45 1.00 1.00 3.00 55.76 0.724 69 Act P -1.3

1504209 Diplosalsalate analgesic, antipyretic

300.06 4.12 1.00 4.00 6.00 89.90 0.724 71 Act P -1.5

200034 Atranorine - 374.10 3.48 3.00 4.00 6.00 130.36 0.719 77 Act P -5.9

1504118 Difractaic acid - 374.14 3.37 2.00 3.00 6.00 102.29 0.719 79 Act P -6.5

1505186 Culmorin - 238.19 3.70 2.00 2.00 0.00 40.46 0.714 82 Act P -2.4

201448 4,4'-Dimethoxy

dalbergione - 284.10 2.88 0.00 3.00 5.00 52.60 0.710 96 Act P -5.5

2300228 Kainic acid

Glutamate receptor agonist,

anthelmintic

213.10 -0.21 3.00 5.00 4.00 86.63 0.708 98 Act P -1.9

1505673 Troglitazone Antidiabetic 441.16 3.52 2.00 3.00 5.00 110.16 0.700 111 Act P -7.2

330032 Dicamba Herbicide 219.97 1.78 1.00 2.00 2.00 46.53 0.700 115 Act P -0.9

*ID =Code of Spectrum Collection;aMW = Molecular weight; bLogP = Computed octanol/water partition coefficient; cnHBDon = Number of hydrogen bond donors; dnHBAc = Num-

ber of hydrogen bond acceptors; fPSA = Polar surface area; gSf =Fused Score for the maximum of the similarity; hAct =Active against the tyrosinase identified by Clasiffication MCS

QSAR; iP = Potent inhibitor of tyrosinase identified by Potency MCS QSAR; kBE =Binding energy (PDB ID: 3NQ1).


Fig. (3). Molecular structures of the identified virtual hits from Spectrum Collection by VS protocol.

Table 3. Molecular structures of virtual hits identified on Drugbank by current VS protocol.

H

OH

O

O

O

O DB00227

H

O

H

O

HO

DB00486

H

OH

O

O

O

O DB00641

NO

O

O

OH

O

HO

DB00769

OHHO

OO

O

DB00929

H

O

O

O

HO

DB02205

O

HO

O

O

O

OH

O

DB02329

O

HO

DB02699

HN

OF

OH

Br

DB02880

OHO

O

DB03007

HO

O

OH

OH DB03451

HHO HH

H

HO

O

H

H

H

HO

OH

O DB03785

H

O

OH

H O

O

OH

DB04324

HH

HO

HO

OH

HO HH

OH

O

O DB04376

HH

O O

O

O

OO

DB04392

O

ON

O

DB04599


(Table 3) contd….

HOO

HO

OH

DB04641

H F

F

H

H

O

O

HO

OH

DB07036

O

N

DB07123

H

OH

O

H

HO

O

H

O

DB07177

O

O

OH

OH DB07500

H

OH

O

H

O

N

H

HO DB07567

HH

H

HO

H

OO

HH

O

DB07703

SHO

HN

N

DB07734

O

O

N

HNO

SH DB07735

HH

NH2

O

H

DB07883

H

H

HO

OH

HO

DB07933

HO

OH

O

HO

H

H

DB08020

H

OH

O

O

HH

H

H

OH

H

DB08224

HN

O

HO

HO

DB08442

H

O

O

O

HO

OH

DB08517

H

H

H

O

HO

HO

DB08737

Table 4. Results of different in silico filters of virtual screening protocol on the DrugBank database.

