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Life Sciences Modeling & Simulation

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Protein Modeling and Sequence Analysis

  1. Hirashima, A.; Huang, H. Homology modeling, agonist binding site identification, and docking in octopamine receptor of Periplaneta americana. Comput. Biol. Chem. 2008, 32, 185-190.
  2. Spassov, V. Z.; Yan, L. A fast and accurate computational approach to protein ionization. Protein Sci. 2008, 17, 1955-1970.
  3. Koska, J.; Spassov, V. Z.; Maynard, A. J.; Yan, L.; Austin, N.; Flook, P. K.; Venkatachalam, C. M. Fully automated molecular mechanics based induced fit protein-ligand docking method. J. Chem. Inf. Model. 2008, 48, 1965-1973.
  4. Eswar, N.; Eramian, D.; Webb, B.; Shen, M. Y.; Sali, A. Protein structure modeling with MODELLER. Methods Mol. Biol. 2008, 426, 145-159.
  5. Spassov, V.; Yan, L.; Flook, P. The dominant role of side-chain backbone interactions in structural realization of amino acid code. ChiRotor: a side-chain prediction algorithm based on side-chain backbone interactions. Protein Sci. 2007, 16, 494-506.
  6. Marti-Renom, M. A.; Madhusudhan, M. S.; Sali, A. Alignment of protein sequences by their profiles. Protein Sci. 2004, 13, 1071–1087.
  7. Li, L.; Chen, R.; Weng, Z. RDOCK: refinement of rigid-body protein docking predictions. Proteins 2003, 53, 693–707.
  8. Chen, R.; Li, L.; Weng, Z. ZDOCK: an initial-stage protein-docking algorithm. Proteins 2003, 52, 80–87.
  9. Morea, V.; Lesk, A.; Tramontano, A. Antibody modeling: implications for engineering and design. Methods 2000, 20, 267-279.
  10. Sali, A.; Pottertone, L.; Yuan, F.; van Vlijmen, H.; Karplus, M.; Evaluation of comparative protein modeling by MODELLER. Proteins 1995, 23, 318-326.

Biopolymer Building and Analysis

  1. Honig, B.; Sharp, K.; Yang, A. S. Macroscopic models of aqueous solutions: biological and chemical applications. J. Phys. Chem. 1993, 97, 1101-1109.
  2. Nicholls, A.; Honig, B. A rapid finite difference algorithm, utilizing successive over-relaxation to solve the Poisson-Boltzmann equation. J. Comp. Chem. 1991, 12, 435-445.
  3. Sharp, K. A.; Nichols, A.; Friedman, R.; Honig, B. Extracting hydrophobic free energies from experimental data: relationship to protein folding and theoretical models. Biochemistry 1991, 30, 9686-9697.

Simulations

  1. Brooks, B. R.; Brooks, C. L. 3rd; Mackerell, A. D. Jr.; Nilsson, L.; Petrella, R. J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A. R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M.; Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R. W.; Post, C. B.; Pu, J. Z.; Schaefer, M.; Tidor, B.; Venable, R. M.; Woodcock, H. L.; Wu, X.; Yang, W.; York, D. M.; Karplus, M. CHARMM: the biomolecular simulation program. J. Comput. Chem. 2009, 30, 1545-1614.
  2. Luthra, P. M.; Prakash, A.; Barodia, S. K.; Kumari, R.; Mishra, C. B.; Kumar, J. B. S. In silico study of naphtha [1,2-d] thiazol-2-amine with adenosine A2A receptor and its role in Antagonism of Haloperidol-induced motor impairments in mice. Neurosci. Lett. 2009, 463, 215-218.
  3. Spassov, V. Z; Flook, P. K.; Yan, L. LOOPER: a molecular mechanics based algorithm for protein loop prediction. Prot. Eng. Des. Sel. 2008, 21, 91-100.
  4. Nimlos, M. R.; Matthews, J. F.; Crowley, M. F.; Walker, R. C.; Chukkapalli, G.; Brady, J. W.; Adney, W. S.; Cleary, J. M.; Zhong, L.; Himmel, M. E. Molecular modeling suggests induced fit of Family I carbohydrate-binding modules with a broken-chain cellulose surface. Protein Eng. Des. Sel. 2007, 20, 179-87.
  5. Foloppe, N.; Fisher, L. M.; Howes, R.; Kierstan, P.; Potter, A.; Robertson, A. G.; Surgenor, A. E. Structure-based design of novel Chk1 inhibitors: insights into hydrogen bonding and protein-ligand affinity. J. Med. Chem. 2005, 48, 4332-45.
  6. Momany, F. A.; Rone, R. Validation of the general purpose QUANTA 3.2/CHARMm force field. J. Comput. Chem. 1992, 13, 888-900.
  7. Brooks, B. R.; Bruccoleri, R. E.; Olafson, B. D.; States, D. J.; Swaminathan, S.; Karplus, M. CHARMM: a program for macromolecular energy, minimization, and dynamics calculations. J. Comput. Chem. 1983, 4, 187-217.

Structure-Based Design

  1. Pearce, B. C.; Langley, D. R.; Kang, J.; Huang, H.; Kulkarni, A. E-novo: an automated workflow for efficient structure-based lead optimization. J. Chem. Inf. Model. 2009, 49, 1797-1809.
  2. Yu, H.; Wang, Z.; Zhang, L.; Zhang, J.; Huang, Q. The discovery of novel vascular endothelial growth factor receptor tyrosine kinases inhibitors: pharmacophore modeling, virtual screening and docking studies. Chem. Biol. Drug Des. 2007, 69, 204-211.
  3. Rao, S. N.; Head, M. S.; Kulkarni, A.; LaLonde, J. M. Validation studies of the site-directed docking program LibDock. J. Chem. Inf. Model. 2007, 47, 2159-2171.
  4. Sato, H.; Shewchuk, L. M.; Tang, J. Prediction of multiple binding modes of the CDK2 inhibitors, anilinopyrazoles, using the automated docking programs GOLD, Fl exX, and LigandFit: an evaluation of performance. J. Chem. Inf. Model. 2006, 46, 2552-2662.
  5. Warren G. L.; Andrews, C. W.; Capelli, A. M.; Clarke, B.; LaLonde, J.; Lambert, M.H.; Lindvall, M.; Nevins, N.; Semus, S. F.; Senger, S.; Tedesco, G.; Wall, I. D.; Woolven, J. M.; Peishoff, C. E.; Head, M. S. A critical assessment of docking programs and scoring functions. J. Med. Chem. 2006, 49, 5912-5931.
  6. Wang, Z. Q.; Weber, N.; Lou, Y. J.; Proksch, P. Prenylflavonoids as nonsteroidal phytoestrogens and related structure-activity relationships. ChemMedChem. 2006, 1, 482-488.
  7. Potts, S. J.; Edwards, D. J.; Hoffman, R. Challenges of target/compound data integration from disease to chemistry: a case study of dihydrofolate reductase inhibitors. Curr. Drug. Discov. Technol. 2005, 2, 75-87.
  8. O’Brien, S. E.; Brown, D. G.; Mills, J. E.; Phillips, C.; Morris, G. Computational tools for the analysis and visualization of multiple protein-ligand complexes. J. Mol. Graph. Model. 2005, 24, 186-194.
  9. Erickson, J. A.; Jalaie, M.; Robertson, D. H.; Lewis, R. A.; Vieth, M. Lessons in molecular recognition: the effects of ligand and protein flexibility on molecular docking accuracy. J. Med. Chem. 2004, 47, 45-55.
  10. Blaise, M. C.; Sowdhamini, R.; Rao, M. R.; Pradhan, N. Evolutionary trace analysis of ionotropic glutamate receptor sequences and modeling the interactions of agonists with different NMDA receptor subunits. J. Mol. Model. 2004, 10, 305-316.
  11. Varady, J.; Wu, X.; Fang, X.; Min, J.; Hu, Z.; Levant, B.; Wang, S. Molecular modeling of the three-dimensional structure of dopamine 3 (D3) subtype receptor: discovery of novel and potent D3 ligands through a hybrid pharmacophore- and structure-based database searching approach. J. Med. Chem. 2003, 46, 4377-4392.

