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[1] Barreca, M. L., L. De Luca, et al. (2007). “Structure-Based Pharmacophore Identification of New Chemical Scaffolds as Non-Nucleoside Reverse Transcriptase Inhibitors.” J. Chem. Inf. Model. 47: 557-562.

            A structure-based mol. modeling approach was performed to identify novel structural characteristics and scaffolds that might represent new classes of HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs). The software LigandScout was used for identification and visualization of protein-ligand interaction sites and pharmacophore model generation. In the next step virtual screening of 3D multiconformational databases together with docking expts. allowed the identification of promising candidates for biol. testing. The pos. biol. results obtained confirm the validity of our work strategy.

[2] Bissoyi, A., C. Mahapatra, et al. (2013). “In silico prediction of novel drug molecule for migraine using blind docking.” Global J. Biotechnol. Biochem. 8: 25-32.

            This study deals with a comprehensive pathway of lead mol. design for migraine focusing on the emerging in silico trends and techniques which include generation of candidate mols., checking for their toxicity and human body likeliness, docking them with the target and ranking them based on their binding affinities. Protein sequence of Human receptor activity-modifying protein-1 (hRAMPI) was taken and prediction of missing side chain was performed using SQWRL. Energy minimization was done in Chimera using AMBER force field, followed by blind docking of the available drugs with Autodock software. Ligandscout was used to analyze the pharmacophore and its derivs. were derived using ProDRG. The derivs. were analyzed for drug-likeliness using Lipinski filters and ADME/Tox filter. Protein-ligand interactions for all the derivs. were detd. using Autodock and Hex. Sumatriptan was found to be the best ligand as it had the lowest binding energy. Its 15 derivs. were drawn with minimized energy. It was found that compd. no. 13, 9, 10 and 12 had lowest binding energy and all had drug likeliness. In all the four compds., ASP90A, TRP84A, ALA70A and TYR66A amino acids were found to participate in ligand-protein interaction. By the comparative anal. of the binding energies of all the complexes thus formed, three of the best ligands were chosen and analyzed for active amino acid groups mainly involved in ligand-protein interaction using Ligand Scout 2.0. The amino acid groups ALA70A and ASP90A were found to be involved in favorable binding interaction.

[3] Biswas, S., S. K. Kushwaha, et al. (2011). “Structural model based designing of inhibitors for glial fibrillary acidic protein.” Ann. Biol. Res. 2: 40-50.

            Glial fibrillary acidic protein (GFAP) is an intermediate filament Type III protein and is contg. three domains. The most conserved domain of GFAP is the rod domain. The present study has been made for in silico prediction to det. the three-dimensional structure of GFAP protein. It has been carried out through mol. modeling using MODELLER 9v5. Its active site residues has been predicted through comparative results of MODELLER 9v5. The ligands were designed using LigandScout 2.0. The designed ligand and receptor interaction studies were carried out through pharmacophore anal. followed by interaction studies using AUTODOCK4. Virtual screening of ligands has been performed by Molegro Virtual Docker. Analogs of ligands were generated through Chemsketch10.0 and ARG (258) was identified as a catalytic residue.

[4] Bryant, S. D., J. Dekeyser, et al. (2008). Ligand based virtual screening identifies CAR nuclear receptor activators and active opioid receptor molecules, American Chemical Society.

            Ligand-based virtual screening was utilized to identify active mols. targeted for splice variants of the nuclear constitutive androstane receptor (CAR) and G-protein coupled opioid receptors. Pharmacophores were derived from known active mols. using LigandScout. CAR pharmacophores were generated based on shared or merged features of CITCO, meclizine, pregnane and clotrimazole. Models for opioid ligands were developed using pharmacophores of 2′,6′-dimethyltyrosine (Dmt), 1,2,3,4-tetrahydroisoquinoline carboxylic acid (Tic) and low energy structures of Dmt-Tic-Bid (1H-benzimidazole-2-yl) derivs. The pharmacophores were deployed as 3D-search queries using Catalyst to screen several chem. databases. Two hits from the NCI database activated CAR2 in the biol. assays. An opioid pharmacophore identified three active hits; one exhibiting high f–affinity (Kif- = 0.075 nM). Pharmacophore development and virtual screening methods offer a feasible and effective approach to identify unique mols. relevant for activating CAR splice variants and an alternative approach to identify pharmacophores for virtual screening when bioactive ligand conformations and receptor binding sites are unknown.

[5] Buu, T. T. H. H., G. Wolber, et al. (2007). Scoring function to rank pharmacophoric alignments and its application to h1 antagonists, American Chemical Society.

            Virtual screening using 3D pharmacophores has evolved into an important and successful method for drug discovery over the last decades. We recently presented an efficient alignment method for super-positioning shared chem. features of pharmacophores and/or mols. in 3D space. Although efficient super-positioning techniques are of utmost importance to guarantee high throughput in virtual screening technologies, there is a need for automatically assessing the relevance and quality of a specific alignment for processing large data sets. Being aware of the problems of scoring functions in docking approaches the presented ranking approach has a different scope, since the position in 3D space is already defined by the single alignment soln. coming from the alignment algorithm. The presented scoring function is therefore designed not to select poses of one single mol., but to select those mols., which better fit to a pharmacophore (or a shared feature pharmacophore hypothesis) compared to others. Geometric, steric and energetical contributions have been used for implementation and parametrization and applied to a diverse set of H1 antagonists. We used a pseudo-structure-based approach using a homol. model and docked a data set of selected, active H receptor ligands using GOLD, and compared this to a ligand-based approach using multiple conformations generated by OMEGA within the LigandScout framework.

[6] Chauhan, P. and M. Shakya (2009). “Model based design of inhibitors for c-jun.” Bioinformation 4: 223-228.

            Literature shows that various molecular cascades are activated by stress, UV rays and pollutants leading to wrinkle formation of the skin. These cascades start from five types of receptors (EGFR, PDGFR, PAFR, IL1R, TNFRB) and terminate with the production of matrix metalloproteinase’s, which degrades collagen leading to wrinkle formation. Signaling pathway leading to wrinkle formation showed that c-jun is involved in these cascades. Therefore, c-jun is the preferential choice for inhibition to reduce the intensity of collagen degradation. Hence, the 3D structure of c-jun was modeled using segment based homology modeling by MODELLER 9v5. Evaluation of the constructed model was done by PROCHECK, WHAT CHECK and through RMSD/RMSF calculations. Ligands for the inhibitory sites were designed using LIGANDSCOUT. The interaction study of ligand and receptor was performed by AUTODOCK. A library of analogues was constructed for three known inhibitory sites. The receptor-analogue study was performed using the software MOLEGRO Virtual Docker. The analogues constructed from the designed novel reference ligands showed good binding with the receptor binding sites. It should be noted that these predicted data should be validated using suitable assays for further consideration.

[7] Chitranshi, N., S. Gupta, et al. (2013). “New molecular scaffolds for the design of Alzheimer’s acetylcholinesterase inhibitors identified using ligand- and receptor-based virtual screening.” Med. Chem. Res. 22: 2328-2345.

            The identification of important chem. features of acetylcholinesterase (AChE) inhibitors will be helpful to discover the potent candidate to inhibit the AChE activity. The best hypothesis from structure-based, Hypo1, one hydrophobic (H) pointed toward ILE444, TRP84, three hydrogen bond accepter (HBA), two hydrogen bond donor, one pos. ionizable toward TRP84, PHE330, and 11 excluded vol. sphere, were generated using LigandScout. Test and decoy sets were used to corroborate the best hypotheses, and the validated hypotheses were used to screen the Maybridge database. Only 14 compds. were prioritized as promising hits. The quant. structure-activity relation (QSAR) equation was developed based on 44 AChE inhibitors: 34 training set compds. and 10 test set compds. The model was developed using five information-rich descriptors-HBA, log P, HOF, EE, and dipole-playing an important role in detg. AChE inhibitory activity. QSAR model (model 3) yielded good statistical data, r2 = 0.723; q2 = 0.703; n = 34 for training set. This model was further validated using leave-one-out cross-validation approach, Fischer statistics (F), Y randomization test, and prediction based on the test data set. Mol. docking of 44 1-indanone derivs. & screened hits compds. were performed to identify the binding residue in AChE. Finally, the screened hits prioritized belong to several classes of mol. scaffolds with several available substitution positions that could allow chem. modification to enhance AChE binding affinity for Alzheimer disease.

[8] Chowdhury, A., S. Sen, et al. (2012). “Computational validation of 3-ammonio-3-(4-oxido- 1H-imidazol-1-ium-5-yl) propane-1, 1-bis (olate) as a potent anti-tubercular drug against mt-MetAP.” Bioinformation 8: 875-880.