ID* Compound Drug Group MW

a LogP

b nHBDon

c nHBA

d nRotB

e PSA

f

Similarity

fused

score

Predicted

classh

Esti-

mated

potencyi

DB00227 Lovastatin approved, inves-

tigational 404.26 4.574 1 5 7 72.83 0.56 Act P

DB00486 Nabilone approved, inves-

tigational 372.27 6.155 1 1 6 46.53 0.573 Act P

DB00641 Simvastatin approved 418.27 4.775 1 5 7 72.83 0.56 Act P

DB00769 Hydrocortamate approved 475.29 1.235 2 7 8 104.14 0.618 Act P

DB00929 Misoprostol approved 382.27 3.567 2 5 14 83.83 0.557 Act P

DB02205

6-(1.1-Dimethylallyl)-2-(1-Hydroxy-1-Methylethyl)-2.3-Dihydro-7h-Furo[3.2-

G]Chromen-7-One

experimental 314.15 3.44 1 2 3 55.76 0.629 Act P

DB02329 Carbenoxolone experimental 570.36 6.855 2 7 6 117.97 0.759 Act P

DB02699 4-Oxoretinol experimental 300.21 4.185 1 2 5 37.3 0.553 Act P


(Table 4) contd….


a LogP

b nHBDon

c nHBA

d nRotB

e PSA

f

Similarity

fused

score

Predicted

classh

Esti-

mated

potencyi

DB02880 N-[1-(4-Bromophenyl)-

Ethyl]-5-Fluoro Salicylamide experimental 337.01 2.77 2 2 4 49.33 0.551 Act P

DB03007 9r.13r-Opda experimental 292.20 4.903 1 3 11 54.37 0.551 Act P

DB03451

1alpha.25-Dihydroxyl-20-Epi-22-Oxa-24.26.27-Trihomovitamin D3

experimental 460.355

3 5.452 3 4 9 69.92 0.623 Act P

DB03785

(3r.5r)-7-((1r.2r.6s.8r.8as)-2.6-Dimethyl-8-{[(2r)-2-Methylbutanoyl]Oxy}-

1.2.6.7.8.8a-Hexahydrona-phthalen-1-Yl)-3.5-

Dihydroxyheptanoic Acid


DB04324 Ovalicin experimental 298.18 0.905 2 5 4 79.29 0.563 Act P

DB04376 13-Acetylphorbol experimental 406.20 -0.741 4 7 3 124.29 0.566 Act P

DB04392

Bishydroxy[2h-1-Benzopyran-2-One.1.2-

Benzopyrone] experimental 336.06 0.156 0 4 2 86.74 0.573 Act P

DB04599 Aniracetam experimental 219.09 0.369 0 3 3 46.61 0.617 Act P

DB04641 3.7-Dihydroxynaphthalene-

2-carboxylic acid experimental 204.04 0.914 3 2 1 77.76 0.659 Act P

DB07036

(3aS.4R.9bR)-2.2-difluoro-4-(4-hydroxyphenyl)-6-

(methoxymethyl)-1.2.3.3a.4.9b-hexahydro-

cyclopenta[c]chromen-8-ol


DB07123 n-(4-methylbenzoyl)-4-

benzylpiperidine experimental 293.18 4.138 0 2 4 20.31 0.556 Act P

DB07177

(5e.13e)-11-hydroxy-9.15-dioxoprosta-5.13-dien-1-oic

acid experimental 350.21 2.517 2 5 12 91.67 0.598 Act P

DB07500

(2E)-1-[2-hydroxy-4-meth-oxy-5-(3-methylbut-2-en-1-

yl)phenyl]-3-(4-hydroxy-phenyl)prop-2-en-1-one


DB07567

(2r.3r.4s)-3-(4-hydroxyphenyl)-4-methyl-2-[4-(2-pyrrolidin-1-ylethoxy)

phenyl]chroman-6-ol


DB07703

(3r.4s.5s.7r.9e.11r.12r)-12-ethyl-4-hydroxy-3.5.7.11-

tetramethyloxacyclododec-9-ene-2.8-dione


DB07734 N-(1-benzylpiperidin-4-yl)-

4-sulfanylbutanamide experimental 292.16 2.058 1 3 7 71.14 0.708 Act P

DB07735

N-[1-(2.6-dimethoxybenzyl)piperidin-4-yl]-4-sulfanylbutanamide



(Table 4) contd….