Ligand-Based Design

MCSS

  1. Evensen, E.; Joseph-McCarthy, D.; Weiss, G. A.; Schreiber, S. L.; Karplus, M. Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4. J. Comput. Aided Mol. Des. 2007, 21, 395-418.
  2. Zheng, C. H.; Zhou, Y. J.; Zhu, J.; Ji, H. T.; Chen, J.; Li, Y. W.; Sheng, C. Q.; Lu, J. G.; Jiang, J. H.; Tang, H.; Song, Y. L. Construction of a three-dimensional pharmacophore for Bcl-2 inhibitors by flexible docking and the multiple copy simultaneous search method. Bioorg. Med. Chem. 2007, 15, 6407-6417.
  3. Schechner, M.; Sirockin, F.; Stote, R. H.; Dejaegere, A. P. Functionality maps of the ATP binding site of DNA gyrase B: generation of a consensus model of ligand binding. J. Med. Chem. 2004, 47, 4373-4390.
  4. Takano, Y.; Koizumi, M.; Takarada, R.; Kamimura, M. T.; Czerminski, R.; Koike, T. Computer-aided design of a factor Xa inhibitor by using MCSS functionality maps and a CAVEAT linker search. J. Mol. Graph. Model. 2003, 22, 105-114.
  5. Joseph-McCarthy, D.; Alvarez, J. C. Automated generation of MCSS-derived pharmacophoric DOCK site points for searching multiconformation databases. Proteins. 2003, 51, 189-202.
  6. Joseph-McCarthy, D.; Tsang, S. K.; Filman, D. J.; Hogle, J. M.; Karplus, M. Use of MCSS to design small targeted libraries: application to picornavirus ligands. J. Am. Chem. Soc. 2001, 123, 12758-12769.
  7. Adlington, R. M.; Baldwin, J. E.; Becker, G. W.; Chen, B.; Cheng, L.; Cooper, S. L.; Hermann, R. B.; Howe, T. J.; McCoull, W.; McNulty, A. M.; Neubauer, B. L.; Pritchard, G. J. Design, synthesis, and proposed active site binding analysis of monocyclic 2-azetidinone inhibitors of prostate specific antigen. J. Med. Chem. 2001, 44, 1491-1508.
  8. English, A. C.; Groom, C. R.; Hubbard, R. E. Experimental and computational mapping of the binding surface of a crystalline protein. Protein Eng. 2001, 14, 47-59.
  9. Zeng, J.; Nheu, T.; Zorzet, A.; Catimel, B.; Nice, E.; Maruta, H.; Burgess, A. W.; Treutlein, H. R. Design of inhibitors of Ras--Raf interaction using a computational combinatorial algorithm. Protein Eng. 2001, 14, 39-45.
  10. Caflisch, A.; Schramm, H. J.; Karplus, M. Design of dimerization inhibitors of HIV-1 aspartic proteinase: a computer-based combinatorial approach. J. Comput. Aided Mol. Des.2000, 14, 161-179.

Receptor-Ligand Interactions

Ligandfit

  1. Montes, M.; Miteva, M. A.; Villoutreix, B. O. Structure-based virtual ligand screening with LigandFit: pose prediction and enrichment of compound collections. Proteins 2007, 68, 712-725.
  2. Taha, M. O.; AlDamen, M. A. Effects of variable docking conditions and scoring functions on corresponding protein-aligned comparative molecular field analysis models constructed from diverse human protein tyrosine phosphatase 1B inhibitors. J. Med. Chem. 2005, 48, 8016-8034.
  3. Taha, M. O.; Qandil, A. M.; Zaki, D. D.; AlDamen, M. A. Ligand-based assessment of factor Xa binding site flexibility via elaborate pharmacophore exploration and genetic algorithm-based QSAR modeling. Eur. J. Med. Chem. 2005, 40, 701-727.
  4. Triballeau, N.; Acher, F.; Brabet, I. ; Pin, J. P.; Bertrand, H. O. Virtual screening workflow development guided by the "receiver operating characteristic" curve approach. Application to high-throughput docking on metabotropic glutamate receptor subtype 4. J. Med. Chem. 2005, 48, 2534-2547.
  5. Kontoyianni, M.; McClellan, L. M.; Sokol, G. S. Evaluation of docking performance: comparative data on docking algorithms. J. Med. Chem. 2004, 47, 558-565. (LigandFit/LigandScore)
  6. Kroemer, R. T.; Vulpetti, A.; McDonald, J. J.; Rohrer, D. C.; Trosset, J. Y.; Giordanetto, F.; Cotesta, S.; McMartin, C.; Kihlén, M.; Stouten, P. F. Assessment of docking poses: interactions-based accuracy classification (IBAC) versus crystal structure deviations. J. Chem. Inf. Comput. Sci. 2004, 44, 871-881. (LigandFit/LigandScore)
  7. Asano, T.; Yoshikawa, T.; Usui, T.; Yamamoto, H.; Yamamoto, Y.; Uehara, Y.; Nakamura, H. Benzamides and benzamidines as specific inhibitors of epidermal growth factor receptor and v-Src protein tyrosine kinases. Bioorg. Med. Chem. 2004, 12, 3529-3542. (LigandFit/LigandScore)
  8. Gouldson, P. R.; Kidley, N. J.; Bywater, R. P.; Psaroudakis, G.; Brooks, H. D.; Diaz, C.; Shire, D; Reynolds, C. A. Toward the active conformations of rhodopsin and the ß2-adrenergic receptor. Proteins 2004, 56, 67-84. (LigandFit/LigandScore)
  9. Krovat, E. M.; Langer, T. Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors. J. Chem. Inf. Comput. Sci. 2004, 44, 1123-1129. (LigandFit/LigandScore)
  10. Varady, J.; Wu, X.; Fang, X.; Min, J.; Hu, Z.; Levant, B.; Wang, S. Molecular modeling of the three-dimensional structure of dopamine 3 (D3) subtype receptor: discovery of novel and potent D3 ligands through a hybrid pharmacophore- and structure-based database searching approach. J. Med. Chem. 2003, 46, 4377-4392. (LigandFit/LigandScore)
  11. Wang, R.; Lu, Y.; Wang, S. Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. 2003, 46, 2287-2303. (LigandFit/LigandScore)
  12. Venkatachalam, C. M.; Jiang, X.; Oldfield, T.; Waldman, M. LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. J. Mol. Graph. Model. 2003, 21, 289-307. (LigandFit/LigandScore)
  13. Bertrand, H. O.; Bessis, A. S.; Pin, J. P.; Acher, F. C. Common and selective molecular determinants involved in metabotopic glutamate receptor agonist activity. J. Med. Chem. 2002, 45, 3171-3183.
  14. Xu, R. X.; Hassell, A. M.; Vanderwall, D.; Lambert, M. H.; Holmes, W. D.; Luther, M. A.; Rocque, W. J.; Milburn, M. V.; Zhao, Y.; Ke, H.; Nolte, R. T. Atomic structure of PDE4: insights into phosphodiesterase mechanism and specificity. Science 2000, 288, 1822-1825. (LigandFit/LigandScore)

LigandScore

  1. Cotesta, S., Giordanetto, F., Trosset, J.-Y., Crivori, P., Kroemer, R. T., Stouten, P. F.W. and Vulpetti, A., “Virtual Screening to Enrich a Compound Collection with CDK2 Inhibitors Using Docking, Scoring, and Composite Scoring Models," Proteins: Struct., Funct., Bioinf. , 2005 , 60 , 629-643.