            The advent of Multi Drug Resistant (MDR) strain of Mycobacterium tuberculosis (TB) necessitated search for new drug targets for the bacterium. It is reported that 3.3% of all new tuberculosis cases had multidrug resistance (MDR-TB) in 2009 and each year, about 0.44 million MDR-TB cases are estimated to emerge and 0.15 million people with MDR-TB die. Keeping such an alarming situation under consideration we wanted to design suitable anti tubercular molecules for new target using computational tools. In the work Methionine aminopeptidase (MetAP) of Mycobacterium tuberculosis was considered as target and three non-toxic phenolic=ketonic compounds were considered as ligands. Docking was done with Flex X and AutoDock 4.2 separately. Ten proven inhibitors of MetAP were collected from literature with their IC50 and were correlated using EasyQSAR to generate QSAR model. Activity of ligands in question was predicted from QSAR. Pharmacophore for each docking was generated using Ligandscout 3.0. Toxicity of the ligands in question was predicted on Mobyle@rpbs portal and Actelion property explorer. Molecular docking with target showed that of all three ligands, 3-ammonio-3-(4-oxido-1H-imidazol-1-ium-5-yl) propane-1, 1-bis (olate) has highest affinity (- 37.5096) and lowest IC50 (4.46 μM). We therefore, propose that -3-ammonio-3-(4-oxido-1H-imidazol-1-ium-5-yl) propane-1,1- bis(olate) as a potent MetAP inhibitor may be a new anti-tubercular drug particularly in the context of Multi Drug Resistant Tuberculosis (MDR-TB).

[9] Collier, H. C. and M. Gourley (2009). Pharmacophore elucidation of a triple reuptake inhibitor, American Chemical Society.

            Serotonin, norepinephrine and dopamine are neurotransmitters which are thought to affect mood. They are released into the synaptic cleft by a neuron and are subsequently attached to a neurotransmitter transporter, broken down and then transported back into the neuron. These neurotransmitters are also known as monoamines. Monoamine reuptake inhibitors block the binding of serotonin, norepinephrine and dopamine to their resp. transporters allowing them to remain in the synaptic cleft. This allows for increased stimulation on the dendrites and soma of the receiving neuron. There are problems assocd. with the use of the monoamine reuptake inhibitors: undesired side effects, effective for only 60-70% of the population, delayed onset of 2-6 wk. For these reasons, Triple reuptake inhibitors have been proposed to increase the efficacy with fewer side effects and to shorted responsive time. We are using LIGANDSCOUT 2.0 (a mol. modeling program) to design a triple reuptake inhibitor pharmacophore based on known uptake inhibitors. This pharmacophore will be used to search chem. databases to find mols. which match the criteria of the pharmacophore.

[10] De Luca, L., M. L. Barreca, et al. (2009). “Pharmacophore-Based Discovery of Small-Molecule Inhibitors of Protein-Protein Interactions between HIV-1 Integrase and Cellular Cofactor LEDGF/p75.” ChemMedChem 4: 1311-1316.

            The cellular protein lens epithelium-derived growth factor, or transcriptional coactivator p75 (LEDGF/p75), plays a crucial role in HIV integration. The protein-protein interactions (PPIs) between HIV-1 integrase (IN) and its cellular cofactor LEDGF/p75 may therefore serve as targets for the development of new anti-HIV drugs. In this work, a structure-based pharmacophore model for potential small-mol. inhibitors of HIV-1 IN-LEDGF/p75 interaction was developed using the LigandScout software. The 3D model obtained was used for virtual screening of our inhouse chem. database, CHIME, leading to the identification of compd. CHIBA-3002 as an interesting hit for further optimization. The rational design, synthesis and biol. evaluation of four derivs. were then carried out. Our studies resulted in the discovery of a new and more potent small mol. (7, CHIBA-3003)(I) that is able to interfere with the HIV-1 IN-LEDGF/p75 interaction at micromolar concn., representing one of the first compds. to show activity against these specific PPIs. Docking simulations were subsequently performed in order to investigate the possible binding mode of our new lead compd. to HIV-1 IN. This study is a valid starting point for the identification of anti-HIV agents with a different mechanism of action from currently available antiviral drugs.

[11] De Luca, L., S. Ferro, et al. (2014). “Structure-based screening for the discovery of new carbonic anhydrase VII inhibitors.” Eur. J. Med. Chem. 71: 105-111.

            Among the different mammalian isoforms of Carbonic Anhydrase, the hCA VII is mainly expressed in the brain where it is involved in several neurol. diseases. Thereby hCA VII has been validated as an attractive target for the discovery of selective inhibitors for the treatment of epilepsy and neurol. pain. To identify new chem. entities as carbonic anhydrase inhibitors (CAIs) targeting hCA VII, we used a structure-based approach. By LigandScout software we built pharmacophore models from crystal structures of two well-known CAIs in complex with hCA VII. A merged pharmacophore hypothesis has been obtained. Subsequently, a focused library of compds. was screened against pharmacophore model and the most interesting hits were docked into the crystal structure of hCA VII. As a result, we identified new compds. displaying significant CA inhibitory effects in the nanomolar range.

[12] Dornhofer, A., M. Biely, et al. (2006). A novel 2D depiction method using breadth-first ordering and an adapted 2D force field, Computer Aided Drug Design & Development Society in Turkey.

            A 2D depiction method was developed based on graph-theor. abstraction of non-ring atoms and complete ring systems as nodes and resembles previously presented ideas using reduced graphs. The method was manually evaluated for speed and accuracy with virtual mol. libraries generated by the software application CombiGen as well as with ligands from the Protein Data Bank where the software application LigandScout has been used to ext. the ligands. The algorithm proved to be a robust, usable and fast tool for the fully automated two-dimensional depiction of mols.

[13] Ehrman, T. M., D. J. Barlow, et al. (2010). “In silico search for multi-target anti-inflammatories in Chinese herbs and formulas.” Bioorg. Med. Chem. 18: 2204-2218.

            Chinese herbs were screened for compds. which may be active against four targets involved in inflammation, using pharmacophore-assisted docking. Multiple LigandScout (LS) pharmacophores built from ligand-receptor complexes in the protein databank (PDB) were first employed to select compds. These compds. were then docked using LS-derived templates and ranked according to docking score. The targets comprised cyclooxygenases 1&2 (COX), p38 MAP kinase (p38), c-Jun terminal-NH2 kinase (JNK) and type 4 cAMP-specific phosphodiesterase (PDE4). The results revealed that multi-target inhibitors are likely to be relatively common in Chinese herbs. Details of their distribution are given, in addn. to exptl. evidence supporting these results. Examples of compds. predicted to be active against at least three targets are presented, and their features outlined. The distribution of herbs contg. predicted inhibitors was also analyzed in relation to 192 Chinese formulas from over 50 herbal categories. Among those found to contain a high proportion of these herbs were formulas traditionally used to treat fever, headache, rheumatoid arthritis, inflammatory bowel disorders, skin disease, cancer, and traumatic injury. Relationships between multi-target drug discovery and Chinese medicine are discussed.

14] Ghorab, M. M., Z. H. Ismail, et al. (2013). “Synthesis, antimicrobial evaluation and molecular modelling of novel sulfonamides carrying a biologically active quinazoline nucleus.” Arch. Pharmacal Res. 36: 660-670.

            A novel series of quinazolines, triazoloquinazolines, and triazinoquinazoline bearing a biol. active sulfonamide moiety were synthesized, utilizing Me 2-isothiocyanatobenzoate. Some of the newly synthesized compds. revealed promising bacterial growth inhibition, compared with ampicillin as the ref. drug. A LigandScout approach-generated pharmacophore model for the Staph aureus bacterial growth inhibition was done. The degree of fitting of the test set compds. to the generated hypothetical model revealed a qual. measure of the more or less microbial inhibition of Staphylococcus aureus. Several compds., which revealed significant activity, are able to effectively satisfy the proposed pharmacophore geometry, using the energy accessible conformers (Econf < 20 kcal/mol).

15] Golbabaei, S., R. Bazl, et al. (2013). “Urease inhibitory activities of β-boswellic acid derivatives.” Daru, J. Pharm. Sci. 21: 2.

            Boswellia carterii have been used in traditional medicine for many years for management different gastrointestinal disorders. In this study, we wish to report urease inhibitory activity of four isolated compd. of boswellic acid deriv. 4 Pentacyclic triterpenoid acids were isolated from Boswellia carterii and identified by NMR and Mass spectroscopic anal. 3-O-acetyl-9,11-dehydro-β-boswellic acid; 2. 3-O-acetyl-11-hydroxy-β-boswellic acid; 3. 3-O-acetyl-11-keto-β-boswellic acid and 4. 11-keto-β-boswellic acid. Their inhibitory activity on Jack bean urease were evaluated. Docking and pharmacophore anal. using AutoDock 4.2 and Ligandscout 3.03 programs were also performed to explain possible mechanism of interaction between isolated compds. and urease enzyme. It was found that compd. 1 has the strongest inhibitory activity against Jack bean urease (IC50 = 6.27 ± 0.03 μM), compared with thiourea as a std. inhibitor (IC50 = 21.1 ± 0.3 μM). The inhibition potency is probably due to the formation of appropriate hydrogen bonds and hydrophobic interactions between the investigated compds. and urease enzyme active site and confirms its traditional usage.