a LogP

b nHBDon

c nHBA

d nRotB

e PSA

f

Similarity

fused

score

Predicted

classh

Esti-

mated

potencyi

DB07883

(2-amino-3-phenyl-bicyclo[2.2.1]hept-2-yl)-

phenyl-methanone experimental 291.16 2.801 1 2 3 43.09 0.602 Act P

DB07933

(3as.4r.9br)-4-(4-hydroxy-phenyl)-1.2.3.3a.4.9b-

hexahydrocyclopenta[c] chromen-8-ol


DB08020

(3as.4r.9br)-4-(4-hydroxy-phenyl)-6-(methoxymethyl)-

1.2.3.3a.4.9b-hexahydro-cyclopenta[c]chromen-8-ol


DB08224

(1s.7s.8s.8ar)-1.2.3.7.8.8a-hexahydro-7-methyl-8-[2-[(2r.4r)-tetrahydro-4-hy

droxy-6-oxo-2h-pyran-2-yl]ethyl]-1-naphthalenol


DB08517

(2S)-5-hydroxy-2-(4-hydroxyphenyl)-7-methoxy-2.3-dihydro-4H-chromen-4-

one


DB08737

(3as.4r.9br)-4-(4-hydroxy-phenyl)-1.2.3.3a.4.9b-

hexahydrocy-clopenta[c]chromen-9-ol


*ID =Code of DrugBank;aMW = Molecular weight; bLogP = Computed octanol/water partition coefficient; cnHBDon = Number of hydrogen bond donors; dnHBAc = Number of hydrogen bond acceptors; fPSA = Polar surface area; gSf =Fused Score for the maximum of the similarity; hAct =Active against the tyrosinase identified by Clasiffication MCS QSAR; iP = Potent inhibitor of tyrosinase identified by Potency MCS QSAR. CONFLICT OF INTEREST

The authors confirm that this article content has no con-flict of interest.

ACKNOWLEDGEMENTS

Authors acknowledge the supports from a National Foundation for Science and Technology of Vietnam (NA-FOSTED, Grant number 106-YS.05-2014.01). Casañola-Martin. G.M. thanks the program ‘Estades Temporals per a Investigadors Convidats’ for a fellowship to research at Va-lencia University (2013-2014). Marrero-Ponce, Y. thanks to the program ‘International Professor’ for a fellowship to work at Cartagena University in 2013-2014.

REFERENCES

[1] Robb, D.A. Copper Proteins and Copper Enzymes., CRC Press: Boca Raton, FL 1984.

[2] Briganti, S.; Camera, E.; Picardo, M. Chemical and instrumental approaches to treat hyperpigmentation. Pigment. Cell Res, 2003, 16, 101-10.

[3] Cullen, M.K. Genetic epidermal syndromes: disorders characterized by lentigines, In: The Pigmentary System: Physiology and Pathophysiology, J.J. Norlund; R.E. Boissy; V.J. Hearing; R.A. King; J.P. Ortonne, eds.; Oxford University Press: New York, 1998, pp. 760-766.

[4] Urabe, K.; Nakayama, J.; Hori, Y. Mixed epidermal and dermal hypermelanoses, In: The Pigmentary System: Physiology and

Pathophysiology, J.J. Norlund; R.E. Boissy; V.J. Hearing; R.A. King; J.P. Ortonne, eds.; Oxford University Press: New York 1998

pp. 909-91. [5] Parvez, S.; Kang, M.; Chung, H.S.; Cho, C.; Hong, M.C.; Shin,

M.K.; Bae, H. Survey and mechanism of skin depigmenting and lightening agents. Phytother. Res., 2006, 20, 921-34.

[6] Parvez, S.; Kang, M.; Chung, H.S.; Bae, H. Naturally occurring tyrosinase inhibitors: mechanism and applications in skin health, cosmetics and agriculture industries. Phytother. Res., 2007, 21, 805-16.

[7] Rescigno, A.; Sollai, F.; Pisu, B.; Rinaldi, A.; Sanjust, E. Tyrosinase inhibition: general and applied aspects. J. Enzy. Inhib. Med. Chem., 2002, 17, 207-18.