Ludi

  1. Khedkar, S. A.; Malde, A. K.; Coutinho, E. C. Design of inhibitors of the MurF enzyme of Streptococcus pneumoniae using docking, 3D-QSAR, and de Novo design. J. Chem. Inf. Model. 2007, 47, 1839-1846.
  2. Herschhorn, A.; Lerman, L.; Weitman, M.; Gleenberg, I. O.; Nudelman, A.; Hizi, A. De novo parallel design, synthesis and evaluation of inhibitors against the reverse transcriptase of human immunodeficiency virus type-1 and drug-resistant variants. J. Med. Chem. 2007, 50, 2370-2384.
  3. Grembecka, J.; Mucha, A.; Cierpicki, T.; Kafarski, P. The most potent organophosphorus inhibitors of leucine aminopeptidase. Structure-based design, chemistry, and activity. J. Med. Chem. 2003, 46, 2641-2655.
  4. Ji, H.; Zhang, W.; Zhang, M.; Kudo, M.; Aoyama, Y.; Yoshida, Y.; Sheng, C.; Song, Y.; Yang, S.; Zhou, Y.; Lu, J.; Zhu, J. Structure-based de novo design, synthesis, and biological evaluation of non-azole inhibitors specific for lanosterol 14r-demethylase of fungi. J. Med. Chem. 2003, 46, 474-485.
  5. Jorgensen, F. S.; Christensen, I. T.; Johansen, B. N.; Terp, G. E. A new concept for multidimensional selection of ligand conformations (MultiSelect) and multidimensional scoring (MultiScore) of protein-ligand binding affinities. J. Med. Chem. 2001, 44, 2333-2343.
  6. Kubinyi, H. Chance favors the prepared mind-from serendipity to rational drug design. J. Recept. Signal Transduct. Res. 1999, 19, 15-39.
  7. Muegge, I.; Martin, Y. C. A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J. Med. Chem. 1999, 42, 791-804.
  8. Böhm, H. J. Prediction of binding constants of protein ligands: a fast method for the prioritization of hits obtained from de novo design or 3D database search programs. J. Comput. Aided Mol. Des. 1998, 4, 309-323.
  9. Böhm, H. J. Towards the automatic design of synthetically accessible protein ligands: peptides, amides and peptidomimetics. J. Comput. Aided Mol. Des. 1996, 4, 265-272.
  10. Böhm, H. J. On the use of Ludi to search the fine chemicals directory for ligands of proteins of known three-dimensional structure. J. Comput. Aided Mol. Des. 1994, 5, 623-632.
  11. Böhm, H. J. The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure. J. Comput. Aided Mol. Des. 1994, 3, 243-256.
  12. Böhm, H. J. A novel computational tool for automated structure-based drug design. J. Mol. Recognit. 1993, 3, 131-137.
  13. Böhm, H. J. Ludi: rule-based automatic design of new substituents for enzyme inhibitor leads. J. Comput. Aided Mol. Des. 1992, 6, 593-606.
  14. Böhm, H. J. The computer program Ludi: a new method for the de novo design of enzyme inhibitors. J. Comput. Aided Mol. Des. 1992, 1, 61-78.