[16] Golbabaei, S., R. Bazl, et al. (2013). “Urease inhibitory activities of β-boswellic acid derivatives.” Daru 21: 2.

            UNLABELLED: BACKGROUND AND THE PURPOSE OF THE STUDY: Boswellia carterii have been used in traditional medicine for many years for management different gastrointestinal disorders. In this study, we wish to report urease inhibitory activity of four isolated compound of boswellic acid derivative. METHODS: 4 pentacyclic triterpenoid acids were isolated from Boswellia carterii and identified by NMR and Mass spectroscopic analysis (compounds 1, 3-O-acetyl-9,11-dehydro-β-boswellic acid; 2, 3-O-acetyl-11-hydroxy-β-boswellic acid; 3. 3-O- acetyl-11-keto-β-boswellic acid and 4, 11-keto-β-boswellic acid. Their inhibitory activity on Jack bean urease were evaluated. Docking and pharmacophore analysis using AutoDock 4.2 and Ligandscout 3.03 programs were also performed to explain possible mechanism of interaction between isolated compounds and urease enzyme. RESULTS: It was found that compound 1 has the strongest inhibitory activity against Jack bean urease (IC50 = 6.27 ± 0.03 μM), compared with thiourea as a standard inhibitor (IC50 = 21.1 ± 0.3 μM). CONCLUSION: The inhibition potency is probably due to the formation of appropriate hydrogen bonds and hydrophobic interactions between the investigated compounds and urease enzyme active site and confirms its traditional usage.

[17] Grona, S., P. Markt, et al. (2007). How to improve structure-based pharmacophores by modeling the binding site shape, American Chemical Society.

            Exptl. X-ray crystal structures of drug target proteins in complex with small ligand mols. are frequently used to create structure-based pharmacophore models, in order to find new promising drug candidates by virtual screening. Few authors also describe the inclusion of the shape of the binding site to reduce the no. of false-pos. hits that are simply to big to bind. Here we present the first systematic study of such exclusion shape models to establish guidelines for binding site assisted structure-based pharmacophore modeling: Models for different drug targets (PPAR, Factor Xa, CDK2, COX1) were created in LigandScout and used for database screening in Catalyst. ROC curves were plotted to visualize the effects of different parameters such as no., size, and flexibility of the features creating the shape restriction on the ability of retrieving known actives from a collection of drug-like mols. Furthermore, the effect on computational time was studied.

[18] Gupta, A., V. Sharma, et al. (2013). “Comparative Molecular docking analysis of DNA Gyrase subunit A in Pseudomonas aeruginosaPAO1.” Bioinformation 9: 116-120.

            Pseudomonas aeruginosa is an opportunistic bacterium known for causing chronic infections in cystic fibrosis and chronic obstructive pulmonary disease (COPD) patients. Recently, several drug targets in Pseudomonas aeruginosa PAO1 have been reported using network biology approaches on the basis of essentiality and topology and further ranked on network measures viz. degree and centrality. Till date no drug/ligand molecule has been reported against this targets.In our work we have identified the ligand /drug molecules, through Orthologous gene mapping against Bacillus subtilis subsp. subtilis str. 168 and performed modelling and docking analysis. From the predicted drug targets in PA PAO1, we selected those drug targets which show statistically significant orthology with a model organism and whose orthologs are present in all the selected drug targets of PA PAO1.Modeling of their structure has been done using I-Tasser web server. Orthologous gene mapping has been performed using Cluster of Orthologs (COGs) and based on orthology; drugs available for Bacillus sp. have been docked with PA PAO1 protein drug targets using MoleGro virtual docker version 4.0.2.Orthologous gene for PA3168 gyrA is BS gyrAfound in Bacillus subtilis subsp. subtilis str. 168. The drugs cited for Bacillus sp. have been docked with PA genes and energy analyses have been made. Based on Orthologous gene mapping andin-silico studies, Nalidixic acid is reported as an effective drug against PA3168 gyrA for the treatment of CF and COPD.

[19] Khan, A. H., A. Prakash, et al. (2010). “Virtual screening and pharmacophore studies for ftase inhibitors using Indian plant anticancer compounds database.” Bioinformation 5: 62-66.

            Farnesyl transferase (FTase) is an enzyme responsible for post-translational modification in proteins having a carboxy-terminal CaaX motif in human. It catalyzes the attachment of a lipid group in proteins of RAS superfamily, which is essential in signal transduction. FTase has been recognized as an important target for anti cancer therapeutics. In this work, we performed virtual screening against FTase with entire 125 compounds from Indian Plant Anticancer Database using AutoDock 3.0.5 software. All compounds were docked within binding pocket containing Lys164, Tyr300, His248 and Tyr361 residues in crystal structure of FTase. These complexes were ranked according to their docking score, using methodology that was shown to achieve maximum accuracy. Finally we got three potent compounds with the best Autodock docking Score (Vinorelbine: -21.28 Kcal/mol, Vincristine: -21.74 Kcal/mol and Vinblastine: -22.14 Kcal/mol) and their energy scores were better than the FTase bound co-crystallized ligand (L- 739: -7.9 kcal/mol). These three compounds belong to Vinca alkaloids were analyzed through Python Molecular Viewer for their interaction studies. It predicted similar orientation and binding modes for these compounds with L-739 in FTase.Thus from the complex scoring and binding ability it is concluded that these Vinca alkaloids could be promising inhibitors for FTase. A 2-D pharmacophore was generated for these alkaloids using LigandScout to confirm it. A shared feature pharmacophore was also constructed that shows four common features (one hydogen bond Donar, Two hydrogen bond Acceptor and one ionizable area) help compounds to interact with this enzyme.

[20] Khan, H. N., S. Kulsoom, et al. (2012). “Ligand based pharmacophore model development for the identification of novel antiepileptic compound.” Epilepsy Res. 98: 62-71.

            Summary: Epilepsy is a common neurol. disorder throughout the world which is characterized by recurrent unprovoked epileptic seizures. A need exists for the development of new antiseizure drugs with improved efficacy and tolerability, as several of the currently available antiepileptic drugs (AEDs) have been assocd. with severe side effects. A ligand based pharmacophore approach has been generated for 44 new antiepileptic compds. with emphasis on the development of new drugs by using LigandScout software and distance estn. using Jmol. The pharmacophore of the compds. contained three features hydrophobic unit, hydrogen bonding domain and electron donor. The pharmacophore models derived were then filtered using the Lipinski’s rule of five criteria and orally bio-available compds. were obtained. Thus, this approach was able to reclaim few leads which had projected inhibitory activity alike to most active compds. with suitable calcd. drug-like properties and therefore they could be recommended for further studies.

[21] Khan, M. T. H., Y. Wuxiuer, et al. (2012). “Binding modes and pharmacophore modelling of thermolysin inhibitors.” Mini-Rev. Med. Chem. 12: 515-533.

            In the present paper 25 known thermolysin inhibitors were docked into thermolysin using the Internal Coordinate Mechanics (ICM) software. Pharmacophore models based on thermolysin binding modes and activity profiles were generated using the LigandScout program. The docking studies indicated that all 25 inhibitors coordinated the catalytic zinc in bidentate or monodentate geometry. A “three-point” pharmacophore model was proposed which consisted of a hydrophobic group, a neg. ionizable group and a hydrogen bond acceptor group. Finally the pharmacophore model has been tested against a small compd. library contg. 18 highly, moderately, less active as well as inactive compds. The screening indicated that the pharmacophore model could, identify highly active compds. in front of inactive or less active ones.

[22] Kirchmair, J., S. Ristic, et al. (2007). “Fast and Efficient in Silico 3D Screening: Toward Maximum Computational Efficiency of Pharmacophore-Based and Shape-Based Approaches.” J. Chem. Inf. Model. 47: 2182-2196.

            In continuation of our recent studies on the quality of conformational models generated with CATALYST and OMEGA we present a large-scale survey focusing on the impact of conformational model quality and several screening parameters on pharmacophore-based and shape-based virtual high throughput screening (vHTS). Therefore, we collected known active compds. of CDK2, p38 MAPK, PPAR-γ, and factor Xa and built a set of druglike decoys using ilib:diverse. Subsequently, we generated 3D structures using CORINA and also calcd. conformational models for all compds. using CAESAR, CATALYST FAST, and OMEGA. A widespread set of 103 structure-based pharmacophore models was developed with LigandScout for virtual screening with CATALYST. The performance of both database search modes (FAST and BEST flexible database search) as well as the fit value calcn. procedures (FAST and BEST fit) available in CATALYST were analyzed in terms of their ability to discriminate between active and inactive compds. and in terms of efficiency. Moreover, these results are put in direct comparison to the performance of the shape-based virtual screening platform ROCS. Our results prove that high enrichment rates are not necessarily in conflict with efficient vHTS settings: In most of the expts., we obtained the highest yield of actives in the hit list when parameter sets for the fastest search algorithm were used.