[8] Roy, K.; Mitra, I. On Various Metrics Used for Validation of Predictive QSAR Models with Applications in Virtual Screening and Focused Library Design. Combinatorial Chemistry & High Throughput Screening, 2011, 14, 450-474.

[9] Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Molecular Informatics, 2010, 29, 476-488.

[10] DiMasi, J.A.; Hansen, R.W.; Grabowski, H.G. The price of innovation: new estimates of drug development costs. J. Health Econ., 2003, 22, 151-85.

[11] Vidovic, D.; Busby, S.A.; Griffin, P.R.; Schürer, S.C. A Combined Ligand- and Structure-Based Virtual Screening Protocol Identifies Submicromolar PPAR Partial Agonists. ChemMedChem, 2011, 6, 94-103.

[12] Guido, R.; Oliva, G.; Andricopulo, A. Virtual screening and its integration with modern drug design technologies. Curr Med Chem, 2008, 15, 37–46.

[13] Walters, W.P.; Stahl, M.T.; Murcko, M.A. Virtual screening-an overview. Drug Discov. Today, 1998, 3, 160-178.


[14] Casanola-Martin, G.M.; Khan, M.T.; Marrero-Ponce, Y.; Ather, A.; Sultankhodzhaev, M.N.; Torrens, F. New tyrosinase inhibitors selected by atomic linear indices-based classification models. Bioorg. Med. Chem. Lett., 2006, 16, 324-30.

[15] Marrero-Ponce, Y.; Khan, M.T.; Casanola Martin, G.M.; Ather, A.; Sultankhodzhaev, M.N.; Torrens, F.; Rotondo, R. Prediction of Tyrosinase Inhibition Activity Using Atom-Based Bilinear Indices. ChemMedChem, 2007, 2, 449-478.

[16] Marrero-Ponce, Y.; Khan, M.T. H.; Casañola-Martín, G.M.; Ather, A.; Sultankhodzhaev, M.N.; Torrens, F. Atom-Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors. Results of In Silico Studies Supported by Experimentals Results. QSAR Comb. Sci., 2007, 26, 469-487.

[17] Marrero-Ponce, Y.; Khan, M.T. H.; Casañola-Martin, G.M.; Ather, A.; Sultankhodzhaev, M.N.; Garcia-Domenech, R.; Torrens, F.; Rotondo, R. Bond-based 2D TOMOCOMD-CARDD approach for drug discovery: Aiding decision-making in 'in silico' selection of new lead tyrosinase inhibitors.J. Comput-Aided Mol. Des., 2007, 21, 167-188.

[18] Le-Thi-Thu, H.; Casañola-Martín, G.M.; Marrero-Ponce, Y.; Rescigno, A.; Saso, L.; Parmar, V.S.; Torrens, F.; Rescigno, A. Novel coumarin-based tyrosinase inhibitors discovered by OECD principles-validated QSAR approach from an enlarged, balanced database. Mol. Divers., 2011, 15, 507-520.

[19] Le-Thi-Thu, H.; Marrero-Ponce, Y.; Casañola-Martin, G.M.; Cardoso, G.C.; Chávez, M.C.; Garcia, M.M.; Morell, C.; Torrens, F.; Abad, C. A comparative study of nonlinear machine learning for the “in silico” depiction of tyrosinase inhibitory activity from molecular structure. Mol. Inf., 2011, 30, 527-537.

[20] Le-Thi-Thu, H.; Cardoso, G.C.; Casañola-Martín, G.M.; Marrero-Ponce, Y.; Puris, A.; Torrens, F.; Abad, C. QSAR models for tyrosinase inhibitory activity description applying modern statistical classification techniques: A comparative study. Chemom. Int. Lab. Syst., 2010, 104, 249-259.

[21] Le-Thi-Thu, H.; Cruz, I.B.; Marrero-Ponce, Y.; Casañola-Martín, G.M.; Torrens, F.; Abad, C. Classifier Ensembles: the Best Choice for the Modeling of Chemicals against Hyper-pigmentation? J. Comput. Aided Mol. Des., 2011.