Pharmacophore Modeling and Analysis

  1. Tsai, K. C.; Teng, L. W.; Shao, Y. M.; Chen, Y. C.; Lee, Y. C.; Li, M.; Hsiao, N. W. The first pharmacophore model for potent NF-kappaB inhibitors. Bioorg Med Chem Lett. 2009, 19, 5665-5669.
  2. Mony, L.; Krzaczkowski, L.; Leonetti, M.; Le Goff, A.; Alarcon, K.; Neyton, J.; Bertrand, H. O.; Acher, F.; Paoletti, P. Structural basis of NR2B-selective antagonist recognition by N-methyl-D-aspartate receptors. Mol. Pharmacol. 2009, 75, 60-74.
  3. Lei, M.; Zhao, X.; Wang, Z.; Zhu, Y. Pharmacophore modeling, docking studies, and synthesis of novel dipeptide proteasome inhibitors containing boron atoms. J. Chem. Inf. Model. 2009, DOI: 10.1021/ci900225s.
  4. Zampieri, D.; Mamolo, M. G.; Laurini, E.; Florio, C.; Zanette, C.; Fermeglia, M.; Posocco, P.; Paneli, M. S.; Pricl, S.; Vio, L. Synthesis, biological evaluation, and three-dimensional in silico pharmacophore model for σ1 receptor ligands based on a series of substituted benzo[d]oxazol-2(3H)-one derivatives. J. Med. Chem. 2009, 52, 5380-5392.
  5. Ren, J. X.; Li, L. L.; Zou, J.; Yang, L.; Yang, J. L.; Yang, S. Y. Pharmacophore modeling and virtual screening for the discovery of new transforming growth factor-β type I receptor (ALK5) inhibitors. Eur. J. Med. Chem. 2009, doi:10.1016/j.ejmech.2009.07.008.
  6. Li, H. F.; Lu, T.; Zhu, T.; Jiang, Y. J.; Rao, S. S.; Hu, L. Y.; Xin, B. T.; Chen, Y. D. Virtual screening for Raf-1 kinase inhibitors based on pharmacophore model of substituted ureas. Eur. J. Med. Chem. 2009, 44, 1240-1249.
  7. Lee, P. J.; Bhonsle, J. B.; Gaona, H. W.; Huddler, D. P.; Heady, T. N.; Kreishman-Deitrick, M.; Bhattacharjee, A.; McCalmont, W. F.; Gerena, L.; Lopez-Sanchez, M.; Roncal, N. E.; Hudson, T. H.; Johnson, J. D.; Prigge, S. T.; Waters, N. C. Targeting the fatty acid biosynthesis enzyme, β-ketoacyl-acyl carrier protein synthase III(PfKASIII), in the identification of novel antimalarial agents. J. Med. Chem. 2009, 52, 952-963.
  8. Zhang, H.; Xiang, M. L.; Zhao, Y. L.; Wei, Y. Q.; Yang, S. Y. Support vector machine and pharmacophore-based prediction models of multidrug-resistance protein 2 (MRP2) inhibitors. Eur. J. Pharm. Sci. 2009, 36, 451-457.
  9. Shakya, N.; Roy, K. K.; Saxena, A. K. Substituted 1,2,3,4-tetrahydroquinolin-6-yloxypropanes as β3-adrenergic receptor agonists: design, synthesis, biological evaluation and pharmacophore modeling. Bioorg. Med. Chem. 2009, 17, 830-847.
  10. Yang, H.; Shen, Y.; Chen, J.; Jiang, Q.; Leng, Y.; Shen, J. Structure-based virtual screening for identification of novel 11β-HSD1 inhibitors. Eur. J. Med. Chem. 2009, 44, 1167-1171.
  11. Nayana, R. S.; Bommisetty, S. K.; Singh, K.; Bairy, S. K.; Nunna, S.; Pramod, A.; Muttineni. R. Structural analysis of carboline derivatives as inhibitors of MAPKAP K2 using 3D QSAR and docking studies. J. Chem. Inf. Model. 2009, 49, 53-67.
  12. Klabunde, T.; Giegerich, C.; Evers, A. Sequence-derived three-dimensional pharmacophore models for G-protein-coupled receptors and their application in virtual screening. J. Med. Chem. 2009, 52, 2923-2932.
  13. Classen-Houben, D.; Schuster, D.; Da Cunha, T.; Odermatt, A.; Wolber, G.; Jordis, U.; Kueenburg, B. Selective inhibition of 11β-hydroxysteroid dehydrogenase 1 by 18α-glycyrrhetinic acid but not 18β-glycyrrhetinic acid. J. Steroid Biochem. Mol. Biol. 2009, 113, 248-252.
  14. Abu Hammad, A. M.; Taha, M. O. Pharmacophore modeling, quantitative structure-activity relationship analysis, and shape-complemented in silico screening allow access to novel influenza neuraminidase inhibitors. J. Chem. Inf. Model. 2009, 49, 978-996.
  15. Chen, J. J.; Liu, T. L.; Yang, L. J.; Li, L. L.; Wei, Y. Q.; Yang, S. Y. Pharmacophore modeling and virtual screening studies of checkpoint kinase 1 inhibitors. Chem. Pharm. Bull. (Tokyo) 2009, 57, 704-709.
  16. Zawahir, Z.; Dayam, R.; Deng, J.; Pereira, C.; Neamati, N. Pharmacophore guided discovery of small-molecule human apurinic/apyrimidinic endonuclease 1 inhibitors. J. Med. Chem. 2009, 51, 20-32.
  17. Xie, Q. Q.; Xie, H. Z.; Ren, J. X.; Li, L. L.; Yang, S. Y. Pharmacophore modeling studies of type I and type II kinase inhibitors of Tie2. J. Mol. Graph. Model. 2009, 27, 751-758.
  18. Diao, L.; Ekins, S.; Polli, J. E. Novel inhibitors of human organic cation/carnitine transporter (hOCTN2) via computational modeling and in vitro testing. Pharm. Res. 2009, 26, 1890-1900.
  19. Lee, J. Y.; Jeong, K. W.; Lee, J. U.; Kang, D. I.; Kim, Y. Novel E. coli β-ketoacyl-acyl carrier protein synthase III inhibitors as targeted antibiotics. Bioorg. Med. Chem. 2009, 17, 1506-1513.
  20. Huang, W.; Yu, H.; Sheng, R.; Li, J.; Hu, Y. Identification of pharmacophore model, synthesis and biological evaluation of N-phenyl-1-arylamide and N-phenylbenzenesulfonamide derivatives as BACE 1 inhibitors. Bioorg. Med. Chem. 2009, 16, 10190-10197.
  21. Ahmed, A.; Choo, H.; Cho, Y. S.; Park, W. K.; Pae, A. N. Identification of novel serotonin 2C receptor ligands by sequential virtual screening. Bioorg. Med. Chem. 2009, 17, 4559-4568.
  22. Vijayan, R. S.; Prabu, M.; Mascarenhas, N. M.; Ghoshal, N. Hybrid structure-based virtual screening protocol for the identification of novel BACE1 inhibitors. J. Chem. Inf. Model. 2009, 49, 647-657.
  23. Abu-Hammad, A.; Zalloum, W. A.; Zalloum, H.; Abu-Sheikha, G.; Taha, M. O. Homology modeling of MCH1 receptor and validation by docking/scoring and protein-aligned CoMFA. Eur. J. Med. Chem. 2009, 44, 2583-2596.
  24. Neves, M. A.; Dinis, T. C.; Colombo, G.; Sá e Melo, M. L. Fast three dimensional pharmacophore virtual screening of new potent non-steroid aromatase inhibitors. J. Med. Chem. 2009, 52, 143-150.
  25. Markt, P.; Feldmann, C.; Rollinger, J. M.; Raduner, S.; Schuster, D.; Kirchmair, J.; Distinto, S.; Spitzer, G. M.; Wolber, G.; Laggner, C.; Altmann, K. H.; Langer, T.; Gertsch, J. Discovery of novel CB2 receptor ligands by a pharmacophore-based virtual screening workflow. J. Med. Chem. 2009, 52, 369-378.
  26. Ismail, M. A.; Nabil Aboul-Enein, M.; Abouzid, K. A.; Abou El Ella, D. A.; Ismail, N. S. ACE inhibitors hypothesis generation for selective design, synthesis and biological evaluation of 3-mercapto-2-methyl-propanoyl-pyrrolidine-3-imine derivatives as antihypertensive agents. Bioorg. Med. Chem. 2009, 17, 3739-3746.
  27. Wang, H. Y.; Li, L. L.; Cao, Z. X.; Luo, S. D.; Wei, Y. Q.; Yang,  S. Y. A specific pharmacophore model of aurora B kinase inhibitors and virtual screening studies based on it. Chem. Biol. Drug Des. 2009, 73, 115-126.
  28. Bhattacharjee, A. K.; Gordon, J. A.; Marek, E.; Campbell, A.; Gordon, R. K. 3D-QSAR studies of 2,2-diphenylpropionates to aid discovery of novel potent muscarinic antagonists. Bioorg. Med. Chem. 2009, 17, 3999-4012.
  29. Dong, A.; Wei, J.; Gao, Q. 3D-pharmacophore model for RXRγ agonists. Neurochem. Int. 2009, 54, 286-291.
  30. Vaidyanathan, J.; Vaidyanathan, T. K.; Ravichandran, S. Computer simulated screening of dentin bonding primer monomers through analysis of their chemical functions and their spatial 3D alignment. J. Biomed. Mater. Res. B Appl. Biomater. 2009, 88, 447-457.
  31. Chiang, Y. K.; Kuo, C. C.; Wu, Y. S.; Chen, C. T.; Coumar, M. S.; Wu, J. S.; Hsieh, H. P.;  Chang, C. Y.; Jseng, H. Y.; Wu, M. H.; Leou, J. S.; Song, J. S.; Chang, J. Y.; Lyu, P. C.; Chao, Y. S.; Wu, S. Y. Generation of ligand-based pharmacophore model and virtual screening for identification of novel tubulin inhibitors with potent anticancer activity. J. Med. Chem. 2009, 52, 4221-4233.
  32. Foloppe, N.; Benwell, K.; Brooks, T. D.; Kennett, G.; Knight, A. R.; Misra, A.; Monck, N. J. Discovery and functional evaluation of diverse novel human CB(1) receptor ligands. Bioorg. Med. Chem. Lett. 2009, 15, 4183-4190.
  33. Chaudhaery, S. S.; Roy, K. K.; Saxena, A. K. Consensus superiority of the pharmacophore-based alignment, over maximum common substructure (MCS): 3D-QSAR studies on carbamates as acetylcholinesterase inhibitors. J. Chem. Inf. Model. 2009, 49, 1590–1601.