[23] Krovat, E. M., K. H. Fruehwirth, et al. (2005). “Pharmacophore Identification, in Silico Screening, and Virtual Library Design for Inhibitors of the Human Factor Xa.” J. Chem. Inf. Comput. Sci. 45: 146-159.

            Factor Xa inhibitors are innovative anticoagulant agents that provide a better safety/efficacy profile compared to other anticoagulative drugs. A chem. feature-based modeling approach was applied to identify crucial pharmacophore patterns from 3D crystal structures of inhibitors bound to human factor Xa using the software LIGANDSCOUT and CATALYST. The complex structures were selected regarding the criteria of high inhibitory potency (i.e. all ligands show Ki values against factor Xa in the subnanomolar range) and good resoln. (i.e. at least 2.2 Å) in order to generate selective and high quality pharmacophore models. The resulting chem.-feature based hypotheses were used for virtual screening of com. mol. databases such as the WDI database. Furthermore, a ligand-based mol. modeling approach was performed to obtain common-feature hypotheses that represent the relevant chem. interactions between 10 bioactive factor Xa inhibitors and the protein, resp. In a next step a virtual combinatorial library was designed in order to generate new compds. with similar chem. and spatial properties as known inhibitors. The software tool ILIB DIVERSE was used for this procedure in order to provide new scaffolds of this group of anticoagulants. Finally the authors present the combination of these two techniques, hence virtual screening was performed with selective pharmacophore models in a focused virtual combinatorial database. De novo derived mol. scaffolds that were able to adequately satisfy the pharmacophore criteria are revealed and are promising templates for candidates for further development.

[24] Langer, T. (2010). Discovery of PPIs using chemical feature-based pharmacophore models, American Chemical Society.

            Pharmacophore-based virtual screening methods have proven to be successful for the rapid identification of hit compds. for a wide variety of targets. In this paper we present the development and application of pharmacophore-based methods for identifying PPIs. Using both an heuristic approach (LigandScout, Fig. 1) and an interaction energy-based method (GBPM) bio-active compds. could be retrieved selectively from large 3D mol. databases. Figure 1: LigandScout representation of one of the pharmacophore models used for virtual screening. Activity was confirmed by the results of biol. testing. The compds. were found to inhibit XIAP and therefore are interesting as promising starting points for further optimization as anti-cancer agents.

[25] Langer, T. and G. Wolber (2003). “Feature-based pharmacophores: virtual screening for lead identification.” G.I.T. Lab. J., Eur. 7: 208-210.

            A review. The efficiency of combinatorial chem. and high throughput screening can largely be enhanced if focused libraries are designed. Lead structure discovery will be more successful and the risk of failure in later stages will diminish if synthetic medicinal chem. efforts are paired with intelligent in silico filters. The software package LigandScout for high throughput feature-based pharmacophore model generation and tools for reverse virtual screening may help to design in silico lead compds. that are more robust and possess higher chance to reach clin. research phases.

[26] Langer, T. and G. Wolber (2004). “Virtual combinatorial chemistry and in silico screening: Efficient tools for lead structure discovery?” Pure Appl. Chem. 76: 991-996.

            A review. In this article, an overview of the most common ligand-based in silico screening techniques is given together with an example on the recent successful application of combined use of pharmacophore modeling, database mining, and biol. assays. Addnl., a new approach for structure-based high-throughput pharmacophore model generation is presented. The LigandScout program contains an automated method for creating pharmacophore models from exptl. detd. structure data, e.g., publicly available from the Brookhaven Protein Databank (PDB). In a first step, known algorithms were implemented and improved to ext. small-mol. ligands from the PDB including assignment of hybridization states and bond orders. Second, from the interactions of the interpreted ligands with relevant surrounding amino acids, pharmacophore models reflecting functional interactions like H-bonds or ionic transfer interactions were created. These models can be used for screening mol. databases for similar modes of actions on the one hand, or for screening one single compd. for potential side-effects (reversed screening) on the other hand. The implementation was done using the ilib framework, which also formed the basis of the software tool CombiGen, a fragment-based virtual combinatorial library generation program enabling the user to obtain in silico compd. collections with high drug-likeness.

[27] Langer, T. and G. Wolber (2006). High throughput pharmacophore model generation from ligand-target complexes as a basis for activity profiling, Computer Aided Drug Design & Development Society in Turkey.

            A new approach for structure-based high throughput pharmacophore model generation is presented together with the application concept of using such pharmacophore models to bioactivity profiling of potential new drug candidates. The implementation of LigandScout is built upon the ilib framework, which also formed the basis of the software tool Comb Gen, a fragment-based virtual combinatorial library generation program enabling the user to obtain in silico compd. collections with high drug-likeness. The pharmacophore models obtained in high throughput mode can be used for screening mol. databases for similar models of actions on the one hand, or for establishing bioactivity profiles for one single compd. on the other hand.

[28] Langer, T., G. Wolber, et al. (2008). Structure-focused pharmacophore models for teaching and exploring protein-ligand interactions, American Chemical Society.

            Feature-based 3D pharmacophore models have proven to be highly valuable query tools for database mining in virtual screening application scenarios. They reflect in a transparent manner the interactions between ligands and their resp. binding sites and can be visualized easily. Therefore, in teaching cheminformatics related methods, such models are extremely interesting for exploring and understanding binding interaction patterns quickly and easily. We have created LigandScout, a user-friendly pharmacophore-generating platform to allow users to generate, visualize, and manipulate intuitively 3D pharmacophore models starting with ligand-target complex structures. In addn. to the 3D visualization, our program includes a sophisticated 2D depiction algorithm for displaying a projection of the binding interactions. Since the core application is written in Java, it can be included easily into web-based e-learning and teaching protocols such as the PharmXplorer platform.

[29] Lu, X., Y. Chen, et al. (2009). “Pharmacophore Guided 3D-QSAR CoMFA Analysis of Amino Substituted Nitrogen Heterocycle Ureas as KDR Inhibitors.” QSAR Comb. Sci. 28: 1524-1536.

            Vascular endothelial growth factor-2 receptors (VEGR-2) or kinase insertdomain receptor (KDR) is a promising target for the development of novel anticancer drugs. To understand the structural basis for KDR inhibitory activity, 3D-QSAR study using CoMFA anal. was performed on a set of amino substituted nitrogen heterocyclic urea derivs. Due to the flexibility and structural diversity of investigated derivs., we applied pharmacophore based alignment to construct reliable 3D-QSAR models. The pharmacophore model was generated based on the verified docked conformation of compd. 2 with KDR crystal structure using LigandScout 2.0 software. The constructed CoMFA model produced reasonable statistics, with rcv2 =.507 and conventional r2 = 0.982. The predictive power of the developed model was obtained using a test set of 16 mols., giving predictive correlation coeff. of 0.540. Mol. modeling and CoMFA contour anal. were performed to obtain useful information about the structural requirements for the KDR inhibitors which could be utilized in its future design.

[30] Lu, X.-Y., Y.-D. Chen, et al. (2010). “3D-QSAR studies of arylcarboxamides with inhibitory activity on InhA using pharmacophore-based alignment.” Chem. Biol. Drug Des. 75: 195-203.

            Enoyl acyl carrier protein reductase (InhA) is a promising target for the development of antituberculosis drugs. The InhA-bound conformation of an indole-5-amide inhibitor (Genz 10850) (PDB code: IP44) was used to build a pharmacophore model by LigandScout. This model was then successfully used to identify the bioactive conformation and align 40 structurally diverse arylcarboxamide derivs. Comparative mol. field anal. (CoMFA) and comparative mol. similarity indexes anal. (CoMSIA) were performed on arylcarboxamides-based InhA inhibitors based on pharmacophore alignment. The best prediction was obtained with CoMSIA model combining steric and electrostatic fields (r2cv = 0.729, r2 = 0.972). The model was validated by an external test set, which gave a good predictive value (r2pred = 0.826). Graphical interpretation of the results revealed important structural features of the zarylcarboxamides related to the active site of InhA. The results may be exploited for further design and virtual screening for some novel InhA inhibitors.

[31] Mangold, M., G. M. Spitzer, et al. (2007). Evaluation of pharmacophore modeling based virtual screening: comparative assessment of catalyst, phase and MOE at the example of HRV coat protein, American Chemical Society.