[22] Apodaca, R. Sixty-Four Free Chemistry Databases. October 12th, 2011 http://depth-first.com/articles/2011/10/12/sixty-four-free-chemistry-databases/

[23] Fourches, D.; Muratov, E.; Tropsha, A. Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research. J. Chem. Inf. Model, 2010, 50, 1189–1204.

[24] Dolle, R.E.; Bourdonnec, B.L.; Worm, K.; Morales, G.A.; Thomas, C.J.; Zhang, W. Comprehensive Survey of Chemical Libraries for Drug Discovery and Chemical Biology: 2009. J. Comb. Chem., 2010, 12, 765–806.

[25] PubChem. Jul 09, 2012 http://pubchem.ncbi.nlm.nih.gov [26] Proudfoot, J.R. Drugs, leads, and drug-likeness: an analysis of

some recently launched drugs. Bioorg. Med. Chem. Lett., 2002, 12, 1647-1650.

[27] Muegge, I. Selection Criteria for Drug-like Compounds. Med. Res.Rev., 2003, 23, 302-321.

[28] Lipinski, C.A. Drug-like properties and the causes of poor solubility and poor permeability.J. Pharmacol. Toxicol. Methods, 2000, 44, 235-249.

[29] Walters, W.P.; Ajay, X.; Murcko, M.A. Recognizing molecules with drug-like properties. Curr. Opin. Chem. Biol., 1999, 3, 384-387.

[30] Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev., 1997, 23, 3-25.

[31] Oprea, T.I. Property distribution of drug-related chemical databases. J Comput Aided Mol Des, 2000, 14, 251-264.

[32] Schneider, G.; Baringhaus, K.-H. Molecular Design: Concept and Applications, Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim 2008.

[33] Veber, D.F.; Johnson, S.R.; Cheng, H.Y.; Smith, B.R.; Ward, K.W.; Kopple, K.D. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem, 2002, 45, 2615-23.

[34] Monge, A.; Arrault, A.; Marot, C.; Morin-Allory, L. Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers. Mol Div, 2006, 10, 389-403.

[35] Vieth, M.; Siegel, M.G.; Higgs, R.E.; Watson, I.A.; Robertson, D.H.; Savin, K.A.; Durst, G.L.; Hipskind, P.A. Characteristic physical properties and structural fragments of marketed oral drugs. J Med Chem, 2004, 47, 224-232.

[36] Walters, W.P.; Murcko, M.A. Prediction of ‘drug-likeness’. Adv. Drug Delivery Rev., 2002, 54, 255-71.

[37] Marrero-Ponce, Y.; Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit): Santa Clara, Villa Clara, 2002.

[38] Steinbeck, C. Recent developments of the chemistry development kit (CDK) - an open-source Java library for chemo- and bioinformatics. Curr. Pharm. Des., 2006, 12, 2111-2120.

[39] Oprea, T.I.; Matter, H. Integrating virtual screening in lead discovery. Curr Opin Chem Biol, 2004, 8, 349-358.

[40] Klein, L.A. Sensor and Data Fusion Concepts and Applications, SPIE The International Society for Optical Engineering: Bellingham, WA 1999.

[41] Molecular Design Limited: San Leandro, CA 94577., 1989. [42] Ellis, D.; Furner-Hines, J.; Willett, P. On the measurement of inter-

linker consistency and retrieval effectiveness in hypertext databases. Perspect Inf Manag, 1993, 3, 128-149.

[43] Marrero-Ponce, Y.; Khan, M.T.; Casanola Martin, G.M.; Ather, A.; Sultankhodzhaev, M.N.; Torrens, F.; Rotondo, R. Prediction of Tyrosinase Inhibition Activity using Atom-Based Bilinear Indices. Chem Med Chem, 2007, 2, 449-478.

[44] Casanola-Martin, G.M.; Khan, M.T.; Marrero-Ponce, Y.; Ather, A.; Sultankhodzhaev, M.N.; Torrens, F. New tyrosinase inhibitors selected by atomic linear indices-based classification models. Bioorg Med Chem Lett, 2006, 16, 324-30.