  34. Barakat, K. H.; Torin Huzil, J.; Luchko, T.; Jordheim, L.; Dumontet, C.; Tuszynski, J. Characterization of an inhibitory dynamic pharmacophore for the ERCC1–XPA interaction using a combined molecular dynamics and virtual screening approach,” J. Mol. Graph. Model. 2009, doi:10.1016/j.jmgm.2009.04.009
  35. Neves, M. A.; Dinis, T. C.; Colombo, G.; Sá e Melo, M. L. An efficient steroid pharmacophore-based strategy to identify new aromatase inhibitors,” Eur. J. Med. Chem., 2009, 44, 4121-4127.
  36. Ravikumar, M.; Pavan, S.; Bairy, S.; Pramod, A. B.; Sumakanth, M.; Kishore, M.; Sumithra, T. Virtual screening of cathepsin k inhibitors using docking and pharmacophore models. Chem. Biol. Drug Des. 2008, 72, 79-90.
  37. Taha, M. O.; Atallah, N.; Al-Bakri, A. G.; Paradis-Bleau, C.; Zalloum, H.; Younis, K. S.; Levesque, R. C. Discovery of new MurF inhibitors via pharmacophore modeling and QSAR analysis followed by in-silico screening. Bioorg. Med. Chem. 2008, 16, 1218-1235.
  38. Hermone, A. R.; Burnett, J. C.; Nuss, J. E.; Tressler, L. E.; Nguyen, T. L.; Solaja, B. A.; Vennerstrom, J. L.; Schmidt, J. J.; Wipf, P.; Bavari, S.; Gussio, R. Three-dimensional database mining identifies a unique chemotype that unites structurally diverse botulinum neurotoxin serotype A inhibitors in a three-zone pharmacophore. ChemMedChem. 2008, 3, 1905-1912.
  39. Ananthula, R. S.; Ravikumar, M.; Pramod, A. B.; Madala, K. K.; Mahmood, S. K. Strategies for generating less toxic P-selectin inhibitors: Pharmacophore modeling, virtual screening and counter pharmacophore screening to remove toxic hits. J. Mol. Graph. Model. 2008, 27, 546-557.
  40. Joseph, T. B.; Suneel Kumar, B. V.; Santhosh, B.; Kriti, S.; Pramod, A. B.; Ravikumar, M.; Kishore, M. Quantitative structure activity relationship and pharmacophore studies of adenosine receptor A2B inhibitors. Chem. Biol. Drug. Des. 2008, 72, 395-408.
  41. Yang, Q.; Du, L.; Wang, X.; Li, M.; You, Q. Modeling the binding modes of Kv1.5 potassium channel and blockers. J. Mol. Graph. Model. 2008, 27, 178-187.
  42. Wainer, I. W. Investigation of molecular recognition in biological systems using cellular membrane affinity chromatography. Chim. Oggi., 2008, 26, 19-22.
  43. Barillari, C.; Marcou, G.; Rognan, D. Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores. J. Chem. Inf. Model. 2008, 48, 1396-1410.
  44. Markt, P.; McGoohan, C.; Walker, B.; Kirchmair, J.; Feldmann, C.; De Martino, G.; Spitzer, G.; Distinto, S.; Schuster, D.; Wolber, G.; Laggner, C.; Langer, T. Discovery of novel Cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening. J. Chem. Inf. Model. 2008, 48, 1693-1705.
  45. Deng, J.; Taheri, L.; Grande, F.; Aiello, F.; Garofalo, A.; Neamati, N. Discovery of novel anticancer compounds based on a quinoxalinehydrazine pharmacophore. ChemMedChem. 2008, 3, 1677-1686.
  46. Schuster, D.; Nashev, L. G.; Kirchmair, J.; Laggner, C.; Wolber, G.; Langer, T.; Odermatt, A. Discovery of nonsteroidal 17β-hydroxysteroid dehydrogenase 1 inhibitors by pharmacophore-based screening of virtual compound libraries. J. Med. Chem. 2008, 51, 4188-4199.
  47. Leong, M. K.; Chen, Y. M.; Chen, H. B.; Chen, P. H. Development of a new predictive model for interactions with human cytochrome P450 2A6 using pharmacophore ensemble/support vector machine (PhE/SVM) approach. Pharm. Res. 2008, 26, 987-1000.
  48. Gitto, R.; De Luca, L.; Ferro, S.; Occhiuto, F.; Samperi, S.; De Sarro, G.; Russo, E.; Ciranna, L.; Costa, L.; Chimirri, A. Computational studies to discover a new NR2B/NMDA receptor antagonist and evaluation of pharmacological profile. ChemMedChem. 2008, 10, 1539-1548.
  49. Tonelli, M.; Boido, V.; Canu, C.; Sparatore, A.; Sparatore, F.; Paneni, M. S.; Fermeglia, M.; Pricl, S.; La Colla, P.; Casula, L.; Ibba, C.; Collu, D.; Loddo, R. Antimicrobial and cytotoxic arylazoenamines. Part III: Antiviral activity of selected classes of arylazoenamines. Bioorg. Med. Chem. 2008, 16, 8447-8465.
  50. La Regina, G.; D'Auria, F. D.; Tafi, A.; Piscitelli, F.; Olla, S.; Caporuscio, F.; Nencioni, L.; Cirilli, R.; La Torre, F.; De Melo, N. R.; Kelly, S. L.; Lamb, D. C.; Artico, M.; Botta, M.; Palamara, A. T.; Silvestri, R. 1-[(3-Aryloxy-3-aryl)propyl]-1H-imidazoles, New imidazoles with potent activity against candida albicans and dermatophytes. Synthesis, structure-activity relationship, and molecular modeling studies. J. Med. Chem. 2008, 51, 3841-3855.
  51. Nagar, S.; Islam, M. A.; Das, S.; Mukherjee, A.; Saha, A. Pharmacophore mapping of flavones derivatives for aromatase inhibition. Mol. Divers. 2008, 12, 65-76.
  52. Yang, Q.; Du, L.; Wang, X.; Li, M.; You, Q. Modeling the binding modes of Kv1.5 potassium channel and blockers. J. Mol. Graph. Model. 2008, 27, 178-187.
  53. Purushottamachar, P.; Khandelwal, A.; Chopra, P.; Maheshwari, N.; Gediya, L. K.; Vasaitis, T. S.; Bruno, R. D.; Clement, O. O.; Njar, V. C. First pharmacophore-based identification of androgen receptor down-regulating agents: discovery of potent anti-prostate cancer agents. Bioorg. Med. Chem. 2007, 15, 3413-3421.
  54. Taha, M. O.; Bustanji, Y.; Al-Bakri, A. G.; Yousef, A. M.; Zalloum, W. A.; Al-Masri, I. M.; Atallah, N. Discovery of new potent human protein tyrosine phosphatase inhibitors via pharmacophore and QSAR analysis followed by in silico screening. J. Mol. Graph. Model. 2007, 25, 870-884.
  55. Agrafiotis, D. K.; Gibbs, A. C.; Zhu, F.; Izrailev, S.; Martin, E. Conformational sampling of bioactive molecules: a comparative study. J. Chem. Inf. Model. 2007, 47, 1067-1086.
  56. Ekins, S.; Mankowski, D. C.; Hoover, D. J.; Lawton, M. P.; Treadway, J. L.; Harwood, H. J. Jr. Three-dimensional quantitative structure-activity relationship analysis of human CYP51 inhibitors. Drug. Metab. Dispos. 2007, 35, 493-500.
  57. Bharatham, N.; Bharatham, K.; Lee, K. W. Pharmacophore identification and virtual screening for methionyl-tRNA synthetase inhibitors. J. Mol. Graph. Model. 2007, 25, 813-823.
  58. Wei, J.; Wang, S.; Gao, S.; Dai, X.; Gao, Q. 3D-pharmacophore models for selective A2A and A2B adenosine receptor antagonists. J. Chem. Inf. Model. 2007, 47, 613-625.
  59. Ekins, S.; Chang, C.; Mani, S.; Krasowski, M. D.; Reschly, E. J.; Iyer, M.; Kholodovych, V.; Ai, N.; Welsh, W. J.; Sinz, M.; Swaan, P. W.; Patel, R.; Bachmann, K. Human pregnane X receptor antagonists and agonists define molecular requirements for different binding sites. Mol. Pharmacol. 2007, 72, 592-603.
  60. Liu, F.; You, Q. D.; Chen, Y. D. Pharmacophore identification of KSP inhibitors. Bioorg. Med. Chem. Lett. 2007, 17, 722-726.
  61. Equbal, T.; Silakari, O.; Rambabu, G.; Ravikumar, M. Pharmacophore mapping of diverse classes of farnesyltransferase inhibitors. Bioorg. Med. Chem. Lett. 2007, 17, 1594-1600.
  62. Markt, P.; Schuster, D.; Kirchmair, J.; Laggner, C.; Langer, T. Pharmacophore modeling and parallel screening for PPAR ligands. J. Comput. Aided. Mol. Des. 2007, 21, 575-590.
  63. Li, W. X.; Li, L.; Eksterowicz, J.; Ling, X. B.; Cardozo, M. Significance analysis and multiple pharmacophore models for differentiating P-glycoprotein substrates. J. Chem. Inf. Model. 2007, 47, 2429-2438.
  64. Yu, H.; Wang, Z.; Zhang, L.; Zhang, J.; Huang, Q. The discovery of novel vascular endothelial growth factor receptor tyrosine kinases inhibitors: pharmacophore modeling, virtual screening and docking studies. Chem. Biol. Drug. Des. 2007, 69, 204-211.
  65. Ekins, S.; Mestres, J.; Testa, B. In silico pharmacology for drug discovery: applications to targets and beyond. Br. J. Pharmacol. 2007, 152, 21-37.
  66. Lu, A.; Zhang, J.; Yin, X.; Luo, X.; Jiang, H. Farnesyltransferase pharmacophore model derived from diverse classes of inhibitors. Bioorg. Med. Chem. Lett. 2007, 17, 243-249.
  67. Ekins, S.; Ecker, G. F.; Chiba, P.; Swaan, P. W. Future directions for drug transporter modelling. Xenobiotica 2007, 37, 1152-1170.
  68. Ismail, M. A.; Barker, S.; Abou el-Ella, D. A.; Abouzid, K. A.; Toubar, R. A.; Todd, M. H. Design and synthesis of new tetrazolyl- and carboxy-biphenylylmethyl-quinazolin-4-one derivatives as angiotensin II AT1 receptor antagonists. J. Med. Chem. 2006, 49, 1526-1535.