            The three pharmacophore modeling programs Catalyst (Accelrys), Phase (Schrodinger), and MOE (Chem. Computing Group) are evaluated with respect to their virtual screening algorithms with either a given structure based pharmacophore model generated by LigandScout (Inte:ligand) or a ligand based model created within the resp. program. Several models are generated at the example of Human Rhinovirus (HRV) coat protein. The most restrictive ones are introduced in more detail, including information about the tools available to increase the selectivity of the models. The hits found by at least two search algorithms within the Derwent World Drug Index 2006 are mostly virucides, proving the selectivity of the models in each program.

[32] Montes-Grajales, D. and J. Olivero-Verbel (2013). “Computer-aided identification of novel protein targets of bisphenol A.” Toxicol. Lett. 222: 312-320.

            The xenoestrogen bisphenol A (2,2-bis-(p-hydroxyphenyl)-2-propane, BPA) is a known endocrine-disrupting chem. used in the fabrication of plastics, resins and flame retardants, that can be found throughout the environment and in numerous every day products. Human exposure to this chem. is extensive and generally occurs via oral route because it leaches from the food and beverage containers that contain it. Although most of the effects related to BPA exposure have been linked to the activation of the estrogen receptor (ER), the mechanisms of the interaction of BPA with protein targets different from ER are still unknown. Therefore, the objective of this work was to use a bioinformatics approach to identify possible new targets for BPA. Docking studies were performed between the optimized structure of BPA and 271 proteins related to different biochem. processes, as selected by text-mining. Refinement docking expts. and conformational analyses were carried out using LigandScout 3.0 for the proteins selected through the affinity ranking (lower than -8.0 kcal/mol). Several proteins including ERR gamma (-9.9 kcal/mol), and dual specificity protein kinases CLK-4 (-9.5 kcal/mol), CLK-1 (-9.1 kcal/mol) and CLK-2 (-9.0 kcal/mol) presented great in silico binding affinities for BPA. The interactions between those proteins and BPA were mostly hydrophobic with the presence of some hydrogen bonds formed by leucine and asparagine residues. Therefore, this study suggests that this endocrine disruptor may have other targets different from the ER.

[33] Montes-Grajales, D., J. Olivero-Verbel, et al. (2013). “DDT and derivatives may target insulin pathway proteins.” J. Braz. Chem. Soc. 24: 558-572.

            DDT (1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethane) has been linked to type 2 diabetes. Accordingly, a virtual screening was used to detect possible new targets for DDT and its derivs. in the insulin signaling. Compd. structures were optimized by mol. mechanics and then by d. functional theory (DFT), and protein structures were obtained from Protein Data Bank (PDB). Docking between 59 proteins involved in the insulin pathway according to data mining on PubMed, and DDT-related mols. as ligands, was performed with AutoDock Vina program. Residue-ligand interactions were checked with LigandScout 2.0 software. The greatest binding affinity score was found for the complex AKT-1 (PDB_ID:3cqu)/p,p’-DDE. Other proteins with good affinities for DDT derivs. were eIF4E (PDB_ID: 1wkw) and PKA (PDB_ID: 2qcs). These data show the theor. plausibility that DDT and related chems. could interfere with insulin receptor-related targets. Although biochem. mechanisms are still uncertain, diabetes prevalence in people exposed to DDT could be influenced by the binding of these compds. to proteins involved in the insulin pathway.

[34] Olla, S., F. Manetti, et al. (2009). “Indolyl-pyrrolone as a new scaffold for Pim1 inhibitors.” Bioorg. Med. Chem. Lett. 19: 1512-1516.

            Pim1 belongs to a family of serine/threonine kinases, which is involved in the control of cell growth, differentiation, and apoptosis. Pim1 plays a pivotal role in cytokine signaling and is implicated in the development of a large no. of tumors, representing a very attractive target for anticancer therapy. In this work, we applied a virtual screening protocol aimed at identifying small mols. able to inhibit Pim1 activity. The search of novel inhibitors was performed through a structure-based mol. modeling approach, taking advantage of the availability of the three-dimensional crystal structure of inhibitors bound to Pim1. Starting from the knowledge of protein-ligand complexes, the software LigandScout was used to generate pharmacophoric models, in turn used as queries to perform a virtual screening of databases, followed by docking expts. As a result, a restricted set of candidates for biol. testing was identified. Finally, among the six compds. selected as potential inhibitors of Pim1, two candidates endowed with a significant activity against Pim1 emerged. Interestingly, one of these compds. has a chem. scaffold different from inhibitors previously identified.

[35] Parveen, M., A. Ali, et al. (2013). “Synthesis, characterization, biological evaluation and in silico screening of oxadiazinanones.” Med. Chem. Res. 22: 3085-3095.

            A series of novel oxadiazinone derivs., namely 2-methyl-2-phenyl-1,3,4-oxadiazin-5-one, 2-(3-hydroxyphenyl)-2-methyl-1,3,4-oxadiazin-5-one, 2-(4-hydroxyphenyl)-2-methyl-1,3,4-oxadiazin-5-one (I), 2-(2,4-dihydroxyphenyl)-2-methyl-1,3,4-oxadiazin-5-one and 2-(2,5-dihydroxyphenyl)-2-methyl-1,3,4-oxadiazin-5-one were designed and the synthesis of the target compds. was achieved by a reaction of acetophenone and its derivs. with cyanoacetic acid hydrazide. The structural assignments of the products were done on the basis of IR, 1H NMR, 13C NMR, MS, and anal. data. The in vitro antioxidant and antimicrobial activity of all the synthesized compds. was tested by a DPPH and disk diffusion method, resp. The synthesized compds. were screened against Gram-pos. and Gram-neg. bacterial and fungal strains. One compd. showed the highest inhibition comparable with std. antibiotic drugs Ciprofloxacin and Amphotericin B. The antibacterial activity of the synthesized compds. was further investigated with the help of in silico docking study using Discovery studio 3.1, Molego Virtual Docker and LigandScout to predict the active sites.

[36] Patel, B. and M. Ghate (2013). “Computational studies on structurally diverse dipeptidyl peptidase IV inhibitors: an approach for new antidiabetic drug development.” Med. Chem. Res. 22: 4505-4521.

            Dipeptidyl peptidase IV (DPP-IV) deactivates the natural hypoglycemic incretin hormone GLP-1. Inhibition of this enzyme restores glucose homeostasis in diabetic patients making it an attractive target for the development of new antidiabetic drugs. With this in mind, we suggested an in silico work flow for the identification of novel DPP-IV inhibitors. Ligand-based and structure-based pharmacophores were designed using HipHop program provided in catalyst and ligandScout 3.0 software, resp. Generated models were validated by receiver operating characteristic curve anal., Guner-Henry scoring method and by pharmacophore-based screening of marketed DPP-IV inhibitors. Ligand-based pharmacophore model A scored 0.8 AUC value, 0.865 Guner-Henry score and gave all marketed DPP-IV inhibitors as hits through screening while structure-based pharmacophore B scored 0.77 AUC value, 0.66 Guner-Henry score and gave four marketed DPP-IV inhibitors as hits (except alogliptin) out of five. These validated pharmacophores have effectively been used in search of three databases, Maybridge hitfinder collection, Chemdiv, and Asinex. Resulting hits were subjected to mol. docking using ligandfit program. Five hit compds. namely Asinex ASN 09417841, AW 00785, ChemDiv 0173-0023, ChemDiv 0276-0112, and ChemDiv 8010-1357 scored high Ligscore1 and -PLP1 score comparable to std. drug sitagliptin. Good interactions were found with important residues like Glu205, Glu206, Tyr662, Phe357, Arg358, Tyr666 etc. They were reported as novel virtual leads to design potent DPP-IV inhibitors.

[37] Radwan, A. A. and W. M. Abdel-Mageed (2014). “In silico studies of quinoxaline-2-carboxamide 1,4-di-n-oxide derivatives as antimycobacterial agents.” Molecules 19: 2247-2260.

            Molecular modelling studies were performed on some previously reported novel quinoxaline-2-carboxamide 1,4-di-N-oxide derivatives (series 1-9). Using the LigandScout program, a pharmacophore model was developed to further optimize the antimycobacterial activity of this series of compounds. Using the Dock6 program, docking studies were performed in order to investigate the mode of binding of these compounds. The molecular modeling study allowed us to confirm the preferential binding mode of these quinoxaline-2-carboxamide 1,4-di-N-oxide derivatives inside the active site. The obtained binding mode was as same as that of the novobiocin X-ray structure.

[38] Sakkiah, S., S. Thangapandian, et al. (2011). “Pharmacophore based virtual screening, molecular docking studies to design potent heat shock protein 90 inhibitors.” Eur. J. Med. Chem. 46: 2937-2947.