[45] Marrero-Ponce, Y.; Khan, M.T. H.; Casañola-Martin, G.M.; Ather, A.; Sultankhodzhaev, M.N.; Torrens, F. Atom-Based 2D Quadratic Indices in Drug Discovery of Novel Tyrosinase Inhibitors. Results of In Silico Studies Supported by Experimentals Results. QSAR Comb Sci, 2007, 26, 469-487.

[46] Marrero-Ponce, Y.; Khan, M.T. H.; Casañola-Martin, G.M.; Ather, A.; Sultankhodzhaev, M.N.; Garcia-Domenech, R.; Torrens, F.; Rotondo, R. Bond-based 2D TOMOCOMD-CARDD approach for drug discovery: Aiding decision-making in 'in silico' selection of new lead tyrosinase inhibitors. J Comput Aided Mol Des, 2007, 21, 167-188.

[47] Casañola-Martin, G.M.; Marrero-Ponce, Y.; Khan, M.T. H.; Ather, A.; Sultan, S.; Torrens, F.; Rotondo, R. TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: Evaluation of different classification model combinations using bond-based linear indices. Bioorg Med Chem, 2007, 15, 1483-1503.

[48] Rescigno, A.; Casañola-Martin, G.M.; Sanjust, E.; Zucca, P.; Marrero-Ponce, Y. Vanilloid Derivatives as New Tyrosinase Inhibitors Driven by Virtual Screening-based QSAR Models. Drug Test Anal, 2011, 3, 176-181.

[49] Casañola-Martin, G.M.; Marrero-Ponce, Y.; Khan, M.T. H.; Khan, S.B.; Torrens, F.; Pérez-Jiménez, F.; Rescigno, A.; Abad, C. Bond-Based 2D Quadratic Fingerprints in QSAR Studies. Virtual and In vitro Tyrosinase Inhibitory Activity Elucidation. Chem Biol Drug Des, 2010, 76, 538-545.

[50] Oza, N.C.; Tumer, K. Classifier ensembles: Select real-world applications. Information Fusion, 2008, 9, 4–20.

[51] Fierrez-Aguilar, J.; Nanni, L.; Lopez-Penalba, J.; Ortega-Garcia, J.; Maltoni, D. In Audio- and Video-based Biometric Person Authentication (AVBPA 05); Springer LNCS-3546: New York, USA, July, 2005, pp. 523–532.

[52] Bologna, G.; Appel, R.D. In The ninth international conference on neural information processing: Singapore, 2002, pp. 2492–2496.

[53] Melville, P.; Mooney, R.J. Creating diversity in ensembles using artificial. Information Fusion: Special Issue on Diversity in Multiclassifier Systems, 2005, 6, 99–111.

[54] Lumini, A.; Nanni, L. An experimental comparison of ensemble of classifiers for biometric data. NeuroComputing, 2006 69, 1670-1673.

[55] Ho, T.K.; Hull, J.J.; Srihari, S.N. Decision combination in multiple classifier systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16, 66-76.

[56] Lyne, P.D. Structure-based virtual screening: an overview. Drug Discov Today, 2002, 7, 1047-55.

[57] Abagyan, R.A.; Totrov, M.M.; Kuznetsov, D.A. ICM – a new method for protein modeling and design: applications to docking

https://www.researchgate.net/publication/233748419_A_Comparative_Study_of_Nonlinear_Machine_Learning_for_the_In_Silico_Depiction_of_Tyrosinase_Inhibitory_Activity_from_Molecular_Structure?el=1_x_8&enrichId=rgreq-adf8079e-1606-4bc5-8e38-c1f3c5cf3fa0&enrichSource=Y292ZXJQYWdlOzI2MjU3MzU0NTtBUzozMDQ3NTI2MDMxNDAxMTRAMTQ0OTY3MDA5MTA4OQ==






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https://www.researchgate.net/publication/233748403_QSAR_models_for_tyrosinase_inhibitory_activity_description_applying_modern_statistical_classification_techniques_A_comparative_study?el=1_x_8&enrichId=rgreq-adf8079e-1606-4bc5-8e38-c1f3c5cf3fa0&enrichSource=Y292ZXJQYWdlOzI2MjU3MzU0NTtBUzozMDQ3NTI2MDMxNDAxMTRAMTQ0OTY3MDA5MTA4OQ==







and structure prediction from the distorted native conformation. J Comput Chem, 1994, 15, 488-506.