  69. Rella, M.; Rushworth, C. A.; Guy, J. L.; Turner, A. J.; Langer, T.; Jackson, R. M. Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors. J. Chem. Inf. Model. 2006, 46, 708-716.
  70. Lager, E.; Andersson, P.; Nilsson, J.; Pettersson, I.; Nielsen, E. Ø.; Nielsen, M.; Sterner, O.; Liljefors, T. 4-quinolone derivatives: high-affinity ligands at the benzodiazepine site of brain GABA A receptors. synthesis, pharmacology, and pharmacophore modeling. J. Med. Chem. 2006, 49, 2526-2533.
  71. Delfín, D. A.; Bhattacharjee, A. K.; Yakovich, A. J.; Werbovetz, K. A. Activity of and initial mechanistic studies on a novel antileishmanial agent identified through in silico pharmacophore development and database searching. J. Med. Chem. 2006, 49, 4196-4207.
  72. Charlier, C.; Hénichart, J. P.; Durant, F.; Wouters, J. Structural insights into human 5-lipoxygenase inhibition: combined ligand-based and target-based approach. J. Med. Chem. 2006, 49, 186-195.
  73. Steindl, T. M.; Schuster, D.; Wolber, G.; Laggner, C.; Langer, T. High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening. J. Comput. Aided Mol. Des. 2006, 20, 703-715.
  74. Taha, M. O.; Al-Bakri, A. G.; Zalloum, W. A. Discovery of potent inhibitors of pseudomonal quorum sensing via pharmacophore modeling and in silico screening. Bioorg. Med. Chem. Lett. 2006, 16, 5902-5906.
  75. Michaux, C.; de Leval, X.; Julémont, F.; Dogné, J. M.; Pirotte, B.; Durant, F. Structure-based pharmacophore of COX-2 selective inhibitors and identification of original lead compounds from 3D database searching method. Eur. J. Med. Chem. 2006, 41, 1446-1455.
  76. Cappelli, A.; Pericot Mohr, G.; Giuliani, G.; Galeazzi, S.; Anzini, M.; Mennuni, L.; Ferrari, F.; Makovec, F.; Kleinrath, E. M.; Langer, T.; Valoti, M.; Giorgi, G.; Vomero, S. Further studies on imidazo[4,5-b]pyridine AT1 angiotensin II receptor antagonists. Effects of the transformation of the 4-phenylquinoline backbone into 4-phenylisoquinolinone or 1-phenylindene scaffolds. J. Med. Chem. 2006, 49, 6451-6464.
  77. Dayam, R.; Aiello, F.; Deng, J.; Wu, Y.; Garofalo, A.; Chen, X.; Neamati, N. Discovery of small molecule integrin alphavbeta3 antagonists as novel anticancer agents. J. Med. Chem. 2006, 49, 4526-4534.
  78. Manetti, F.; Locatelli, G. A.; Maga, G.; Schenone, S.; Modugno, M.; Forli, S.; Corelli, F.; Botta, M. A combination of docking/dynamics simulations and pharmacophoric modeling to discover new dual c-Src/Abl kinase inhibitors. J. Med. Chem. 2006, 49, 3278-3286.
  79. De Luca, L.; Gitto, R.; Barreca, M. L.; Caruso, R.; Quartarone, S.; Citraro, R.; De Sarro, G.; Chimirri, A. 3D pharmacophore models for 1,2,3,4-tetrahydroisoquinoline derivatives acting as anticonvulsant agents. Arch. Pharm. Chem. Life Sci. 2006, 339, 388-400.
  80. Deng, J.; Sanchez, T.; Neamati, N.; Briggs, J. M. Dynamic pharmacophore model optimization: identification of novel HIV-1 integrase inhibitors. J. Med. Chem. 2006, 49, 1684-1692.
  81. Dessalew, N.; Bharatam, P. V. Investigation of potential glycogen synthase kinase 3 inhibitors using pharmacophore mapping and virtual screening. Chem. Biol. Drug. Des. 2006, 68, 154-165.
  82. Steindl, T. M.; Schuster, D.; Laggner, C.; Langer, T. Parallel screening: a novel concept in pharmacophore modeling and virtual screening. J. Chem. Inf. Model. 2006, 46, 2146-2157.
  83. Bhattacharjee, A. K.; Dheranetra, W.; Nichols, D. A.; Gupta, R. K. 3D pharmacophore model for insect repellent activity and discovery of new repellent candidates.  QSAR Comb. Sci. 2005, 24, 593-602.
  84. Di Santo, R.; Fermeglia, M.; Ferrone, M.; Paneni, M. S.; Costi, R.; Artico, M.; Roux, A.; Gabriele, M.; Tardif, K. D.; Siddiqui, A.; Pricl, S. Simple but highly effective three-dimensional chemical-feature-based pharmacophore model for diketo acid derivatives as hepatitis C virus RNA-dependent RNA polymerase inhibitors. J. Med. Chem. 2005, 48, 6304-6314.
  85. Dayam, R.; Sanchez, T.; Neamati, N. Diketo acid pharmacophore. 2. Discovery of structurally diverse inhibitors of HIV-1 integrase. J. Med. Chem. 2005, 48, 8009-8015.
  86. Deng, J.; Lee, K. W.; Sanchez, T.; Cui, M.; Neamati, N.; Briggs, J. M. Dynamic receptor-based pharmacophore model development and its application in designing novel HIV-1 integrase inhibitors. J. Med. Chem. 2005, 48, 1496-1505.
  87. Hessler, G. Zimmermann, M.; Matter, H.; Evers, A.; Naumann, T.; Lengauer, T.; Rarey, M. Multiple-ligand-based virtual screening: methods and applications of the MTree approach. J. Med. Chem. 2005, 48, 6575-6584.
  88. Barreca, M. L.; Ferro, S.; Rao, A.; De Luca, L.; Zappalà, M.; Monforte, A. M.; Debyser, Z.; Witvrouw, M.; Chimirri, A. Pharmacophore-based design of HIV-1 integrase strand-transfer inhibitors. J. Med. Chem. 2005, 48, 7084-7088.
  89. Wang, N.; DeLisle, R. K.; Diller, D. J. Fast small molecule similarity searching with multiple alignment profiles of molecules represented in one-dimension. J. Med. Chem. 2005, 48, 6980-6990.
  90. Steindl, T. M.; Crump, C. E.; Hayden, F. G.; Langer, T. Pharmacophore modeling, docking, and principal component analysis based clustering: combined computer-assisted approaches to identify new inhibitors of the human rhinovirus coat protein. J. Med. Chem. 2005, 48, 6250-6260.
  91. Steindl, T.; Laggner, C.; Langer, T. Human rhinovirus 3C protease: generation of pharmacophore models for peptidic and nonpeptidic inhibitors and their application in virtual screening. J. Chem. Inf. Model. 2005, 45, 716-724.
  92. Krovat, E. M.; Frühwirth, K. H.; Langer, T. Pharmacophore identification, in silico screening, and virtual library design for inhibitors of the human factor Xa. J. Chem. Inf. Model. 2005, 45, 146-159.
  93. Klabunde, T.; Wendt, K. U.; Kadereit, D.; Brachvogel, V.; Burger, H. J.; Herling, A. W.; Oikonomakos, N. G.; Kosmopoulou, M. N.; Schmoll, D.; Sarubbi, E.; von Roedern, E.; Schönafinger, K.; Defossa, E. Acyl ureas as human liver glycogen phosphorylase inhibitors for the treatment of type 2 diabetes. J. Med. Chem. 2005, 48, 6178-6193.
  94. Evers, A.; Klabunde, T. Structure-based drug discovery using GPCR homology modeling: successful virtual screening for antagonists of the alpha1A adrenergic receptor. J. Med. Chem. 2005, 48, 1088-1097.
  95. Sorich, M. J.; Miners, J. O.; McKinnon, R. A.; Smith, P. A. Multiple pharmacophores for the investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Mol. Pharmacol. 2004, 65, 301-208.
  96. Hirashima, A.; Kimizu, M.; Shigeta, Y.; Matsugu, S.; Eiraku, T.; Kuwano, E.; Eto, M. The pheromone production of female Plodia interpunctella is inhibited by tyraminergic antagonists. Chem. Biodivers. 2004, 1, 1652-1667.
  97. López-Rodríguez, M. L.; Porras, E.; Morcillo, M. J.; Benhamú, B.; Soto, L. J.; Lavandera, J. L.; Ramos, J. A.; Olivella, M.; Campillo, M.; Pardo, L. Optimization of the pharmacophore model for 5-HT7R antagonism. Design and synthesis of new naphtholactam and naphthosultam derivatives. J. Med. Chem. 2003, 46, 5638-5650.
  98. Ekins, S.; Stresser, D. M.; Williams, J. A. In vitro and pharmacophore insights into CYP3A enzymes. Trends Pharmacol. Sci. 2003, 24, 161-166.
  99. Rastelli, G.; Pacchioni, S.; Sirawaraporn, W.; Sirawaraporn, R.; Parenti, M. D.; Ferrari, A. M. Docking and database screening reveal new classes of Plasmodium falciparum dihydrofolate reductase inhibitors. J. Med. Chem. 2003, 46, 2834-2845.
  100. Koide, Y.; Hasegawa, T.; Takahashi, A.; Endo, A.; Mochizuki, N.; Nakagawa, M.; Nishida, A. Development of novel EDG3 antagonists using a 3D database search and their structure-activity relationships. J. Med. Chem. 2002, 45, 4629-4638.
  101. Barbaro, R.; Betti, L.; Botta, M.; Corelli, F.; Giannaccini, G.; Maccari, L.; Manetti, F.; Strappaghetti, G.; Corsano, S. Synthesis, biological evaluation, and pharmacophore generation of new pyridazinone derivatives with affinity toward α1 and α2 adrenoceptors. J. Med. Chem. 2001, 44, 2118-2132.