            The identification of important chem. features of Heat Shock Protein 90 (HSP90) inhibitors will be helpful to discover the potent candidate to inhibit the HSP90 activity. The best hypothesis from Hip-Hop, Hypo1, one hydrogen bond donor (HBD), two hydrogen bond acceptors (HBA), and two hydrophobic (H) and structure-based hypothesis, SBHypo1, one HBA, one HBD and four H features, were generated using Discovery Studio and LigandScout, resp. Test and decoy sets were used to corroborate the best hypotheses and the validated hypotheses were used to screen the chem. databases. Subsequently, the screened compds. were filtered by applying the rule of five, ADMET and mol. docking. Finally, four compds. were obtained as novel leads to inhibit the HSP90 activity.

[39] Sakkiah, S., S. Thangapandian, et al. (2012). “Molecular docking and dynamics simulation, receptor-based hypothesis: application to identify novel sirtuin 2 inhibitors.” Chem. Biol. Drug Des. 80: 315-327.

            Sirtuin, NAD+-dependent histone deacetylase enzyme, emerged as a potential therapeutic target, and modulations by small mols. could be effective drugs for various diseases. Owing to the absence of complex structure of sirtuin 2 (SIRT2), sirtinol was docked in the NAD+ binding site and subjected to 5-nseconds mol. dynamics (MD) simulation. LigandScout was used to develop hypotheses based on 3-representative SIRT2 complex structures from MD. Three structure-based hypotheses are generated and merged to form dynamics hypothesis. The dynamics hypothesis was validated using test and decoy sets. The results showed that dynamic hypothesis represents the complementary features of SIRT2 active site. Dynamic hypothesis was used to screen ChemDiv database, and hits were filtered through ADMET, rule of five, and two different mol. docking studies. Finally, 21 mols. were selected as potent leads based on consensus score from LigandFit, Gold fitness score, binding affinity from VINA as well as based on the important interactions with crit. residues in SIRT2 active site. Hence, we suggest that the dynamic hypothesis will be reliable in the identification of SIRT2 new lead as well as to reduce time and cost in the drug discovery process.

[40] Sanders, M. P. A., A. J. M. Barbosa, et al. (2012). “Comparative Analysis of Pharmacophore Screening Tools.” J. Chem. Inf. Model. 52: 1607-1620.

            The pharmacophore concept is of central importance in computer-aided drug design (CADD) mainly because of its successful application in medicinal chem. and, in particular, high-throughput virtual screening (HTVS). The simplicity of the pharmacophore definition enables the complexity of mol. interactions between ligand and receptor to be reduced to a handful set of features. With many pharmacophore screening softwares available, it is of the utmost interest to explore the behavior of these tools when applied to different biol. systems. In this work, we present a comparative anal. of eight pharmacophore screening algorithms (Catalyst, Unity, LigandScout, Phase, Pharao, MOE, Pharmer, and POT) for their use in typical HTVS campaigns against four different biol. targets by using default settings. The results herein presented show how the performance of each pharmacophore screening tool might be specifically related to factors such as the characteristics of the binding pocket, the use of specific pharmacophore features, and the use of these techniques in specific steps/contexts of the drug discovery pipeline. Algorithms with rmsd-based scoring functions are able to predict more compd. poses correctly as overlay-based scoring functions. However, the ratio of correctly predicted compd. poses vs. incorrectly predicted poses is better for overlay-based scoring functions that also ensure better performances in compd. library enrichments. While the ensemble of these observations can be used to choose the most appropriate class of algorithm for specific virtual screening projects, we remarked that pharmacophore algorithms are often equally good, and in this respect, we also analyzed how pharmacophore algorithms can be combined together in order to increase the success of hit compd. identification. This study provides a valuable benchmark set for further developments in the field of pharmacophore search algorithms, e.g., by using pose predictions and compd. library enrichment criteria.

[41] Schuster, D., C. Laggner, et al. (2006). “Development and validation of an in silico P450 profiler based on pharmacophore models.” Curr. Drug Discovery Technol. 3: 1-48.

            In today’s drug discovery process, the very early consideration of ADME properties is aimed at a redn. of drug candidate drop out rate in later development stages. A part from in vitro testing, in silico methods are evaluated as complementary screening tools for compds. with unfavorable ADME attributes. Esp. members of the cytochrome P 450 (P 450) enzyme superfamily. e.g. P 450 1A2, P 450 2C9, P 450 2C19, P 450 2D6, and P 450 3A4, contribute to xenobiotic metab., and compd. interaction with one of these enzymes is therefore critically evaluated. Pharmacophore models are widely used to identify common features amongst ligands for any target. In this study, both structure-based and ligand-based models for prominent drug-metabolizing members of the P 450 family were generated employing the software packages LigandScout and Catalyst. Essential chem. ligand features for substrate and inhibitor activity for all five P 450 enzymes investigated were detd. and analyzed. Finally, a collection of 11 pharmacophores for substrates and inhibitors was evaluated as an in silico P 450 profiling tool that could be used for early ADME estn. of new chem. entities.

[42] Schuster, D. and T. Langer (2006). Parallel pharmacophoric profiling as lead optimization tool for the prediction of interactions via the cytochrome P450 enzyme family, American Chemical Society.

            In today’s drug discovery process, the early consideration of ADME properties is aimed at a redn. of drug candidate drop out rate in later clin. development stages. Apart from in vitro testing, in silico methods are evaluated as complementary screening tools for compds. with unfavorable ADME attributes. Esp. members of the cytochrome P 450 (P 450) enzyme superfamily – e.g. P 450 1A2, P 450 2C9, P 450 2C19, P 450 2D6, and P 450 3A4 – contribute to xenobiotic metab., and compd. interaction with one of these enzymes is therefore critically evaluated. In this study, 3D pharmacophore modeling and screening techniques are applied to the prediction of ADME characteristics of small mols. This binding-mode specific approach is quite different from other physico-chem. property-based models and bears the advantage of being able to qual. evaluate the results by considering the 3D interaction between the ligand and the metabolic target. Although this approach is suitable for virtual screening by its high computational efficiency, it still delivers characteristic affinity information of a potential drug candidate towards a certain metabolic binding behavior. Both structure-based and ligand-based models for prominent drug-metabolizing members of the P 450 family were generated using the software packages LigandScout and Catalyst identifying essential chem. features for substrate and inhibitor activity for all five P 450 enzymes investigated. From all the generated pharmacophores, a collection of 11 pharmacophores for substrates and inhibitors was selected, and we suggest using this set of pharmacophores as in silico P 450 profiling tool for early ADME estn. in lead structure identification and optimization.

[43] Singhal, M. and A. Paul (2012). “Computational design, metabolism and toxicity studies of some novel chalconesemicarbazone derivatives.” Asian J. Chem. 24: 5117-5120.

            In the present study we have used pharmacophore hybridization technique of drug design and designed a pharmacophore model “chalconesemicarbazone”, which is having hydrogen acceptor site, hydrogen donor site, lipophilic site etc, which may help in binding with receptors and plays an important role in pharmacol. activities. On these observations, we have designed a synthetic scheme to synthesize this pharmacophore and also synthesize some lead compds. The pharmacophore of the synthesized compd. was developed by ligandscout-2.02 software by minimizing energy with MM3 force field. The possible metabolites and the toxicity of some selected synthesized chalconesemicarbazones were predicted by computational method using Pallas version-3.1 ADME-Tox prediction (metab. prediction by Mexalert/RetroMex and toxicity prediction by Hazardexpert/ToxAlert) software. Compd. 15 has high probability of toxicity. The major pathway of metab. was found to be p-hydroxylation and amide hydrolysis.

[44] Singhal, M., A. Paul, et al. (2010). “Pharmacophore designing, in-silico metabolism and toxicity studies of chalcone semicarbazones.” Pharmacologyonline: 330-338.

            In the present study we have used pharmacophore hybridization technique of drug design and designed a pharmacophore model “chalcone semicarbazone” which is having hydrogen acceptor site, hydrogen donor site, lipophilic site etc which may help in binding with receptors and plays an important role in pharmacol. activities. The pharmacophore of the synthesized compd. was developed by using ligandscout 2.02 software by minimizing energy with MM3 force field. The possible metabolites and the toxicity of some selected synthesized chalcone semicarbazones were predicted by computational method using Pallas version-3.1 ADME-Tox prediction (metab. prediction by Mexalert/RetroMex and toxicity prediction by Hazardexpert/ ToxAlert) software. Compds. 15, 26 and 28 have high probability of toxicity. The major pathway of metab. was p-hydroxylation and amide hydrolysis.

[45] Singhal, M., A. Paul, et al. (2011). “Antipyretic evaluation of synthesized semicarbazone derivatives.” Pharmacologyonline: 266-270.

