Pharmacophore Modelling and Screening: Concepts, Recent Developments and Applications in Rational Drug Design

  • Chinmayee Choudhury
  • G. Narahari SastryEmail author
Part of the Challenges and Advances in Computational Chemistry and Physics book series (COCH, volume 27)


Computational design of molecules with desired properties has become indispensable in many areas of research, particularly in the pharmaceutical industry and academia. Pharmacophore is one of the essential state-of-the-art techniques widely used in various ways in the computer-aided drug design projects. The pharmacophore modelling approaches have been an important part of many drug discovery strategies due to its simple yet diverse usage. It has been extensively applied for virtual screening, lead optimization, target identification, toxicity prediction and de novo lead design and has a huge scope for application in fragment-based drug design and lead design targeting protein–protein interaction interfaces and target-based classification of chemical space. In this chapter, we have briefly discussed the basic concepts and methods of generation of pharmacophore models. The diverse applications of the pharmacophore approaches have been discussed using number of case studies. We conclude with the limitations of the approaches and its wide scope for the future application depending on the research problem and the type of initial data available.


Computer-aided drug design Pharmacophore mapping Receptor-based pharmacophore Ligand-based pharmacophore Pharmacophore features Pharmacophore fingerprints Virtual screening Pharmacophore searching Docking QSAR De novo design 



Absorption, distribution, metabolism, excretion, toxicity


Computer-aided drug design


Mycobacterial cyclopropane synthase


Hexadecahydro-1H-Cyclopenta[a]Phenanthrene Framework


High-throughput screening


Molecular dynamics


Mycobacterium tuberculosis


Quantitative structure-activity relationship





CC and GNS thank the Department of Science and Technology (DST), Government of India, for financial support in the forms of DST-INSPIRE Faculty Award [DST/INSPIRE/04/2016/000732] and JC Bose Fellowship, respectively.