Patents filed for Pharmacophores

  1. Novel pharmacophore for the discovery and testing of NA,K-ATPASE inhibitor compositions and methods for their use in treating cardiovascular diseases and conditions, DATE: Filed Nov-2003, INVENTORS: Welsh, W. J., Keenan, S. M., Delisle, R. K., Ball, W. J. APPL. NO.: 10/534,296.
  2. Antimalarial and antiproliferative pharmacophore models, novel tryptanthrin compounds having increased solubility, and methods of making and using thereof, DATE: Filed Feb-2003, INVENTORS: Nichols, D. A., Hicks, R. P., Bhattacharjee, A. K. APPL. NO.: 10/359,625.
  3. EGF/EGFR Complex, DATE: Filed Sept-2002, INVENTORS: Yokoyama, S., Ogiso, H., Shirouzu, M., Nureki, O., Ishitani, R., Saito, K., Matsusue, T., Nakao, N., Muramatsu, H., Shinozaki, M. APPL.NO.: 10/503,486.
  4. Classification of polypeptides by ligand geometry and related methods, DATE: Filed Jul-2002, INVENTORS: Sem, D. S., Hansen, M. APPL. NO.: 10/206,786.
  5. Pharmacophore models for the identification of the CTP2D6 inhibitory potency of selective serotonin reuptake inhibitors, DATE: Filed Mar-2001, AUTHOR: Ekins, S. APPL. NO.: 09/804,176.
  6. Pharmacophore models for, methods of screening for, and identification of the cytochrome P-450 inhibitory potency of neurokinin-1 receptor antagonists, DATE: Filed Jan-2001, INVENTORS: Ekins, S., Smith, B. J. APPL. NO.: 09/765,150.
  7. Molecular model for VLA-4 inhibitors, DATE: Filed Jan-1999, INVENTORS: Singh, J., Zheng, Z., Sprague, P., Van Vlijmen, H., Castro, A., Adams, S. P. APPL. NO.: 09/236,784.

QSAR and Library Design

  1. Roy, K.; Paul, S. Docking and 3D QSAR studies of protoporphyrinogen oxidase inhibitor 3H-pyrazolo[3,4-d][1,2,3]triazin-4-one derivatives. J. Mol. Model. 2009, doi:10.1007/s00894-009-0528-8.
  2. Chen, K. X.; Li, Z. G.; Xie, H. Y.; Gao, J. R.; Zou, J. W. Quantitative structure–activity relationship analysis of aryl alkanol piperazine derivatives with antidepressant activities. Eur. J. Med. Chem. 2009, doi:10.1016/j.ejmech.2009.05.029.
  3. Nargotra, A.; Sharma, S.; Koul, J. L.; Sangwan, P. L.; Khan, I. A.; Kumar, A.; Taneja, S. C.; Koul, S. Quantitative structure activity relationship (QSAR) of piperine analogs for bacterial NorA efflux pump inhibitors. Eur. J. Med. Chem. 2009, 44, 4128-4135.
  4. Bhonsle, J. B.; Venugopal, D.; Huddler, D. P.; Magill, A. J.; Hicks, R. P. Application of 3D-QSAR for identification of descriptors defining bioactivity of antimicrobial peptides. J. Med. Chem. 2007, 50, 6545-6553.
  5. Cheg, D.; Xu, W. R.; Liu, C. X. Relationship of quantitative structure and pharmacokinetics in fluoroquinolone antibacterials. World J. Gastroenterol. 2007, 13, 2496-2503.
  6. Jiang F. C.; Cheng, C. Y. The design and synthesis of 2-aminothiazole derivatives and their inhibitory activity on apoptosis. Yao Xue Xue Bao 2006, 41, 727-734.
  7. Macchiarulo, A.; De Luca, L.; Costantino, G.; Barreca, M. L.; Gitto, R.; Pellicciari, R.; Chimirri A. QSAR study of anticonvulsant negative allosteric modulators of the AMPA receptor. J. Med. Chem. 2004, 47, 860-863.
  8. Jozwiak, K.; Ravichandran, S.; Collins, J. R.; Wainer, I. W. Interaction of noncompetitive inhibitors with an immobilized α3β4 nicotinic acetylcholine receptor investigated by affinity chromatography, quantitative-structure activity relationship analysis, and molecular docking. J. Med. Chem. 2004, 47, 4008-4021.
  9. Sanders, J. M.; Ghosh, S.; Chan, J. M.; Meints, G.; Wang, H.; Raker, A. M.; Song, Y.; Colantino, A.; Burzynska, A.; Kafarski, P.; Morita, C. T.; Oldfield, E. Quantitative structure-activity relationships for gammadelta T cell activation by bisphosphonates. J. Med. Chem. 2004, 47, 375-384.
  10. Bednarczyk D.; Ekins, S.; Wikel, J. H.; Wright, S. H. Influence of molecular structure on substrate binding to the human organic cation transporter, hOCT. Mol. Pharmacol. 2003, 63, 489-98.

CSAR/ Recursive Partitioning

  1. Stockfisch, T. P. Partially unified multiple property recursive partitioning (PUMP-RP): a new method for predicting and understanding drug selectivity. J. Chem. Inf. Comput. Sci. 2003, 43, 1608-1613.
  2. Rao, S. N.; Stockfisch, T. P. Partially unified multiple property recursive partitioning (PUMP-RP) analyses of cyclooxygenase (COX) inhibitors. J. Chem. Inf. Comput. Sci. 2003, 43, 1614-22.
  3. Seidler, J.; McGovern, S. L.; Doman, T. N.; Shoichet, B. K. Identification and prediction of promiscuous aggregating inhibitors among known drugs. J. Med. Chem. 2003, 46, 4477-4486.
  4. van Rhee, A. M.; Stocker, J.; Printzenhoff, D.; Creech, C.; Wagoner, P. K.; Spear, K. L. Retrospective analysis of an experimental high-throughput screening data set by recursive partitioning. J. Comb. Chem. 2001, 3, 267-277.

GFA

  1. Vadlamudi, S. M.; Kulkarni, V. M. 3D-QSAR of protein tyrosine phosphatase 1B inhibitors by genetic function approximation. Internet Electron. J. Mol. Des. 2004, 3, 586-609.
  2. Lucić, B.; Nadramija, D.; Basic, I.; Trinajstić, N. Toward generating simpler QSAR models: nonlinear multivariate regression versus several neural network ensembles and some related methods. J. Chem. Inf. Comput. Sci. 2003, 43, 1094-1102.
  3. Sutherland, J. J.; Weaver, D. F. Development of quantitative structure-activity relationships and classification models for anticonvulsant activity of hydantoin analogues. J. Chem. Inf. Comput. Sci. 2003, 43, 1028-1036.
  4. Kharkar, P. S.; Desai, B.; Gaveria, H.; Varu, B.; Loriya, R.; Naliapara, Y.; Shah, A.; Kulkarni, V. M. Three-dimensional quantitative structure-activity relationship of 1,4-dihydropyridines as antitubercular agents. J. Med. Chem. 2002, 45, 4858-4867.

Molecular Field Analysis

  1. Hirashima, A.; Nagata, T.; Pan, C.; Kuwano, E.; Taniguchi, E.; Eto M. Three-dimensional molecular field analyses of octopaminergic agonists and antagonists for the locust neuronal octopamine receptor class 3. J. Mol. Graph. Model. 1999, 17, 198-206, 218.
  2. Hirashima, A.; Rafaeli, A.; Gileadi, C.; Kuwano, E. Three-dimensional quantitative structure-activity studies of octopaminergic agonists responsible for the inhibition of sex-pheromone production in Helicoverpa armigera. Bioorg. Med. Chem. 1999, 7, 2621-2628.
  3. Drew, M. G. B.; Wilden, G. R. H. Quantitative Structure−Activity Relationship Studies of Sulfamates RNHSO3Na: Distinction between Sweet, Sweet-Bitter, and Bitter Molecules. J. Agric. Food Chem. 1998, 46, 3016-3026.