            In the present study we have used pharmacophore hybridization technique of drug design and designed a pharmacophore model methylphenylsemicarbazone which is having hydrogen acceptor site, hydrogen donor site, lipophilic site etc using ligandscout-2.02 software. A series of methylphenylsemicarbazone was synthesized and evaluated for their antipyretic activity using Brewer’s yeast induced pyrexia in rats. Based on the results of an antipyretic study, compd. 25 was the most active compd.

[46] Soliman, S. (2012). Ligand- and structure-based modeling of human aryl sulfotransferase 1A1 activity, American Chemical Society.

            [p]Sulfonation catalyzed by sulfotransferase enzymes plays an important role in chem. defense mechanisms against various xenobiotics. A major human sulfotransferase, SULT1A1, metabolizes and/or bioactivates many endogenous compds. and is implicated in a range of cancers because of its ability to modify diverse promutagen and procarcinogen xenobiotics.[p]We examd. binding patterns of various substrates to SULT1A1 using Ligandscout (1) by a combination of ligand- and protein-based modeling approaches. Firstly, we developed and validated a structure-based pharmacophore model for SULT1A1 resulting in a model with high specificity excluding all inactive mols. Secondly, we constructed and validated a ligand-based pharmacophore model for 1A1 substrates using more than 70 substrates covering several activity classes and different chem. scaffolds. Our study provides insights into the mol. mechanisms of interaction of different substrates with human SULT1A1.[p](1) G. Wolber, T. LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use[p]as Virtual Screening Filters[p]. 2005, 45, 160-169.

[47] Steindl, T. M., D. Schuster, et al. (2007). Effective and rapid bioactivity profiling using pharmacophore-based parallel screening, American Chemical Society.

            3D Pharmacophore-based parallel screening is introduced as an in silico method to predict the potential biol. activities of potential drug mols. This study presents an application example employing a Pipeline Pilot-based screening platform and a collection of structure-based pharmacophore models built using the LigandScout software for automatic large-scale virtual activity profiling. An extensive set of HIV protease inhibitor pharmacophore models was used to screen different test sets consisting of active and inactive compds. In addn., we investigated, whether it is possible in a parallel screening system to differentiate between similar mols. / mols. acting on closely related proteins, and therefore we incorporated a collection of other protease inhibitors including aspartic protease inhibitors. The results of the screening expts. show a clear trend towards an enhanced signal to noise ratio (true positives/false positives and true negatives/false negatives).

[48] Steindl, T. M., D. Schuster, et al. (2006). “Parallel Screening: A Novel Concept in Pharmacophore Modeling and Virtual Screening.” J. Chem. Inf. Model. 46: 2146-2157.

            Parallel screening comprises a novel in silico method to predict the potential biol. activities of a compd. by screening it with a multitude of pharmacophore models. Our aim is to provide a fast, large-scale system that allows for virtual activity profiling. In this proof of principle study, carried out with the software tools LigandScout and Catalyst, the authors present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compds. The latter were screened against all pharmacophore models in order to det. if their biol. targets could be correctly predicted via an enrichment of corresponding pharmacophores matching these ligands. The results demonstrate that the desired enrichment, i.e., successful virtual activity profiling, was achieved for approx. 90% of all input mols. The authors discuss descriptors for output validation, as well as various aspects influencing the anal. of the obtained activity profiles, and the effect of the utilized search modus for screening.

[49] Steindl, T. M., D. Schuster, et al. (2006). “High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening.” J. Comput.-Aided Mol. Des. 20: 703-715.

            In order to assess bioactivity profiles for small org. mols. we propose to use parallel pharmacophore-based virtual screening. Our aim is to provide a fast, reliable and scalable system that allows for rapid in silico activity profile prediction of virtual mols. In this proof of principle study, carried out with the new structure-based pharmacophore modeling tool LigandScout and the high-performance database mining platform Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compds. The latter were screened against all pharmacophore models in order to det. if their known biol. targets could be correctly predicted via an enrichment of corresponding pharmacophores matching these ligands. The results demonstrate that the desired enrichment, i.e. a successful activity profiling, was achieved for approx. 90% of all input mols. Addnl., we discuss descriptors for output validation, as well as various aspects influencing the anal. of the obtained activity profiles, and the effect of the searching mode utilized for screening. The results of the study presented here clearly indicate that pharmacophore-based parallel screening comprises a reliable in silico method to predict the potential biol. activities of a compd. or a compd. library by screening it against a series of pharmacophore queries.

[50] Temml, V., S. Kuehnl, et al. (2013). “Interaction of Carthamus tinctorius lignan arctigenin with the binding site of tryptophan-degrading enzyme indoleamine 2,3-dioxygenase.” FEBS Open Bio 3: 450-452.

            Mediterranean Carthamus tinctorius (Safflower) is used for treatment of inflammatory conditions and neuropsychiatric disorders. Recently C. tinctorius lignans arctigenin and trachelogenin but not matairesinol were described to interfere with the activity of tryptophan-degrading enzyme indoleamine 2,3-dioxygenase (IDO) in peripheral blood mononuclear cells in vitro. We examd. a potential direct influence of compds. on IDO enzyme activity applying computational calcns. based on 3D geometry of the compds. The interaction pattern anal. and force field-based minimization was performed within LigandScout 3.03, the docking simulation with MOE 2011.10 using the X-ray crystal structure of IDO. Results confirm the possibility of an intense interaction of arctigenin and trachelogenin with the binding site of the enzyme, while matairesinol had no such effect.

[51] Trinh, Q. and L. Le (2014). “An investigation of antidiabetic activities of bioactive compounds in Euphorbiahirta Linn using molecular docking and pharmacophore.” Med. Chem. Res. 23: 2033-2045.

            Herbal remedies have been considered as potential medication for diabetes type 2 treatment. Bittermelons, onions, or GoryeongGinsengs are popular herbals and their functions in diabetes patients have been well documented. Recently, the Euphorbia hirta has been shown to have strong effects on diabetes in mice, however, there has been no research clearly indicating what the active compd. is. The main purpose of the current study was therefore to evaluate whether a relationship exists between various bioactive compds. in E. hirta Linn and targeted protein relating diabetes type 2 in human. In view of this, extn. from E. hirta Linn was tested if they contained the bioactive compds. This process involved the docking of 3D structures of those substances (ligand) into targeted proteins: 11-β hydroxysteroid dehydrogenase type 1, glutamine: fructose-6-phosphate amidotransferase, protein phosphatase, and mono-ADP-ribosyltransferase sirtuin-6. Then, LigandScout was applied to evaluate the bond formed between ligand and the binding pocket in the protein. These test identified in eight substances with high binding affinity (<-8.0 kcal/mol) to all four interested proteins of this article. The substances are quercetrin, rutin, myricitrin, cyanidin 3,5-O-diglucoside, pelargonium 3,5-diglucose in “flavonoid family” and α-amyrine, β-amyrine, taraxerol in “terpenes group.”. The result can be explained by the 2D picture which showed hydrophobic interaction, hydrogen bond acceptor, and hydrogen bond donor forming between carbonyl oxygen mols. of ligand with free residues in the protein. These pictures of the bonding provide evidence that E. hirta Linn may prove to be an effective treatment for diabetes type 2.

[52] Wolber, G., F. Bendix, et al. (2007). A novel, efficient virtual screening algorithm using 3-D chemical feature pattern recognition, American Chemical Society.

            Virtual screening using 3D pharmacophores has been established as an important and commonly used technique for virtual screening in the last years. However, the geometric alignment performing the comparison of flexible mols. to 3D pharmacophore models remains the most challenging and thus computationally most expensive part. A novel and efficient algorithm is presented that represents mols. and their conformations as pharmacophore points in 3D space and efficiently overlays them in polynomial time using a pattern recognition approach. Alignment and pharmacophore scoring are implemented in one single step, providing the view of a medicinal chemist on the mol. as a similarity measure built into the algorithm. Distance and d. characteristics of chem. features are used to identify optimal pairs and to correlate pharmacophoric geometries in three-dimensional space. The presented algorithm, which was implemented in Inte:Ligand’s LigandScout environment proves to be faster than previous combinatorial alignment approaches and creates more reasonable alignments than earlier methods. Despite the high quality of the alignment results, high-throughput processing for large mol. sets can be achieved by aggregating chem. feature point patterns and thus allows for a dramatic speed-up in the virtual screening of large, multi-conformational databases.

[53] Wolber, G. and A. A. Dornhofer (2006). Efficient overlay of molecular 3-D pharmacophores, American Chemical Society.

            Aligning and overlaying two or more bio-active mols. is one of the most important tasks in computational drug discovery and cheminformatics. Mol. characteristics from the view point of a macromol. target – represented as a 3D pharmacophore – are of special interest when regarding macromol.-ligand interaction. We present a novel approach for aligning rigid three-dimensional mols. according to their chem.-functional and steric pharmacophoric features. Optimal chem. feature pairs are identified using distance and d. characteristics and obtained by correlating pharmacophoric geometries. The presented approach proves to be faster than existing combinatorial alignments and creates more reasonable alignments than earlier methods. Correlations between two similar pharmacophore features can even be identified if they show different constraints. Examples will be provided to demonstrate the feasibility and speed of this method. Fig. 1. Three CDK2 inhibitors from the PDB (1ke5, 1ke6, 1ke7) in their bio-active conformation all aligned with their 3D pharmacophores describing the ligand-macromol. interaction. Graphics were created with LigandScout 1.0, available from http://www.inteligand.com.