[58] Khan, M.T.; Fuskevag, O.M.; Sylte, I. Discovery of potent thermolysin inhibitors using structure based virtual screening and binding assays. J Med Chem, 2009, 52, 48-61.

[59] Khan, M.T.; Khan, R.; Wuxiuer, Y.; Arfan, M.; Ahmed, M.; Sylte, I. Identification of novel quinazolin-4(3H)-ones as inhibitors of thermolysin, the prototype of the M4 family of proteinases. 18, (12), 4317-27. Bioorg Med Chem, 2010, 18, 4317-4327.

[60] Halgren, T.A. Merck Molecular Force Field. III. Molecular Geometries and Vibrational Frequencies. J. Comput. Chem., 1996, 17, 553-586.

[61] Halgren, T.A. Merck Molecular Force Field. II. MMFF94 van der Waals and Electrostatic Parameters for Intermolecular Interactions. J Comp. Chem., 1996, 17, 520-552.

[62] Halgren, T.A. Merck Molecular Force Field.V. Extension of MMFF94 Using Experimental Data, Additional Computational Data and Empirical Rules. J Comp. Chem., 1996, 17, 616-641.

[63] Halgren, T.A. Merck Molecular Force Field: I. Basis, Form, Scope, Parameterization and Performance of MMFF94. J Comp. Chem., 1996, 17, 490-519.

[64] Halgren, T.A. MMFF VI. MMFF94s Option for Energy Minimization Studies. J Comp. Chem., 1999, 20, 720-729.

[65] Halgren, T.A. MMFF VII. Characterization of MMFF94, MMFF94s, and Other Widely Available Force Fields for Conformational Energies and for Intermolecular- Interaction Energies and Geometries. J Comp. Chem., 1999, 20, 730-748.

[66] Halgren, T.A.; Nachbar, R.B. Merck Molecular Force Field. IV. Conformational Energies and Geometries for MMFF94. J Comp. Chem., 1996, 17, 587-615.

[67] Schapira, M.; Totrov, M.; Abagyan, R. Prediction of the binding energy for small molecules, peptides and proteins. J Mol. Recog., 1999, 12, 177-190.

[68] Xu, J.; Hagler, A. Chemoinformatics and Drug Discovery. Molecules, 2002, 7, 566-700.

[69] Mc Farland, J.W.; Gans, D.J. Cluster Significance Analysis, In: Chemometric Methods in Molecular Design, Hvd. Waterbeemd, ed.; VCH Publishers: Weinheim, Ger, 1995, pp. 295–307.

[70] Sakuma, K.; Ogawa, M.; Sugibayashi, K.; Yamada, K.; Yamamoto, K. Relationship between tyrosinase inhibitory action and oxidation-reduction potential of cosmetic whitening ingredients and phenol derivatives. Arch Pharm Res, 1999, 22, 335-9.

[71] Shimizu, K.; Kondo, R.; Sakai, K. Inhibition of tyrosinase by flavonoids, stilbenes and related 4-substituted resorcinols: structure-activity investigations. Planta Med, 2000, 66, 11-5.

[72] Ashburn, T.T.; Thor, K.B. Drug repositioning: Identifying and developing new uses for existing drugs. Nat Rev Drug Discov, 2004, 3, 673-683.

[73] Subramanian, G.; Rao, S.N. An integrated computational workflow for efficient and quantitative modeling of renin inhibitors. Bioorg Med Chem, 2012, 20, 851-858.

[74] Habash, M.; Taha, M.O. Ligand-based modelling followed by synthetic exploration unveil novel glycogen phosphorylase inhibitory leads. Bioorg Med Chem, 2011, 19, 4746–4771.

Received: December 10, 2013 Revised: February 10, 2014 Accepted: February 11, 2014

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