Molecular Surface Analysis

  1. Leonard J. T; Roy, K. Exploring molecular shape analysis of styrylquinoline derivatives as HIV-1 integrase inhibitors. Eur. J. Med. Chem. 2008, 43, 81-92. (MFA/MSA/RSA)
  2. Roy, K.; Leonard, J. T. QSAR analyses of 3-(4-benzylpiperidin-1-yl)-N-phenylpropylamine derivatives as potent CCR5 antagonists. J. Chem. Inf. Model. 2005, 45, 1352-1368.

Neural Nets

  1. O'Brien, S. E.; de Groot, M. J. Greater than the sum of its parts: combining models for useful ADMET prediction. J. Med. Chem. 2005, 48, 1287-1291.

On The Fly Library Optimization

  1. Jamois, E. A, Lin, C. T.; Waldman, M. Design of focused and restrained subsets from extremely large virtual libraries. J. Mol. Graph. Model. 2003. 22, 141-149.

Receptor Surface Analysis

  1. Macchiarulo, A.; Costantino, G.; Meniconi, M.; Pleban, K.; Ecker, G.; Bellocchi, D.; Pellicciari, R. Insights into phenylalanine derivatives recognition of VLA-4 integrin: from a pharmacophoric study to 3D-QSAR and molecular docking analyses. J. Chem. Inf. Comput. Sci. 2004, 44, 1829-1839.
  2. Datar, P. A.; Desai, P. V.; Coutinho, E. C. A 3D-QSAR of angiotensin II (AT1) receptor antagonists based on receptor surface analysis. J. Chem. Inf. Comput. Sci. 2004, 44, 210-220.
  3. Ghoshal, N.; Mukherjee, P. K. 3-D-QSAR of N-substituted 4-amino-3,3-dialkyl-2(3H)-furanone GABA receptor modulators using molecular field analysis and receptor surface modelling study. Bioorg. Med. Chem. Lett. 2004, 14, 103-109. (RSA/MFA)
  4. Costantino, G.; Macchiarulo, A.; Camaioni, E.; Pellicciari, R. Modeling of poly(ADP-ribose)polymerase (PARP) inhibitors. Docking of ligands and quantitative structure-activity relationship analysis. J. Med. Chem. 2001, 44, 3786-3794.
  5. Hahn, M.; Rogers, D. Receptor surface models. 2. Application to quantitative structure-activity relationships studies. J. Med. Chem. 1995, 38, 2091-2102.
  6. Hahn, M. Receptor surface models. 1. Definition and construction. J. Med. Chem. 1995, 38, 2080-2090.

ADMET

  1. Borodina, Y.; Rudik, A.; Filimonov, D.; Kharchevnikova, N.; Dmitriev, A.; Blinova, V.; Poroikov, V. A new statistical approach to predicting aromatic hydroxylation sites. Comparison with model-based approaches. J. Chem. Inf. Comput. Sci. 2004, 44, 1998-2009.
  2. Venkatapathy, R.; Moudgal, C. J.; Bruce, R. M. Assessment of the oral rat chronic lowest observed adverse effect level model in TOPKAT, a QSAR software package for toxicity prediction. J. Chem. Inf. Comput. Sci. 2004, 44, 1623-1629.
  3. Desai, P. V.; Patny, A.; Sabnis, Y.; Tekwani, B.; Gut, J.; Rosenthal, P.; Srivastava, A.; Avery, M. Identification of novel parasitic cysteine protease inhibitors using virtual screening. 1. The ChemBridge database. J. Med. Chem. 2004, 47, 6609-6615.
  4. Cheng, A.; Merz, K. M. Jr. Prediction of aqueous solubility of a diverse set of compounds using quantitative structure-property relationships. J. Med. Chem. 2003, 46, 3572-3580.
  5. Cheng, A.; Dixon, S. L. In silico models for the prediction of dose-dependent human hepatotoxicity. J. Comput. Aided Mol. Des. 2003, 17, 811-823.
  6. Susnow, R. G.; Dixon, S. L. Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition. J. Chem. Inf. Comput. Sci. 2003, 43, 1308-1315.
  7. Richard, A. M.; Williams, C. R. Distributed structure-searchable toxicity (DSSTox) public database network: a proposal. Mutat Res. 2002, 499, 27-52.
  8. Egan, W. J.; Merz, K. M. Jr.; Baldwin, J. J. Prediction of drug absorption using multivariate statistics. J. Med. Chem. 2000, 43, 3867-3877.
  9. Ghose, A. K.; Viswanadhan, V. N.; Wendoloski, J. J.; Prediction of hydrophobic (lipophilic) properties of small organic molecules using fragmental methods: an analysis of ALOGP and CLOGP methods. J. Phys. Chem. 1998, 102, 3762-3772.
  10. Prentis, R. A.; Lis, Y.; Walker, S. R. Pharmaceutical innovation by the seven UK-owned pharmaceutical companies (1964-1985). Br. J. Clin. Pharmac. 1988, 25, 387-396.

Books Citing Accelrys Science

  1. 1. Ekins, S., Ed. Computational Toxicology: Risk Assessment for Pharmaceutical and Environmental Chemicals; Wiley-Interscience: Hoboken, NJ, 2007.
  2. Langer, T., Hoffmann, R., Eds. Pharmacophores and Pharmacophore Searches; Wiley-VCH: Weinheim, Germany, 2006.
  3. Leach, A. Molecular Modelling: Principles and Applications, 2nd ed.; Prentice Hall: Essex, England, 2001
  4. Ghose, A. K.; Viswanadhan, V. N. Combinatorial Library Design and Evaluation: Principles, Software, Tools, and Applications in Drug Discovery, CRC: New York, NY, 2001.
  5. Güner, O. Pharmacophore Perception, Development, and Use in Drug Design; International University Line: La Jolla, CA, 2000.

Accelrys Scientific Patents

  1. Method of Estimating Solvation Energies of Membrane Bound Molecules. DATE: May 05, 2006, INVENTORS: V. Z. Spassov, L. Yan, S. Szalma, United States Patent, Application No.: 60/307,502, Allowed for issuance.
  2. Molecular interactions with atomic parameters including anisotropic dipole polarizability, DATE: Granted Oct-2002, INVENTORS: Jon Maple, Carl Ewig, Marvin Waldman
  3. Methods and systems for estimating binding affiinity, DATE: Granted May-2004, INVENTORS: Paul Kirchoff, C.M. Venkatachalam, Jeff Jiang, Marvin Waldman
  4. Apparatus and method for monitoring the validity of a molecular model, DATE: Granted Jun-2003, INVENTOR: John D. Clark, CONTINUATION: Granted: Jul-2004
  5. One Dimensional Molecular Representations, DATE: filed Jan-2001, INVENTORS: Steven L. Dixon, Kenneth Merz, Marvin Waldman
  6. Method and System for Classifying Compounds: DATE: filed Dec-2001, INVENTOR: Thomas Stockfisch
  7. Method & system for computationally estimating transition geometry between reactant & product structures, DATE: filed Jun-2002, INVENTORS: Niranjan Govind, Dominic King-Smith, George Fitzgerald
  8. Method for Accommodating Missing Descriptor and Property Data while Training Back-propagation Neural Network Models for QSAR, filed Dec-2002, INVENTOR: Thomas Stockfisch
  9. Chart-Ahead Method for Decision Tree Construction Using Cluster-based Screening to Find Useful Alternative Splits, filed Dec-2002, INVENTOR: Thomas Stockfisch
  10. Pharmacophore Model Generation and Use: DATE: filed Sep-2004, INVENTORS: Al Maynard, Jon Sutter, Marvin Waldman
  11. Method of Estimating Solvation Energies of Membrane Bound Molecules. V. Spassov, L. Yan, S. Szalma. United States Patent, Filed: July 23, 2002, Application No.: 60/307,502

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