[54] Wolber, G. and R. Kosara (2006). “Pharmacophores from macromolecular complexes with LigandScout.” Methods Princ. Med. Chem. 32: 131-150.

            A review. LigandScout is a program for structure-based pharmacophore modeling. It promises to become a useful tool to make interaction information available as a transparent three-dimensional model, which not only can be used for efficient virtual screening, but also provides means to understand intuitively the binding mode of a small-mol. ligand to a target. Overlaying for the generation of “common feature pharmacophores” is one interesting application. The need for transparency and the ability to modify automated results emphasize the need for high-quality visualization and the ability for interaction provided with LigandScout. The basic principles and applications of LigandScout are discussed.

[55] Wolber, G. and T. Langer (2005). “LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual Screening Filters.” J. Chem. Inf. Comput. Sci. 45: 160-169.

            From the historically grown archive of protein-ligand complexes in the Protein Data Bank small org. ligands are extd. and interpreted in terms of their chem. characteristics and features. Subsequently, pharmacophores representing ligand-receptor interaction are derived from each of these small mols. and its surrounding amino acids. Based on a defined set of only six types of chem. features and vol. constraints, three-dimensional pharmacophore models are constructed, which are sufficiently selective to identify the described binding mode and are thus a useful tool for in-silico screening of large compd. databases. The algorithms for ligand extn. and interpretation as well as the pharmacophore creation technique from the automatically interpreted data are presented and applied to a rhinovirus capsid complex as application example.

[56] Wolber, G. and T. Langer (2005). LigandScout: Interactive automated pharmacophore model generation from ligand-target complexes, American Chemical Society.

            Computer-aided mol. design together with virtual screening have emerged as one answer to steadily increasing economic pressure that forces the pharmaceutical industry to develop new drugs in a faster and more efficient way. We present a new approach for structure-based high throughput pharmacophore model generation. The LigandScout program provides an automated method for creating feature-based pharmacophore models from exptl. detd. structure data, e.g. publicly available from the Protein Databank (PDB). In a first step, small mol. ligands from the PDB are extd. and assignment of hybridization states and bond orders is performed. Second, from the interactions of the interpreted ligands with relevant surrounding amino acids, pharmacophore models reflecting functional interactions are created. These models can be used for screening mol. databases for similar modes of actions on the one hand, or for establishing bio-activity profiles for one single compd. on the other hand.

[57] Wolber, G., T. Seidel, et al. (2008). “Molecule-pharmacophore superpositioning and pattern matching in computational drug design.” Drug Discov Today 13: 23-29.

            A review. Three-dimensional (3D) pharmacophore modeling is a technique for describing the interaction of a small mol. ligand with a macromol. target. Since chem. features in a pharmacophore model are well known and highly transparent for medicinal chemists, these models are intuitively understandable and have been increasingly successful in computational drug discovery in the past few years. The performance and applicability of pharmacophore modeling depends on two main factors: the definition and placement of pharmacophoric features and the alignment techniques used to overlay 3D pharmacophore models and small mols. An overview of key technologies and latest developments in the area of 3D pharmacophores is given and provides insight into different approaches as implemented by the 3D pharmacophore modeling packages like Catalyst, MOE, Phase and LigandScout.

[58] Wolber, G., T. Seidel, et al. (2008). “Molecule-pharmacophore superpositioning and pattern matching in computational drug design.” Drug Discov Today 13: 23-29.

            Three-dimensional (3D) pharmacophore modeling is a technique for describing the interaction of a small molecule ligand with a macromolecular target. Since chemical features in a pharmacophore model are well known and highly transparent for medicinal chemists, these models are intuitively understandable and have been increasingly successful in computational drug discovery in the past few years. The performance and applicability of pharmacophore modeling depends on two main factors: the definition and placement of pharmacophoric features and the alignment techniques used to overlay 3D pharmacophore models and small molecules. An overview of key technologies and latest developments in the area of 3D pharmacophores is given and provides insight into different approaches as implemented by the 3D pharmacophore modeling packages like Catalyst, MOE, Phase and LigandScout.

[59] Yang, H., Y. Shen, et al. (2009). “Structure-based virtual screening for identification of novel 11β-HSD1 inhibitors.” Eur. J. Med. Chem. 44: 1167-1171.

            Structure-based pharmacophore models were built by using LigandScout and used for virtual screening of the SPECS database to identify new potential 11β-HSD1 inhibitors. As a refinement of the results obtained from virtual 3D pharmacophore screening, the best fitting virtual hits were subjected to docking study. The resulting compds. were tested in an enzyme assay and revealed several compds. with novel scaffolds that show sub-micromolar activity and high selectivity for 11β-HSD1 against 11β-HSD2.

[60] Zhang, Y., Y. Wang, et al. (2013). “Combinatorial screening of COX-2 inhibitors from Chinese herbs based on multiple structure-based pharmacophores.” Adv. Mater. Res. (Durnten-Zurich, Switz.) 791-793: 269-273, 266 pp.

            Ten structure-based pharmacophore models of Cyclooxygenase 2 (COX-2) inhibitors were generated by LigandScout based on COX-2 inhibitor complexes from the Protein Data Bank (PDB). The potential COX-2 inhibitors were identified from traditional Chinese medicine with the method of combinatorial screening with ten models. Based on the screening results of MDDR and the metrics of E, A% and comprehensive appraisal index (CAI), the threshold of hit frequency of mols. was defined and used to identify the active mols. from Chinese herbs. The mols. hit by not less than six pharmacophore models were taken as the screening objects of COX-2 inhibitor and 1103 mols. were obtained.

[61] Zhang, Y., Y. Wang, et al. (2013). “Structure-based pharmacophore models generation and combinatorial screening of ICE inhibitors.” Appl. Mech. Mater. 347-350: 1216-1220, 1216 pp.

            Eight structure-based pharmacophore models of Interleukin-1β converting enzyme (ICE) inhibitors were generated by LigandScout based on ICE inhibitor complexes from the Protein Data Bank (PDB). A combinatorial screening method based on multiple pharmacophore models were proposed in present study, since the binding pockets of different complexes were different, the structure-based pharmacophore models have a high specificity and cannot cover all the active mols. Based on the screening results of MDDR and the metrics of E and A %, a new metric CAI (comprehensive appraisal index) was defined and used to det. the threshold of hit frequency of mols. which screened by the combinatorial screening method. According to the threshold, the potential ICE inhibitors were then identified from traditional Chinese medicine with the method of combinatorial screening with eight models. The mols. hit by not less than five pharmacophore models were taken as the screening objects of ICE inhibitor, and 781 mols. were obtained.

[62] Zhang, Y., Y. Wang, et al. (2014). “Sub-pharmacophore generation of JNK3 inhibitors.” Appl. Mech. Mater. 444-445: 1756-1760, 1756 pp.

            The structure-based pharmacophore (SBP) model is consisted by the complementarity of the chem. features and space of the interaction between the ligand and receptor. The SBP models always have a high specificity which can only represent the specific class of the ligand. To simplify the models, sub-pharmacophore was then proposed in present study, and was expected to have and only have the most important or the common chem. features which play the major role in the interaction of ligand and receptor. Sub-pharmacophore should contain 4-6 features, the higher specificity with more features, and vice versa. The sub-pharmacophore was generated by the random combination of features from the structure-based models. With the MDL Drug Data Report database used as the testing database, a new metric CAI (comprehensive appraisal index), which integrated the metrics of E and A%, was defined and used to det. the best sub-pharmacophore model. C-Jun N-terminal kinase (JNKs) is one of the mitogen-activated protein kinase family, and widely involved in immune response and inflammatory response, and other pathol. processes. JNK3 is mainly distributed in the brain and nervous system. In present study, twenty-five initial SBP models of JNK3 inhibitors were directly constructed from the Protein Data Bank (PDB) complexes by the LigandScout software. Then, 1018 sub-pharmacophore models were obtained from the 25 initial models. Finally, the best sub-pharmacophore was detd. which was simplified from the initial model generated from the 3FI2 complex, and included four features: one hydrogen bond donor, one hydrogen bond acceptor, and two hydrophobic groups. The metrics of E, A% and CAI value of the best sub-pharmacophore model are 17.47, 31.15 and 5.44, resp. The potential JNK3 inhibitors were then identified from Chinese herbs with the best sub-pharmacophore model, and 286 compds. were obtained.