  1. 1.
    Myers S, Baker A (2001) Drug discovery—an operating model for a new era. Nat Biotechnol 19:727–730PubMedCrossRefGoogle Scholar
  2. 2.
    Moses H III, Dorsey ER, Matheson DH et al (2005) Financial anatomy of biomedical research. JAMA 294:1333–1342PubMedCrossRefGoogle Scholar
  3. 3.
    Lahana R (1999) How many leads from HTS? Drug Discov Today 4:447–448PubMedCrossRefGoogle Scholar
  4. 4.
    Veselovsky AV, Zharkova MS, Poroikov VV et al (2014) Computer-aided design and discovery of protein-protein interaction inhibitors as agents for anti-HIV therapy. SAR QSAR Environ Res 25:457–471PubMedCrossRefGoogle Scholar
  5. 5.
    Song CM, Lim SJ, Tong JC (2009) Recent advances in computer-aided drug design. Brief Bioinform 10:579–591PubMedCrossRefGoogle Scholar
  6. 6.
    Taft CA, Da Silva VB, Da Silva CH (2008) Current topics in computer-aided drug design. J Pharm Sci 97:1089–1098PubMedCrossRefGoogle Scholar
  7. 7.
    Thiel KA (2004) Structure-aided drug design’s next generation. Nat Biotechnol 22:513–519PubMedCrossRefGoogle Scholar
  8. 8.
    Reddy AS, Amarnath HSD, Bapi RS et al (2008) Protein ligand intreraction database (PLID): datamining analysis of structure-function relationships. Comput Biol Chem 32:387–390PubMedCrossRefGoogle Scholar
  9. 9.
    Reddy ChS, Vijayasarathy K, Srinivas E et al (2006) Homology modeling of membrane proteins: a critical assessment. Comput Biol Chem 30:120–126PubMedCrossRefGoogle Scholar
  10. 10.
    Srivastava HK, Sastry GN (2012) A molecular dynamics investigation on a series of HIV protease inhibitors: assessing the performance of MM-PBSA and MM-GBSA approaches. J Chem Inf Model 52:3088–3098PubMedCrossRefGoogle Scholar
  11. 11.
    Choudhury C, Priyakumar UD, Sastry GN (2014) Molecular dynamics investigation of the active site dynamics of mycobacterial cyclopropane synthase during various stages of the cyclopropanation process. J Struct Biol 187:38–48PubMedCrossRefGoogle Scholar
  12. 12.
    Badrinarayan P, Sastry GN (2014) Specificity rendering ‘hot-spots’ for aurora kinase inhibitor design: the role of non-covalent interactions and conformational transitions. PLoS ONE 9:e113773PubMedPubMedCentralCrossRefGoogle Scholar
  13. 13.
    Srivastava HK, Choudhury C, Sastry GN (2012) The efficacy of conceptual DFT descriptors and docking scores on the QSAR models of HIV protease inhibitors. Med Chem 8:811–825PubMedCrossRefGoogle Scholar
  14. 14.
    Srivani P, Srinivas E, Raghu R et al (2007) Molecular modeling studies of pyridopurinone derivatives—potential phosphodiesterase 5 inhibitors. J Mol Graph Model 26:378–390PubMedCrossRefGoogle Scholar
  15. 15.
    Janardhan S, RamVivek M, Sastry GN (2016) Modeling the permeability of drug-like molecules through the cell wall of mycobacterium tuberculosis: an analogue based approach. Mol Bio Sys 12:3377–3384Google Scholar
  16. 16.
    Bohari MH, Sastry GN (2012) FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols. J Mol Model 18:4263–4274PubMedCrossRefGoogle Scholar
  17. 17.
    Choudhury C, Priyakumar UD, Sastry GN (2015) Dynamics based pharmacophore models for screening potential inhibitors of mycobacterial cyclopropane synthase. J Chem Inf Model 55:848–860PubMedCrossRefGoogle Scholar
  18. 18.
    Choudhury C, Priyakumar UD, Sastry GN (2016) Dynamic ligand-based pharmacophore modeling and virtual screening to identify mycobacterial cyclopropane synthase inhibitors. J Chem Sci 128:719–732CrossRefGoogle Scholar
  19. 19.
    Reddy AS, Pati SP, Kumar PP et al (2007) Virtual screening in drug discovery—a computational perspective. Curr Protein Pept Sci 8:329–351PubMedCrossRefGoogle Scholar
  20. 20.
    Choudhury C, Priyakumar UD, Sastry GN (2016) Structural and functional diversities of the hexadecahydro-1H-cyclopenta[a] phenanthrene framework, a ubiquitous scaffold in steroidal hormones. Mol Inform 35:145–157PubMedCrossRefGoogle Scholar
  21. 21.
    Agrafiotis DK, Bandyopadhyay D, Wegner JK et al (2007) Recent advances in chemoinformatics. J Chem Inf Model 47:1279–1293PubMedCrossRefGoogle Scholar
  22. 22.
    Vogt M, Bajorath J (2012) Chemoinformatics: a view of the field and current trends in method development. Bioorg Med Chem 20:5317–5323PubMedCrossRefGoogle Scholar
  23. 23.
    Kapetanovic IM (2008) Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. Chem Biol Inter 171:165–176CrossRefGoogle Scholar
  24. 24.
    Karelson M, Lobanov VS, Katritzky AR (1996) Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev 96:1027–1044PubMedCrossRefGoogle Scholar
  25. 25.
    Gozalbes R, Doucet JP, Derouin F (2002) Application of topological descriptors in QSAR and drug design: history and new trends. Curr Drug Targets Infect Disord 2:93–102PubMedCrossRefGoogle Scholar
  26. 26.
    Perkins R, Fang H, Tong W et al (2003) Quantitative structure-activity relationship methods: perspectives on drug discovery and toxicology. Environ Toxicol Chem 22:1666–1679PubMedCrossRefGoogle Scholar
  27. 27.
    Kitchen DB, Decornez H, Furr JR (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949PubMedCrossRefGoogle Scholar
  28. 28.
    Paul N, Kellenberger E, Bret G et al (2004) Recovering the true targets of specific ligands by virtual screening of the protein data bank. Proteins 54:671–680PubMedCrossRefGoogle Scholar
  29. 29.
    Kharkar PS, Warrier S, Gaud RS (2014) Reverse docking: a powerful tool for drug repositioning and drug rescue. Future Med Chem 6:333–342PubMedCrossRefGoogle Scholar
  30. 30.
    Ravindra GK, Srivani P, Achaiah G et al (2007) Strategies to design pyrazolyl urea derivatives for p38 kinase inhibition: a molecular modeling study. J Comput Aided Mol Des 25:155–166Google Scholar
  31. 31.
    Srivastava HK, Bohari M, Sastry GN (2012) Modeling anti-HIV compounds: the role of analogue based approaches. Curr Comput Aided Drug Des 8:224–248PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    Gaur AS, Bhardwaj A, Sharma A et al (2017) Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis (MPDSTB). J Chem Sci 129:515CrossRefGoogle Scholar
  33. 33.
    Yang SY (2010) Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov Today 15:444–450PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Braga RC, Andrade CH (2013) Assessing the performance of 3D pharmacophore models in virtual screening: how good are they? Curr Topics Med Chem 13:1127–1138CrossRefGoogle Scholar
  35. 35.
    Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45:160–169PubMedCrossRefGoogle Scholar
  36. 36.
    Koutsoukas A, Simms B, Kirchmair J et al (2011) From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 74:2554–2574PubMedCrossRefGoogle Scholar
  37. 37.
    Ehrlich P (1909) Ueber den jetzigen Stand der Chemotherapie. Ber Dtsch Chem Ges 42:17–47CrossRefGoogle Scholar
  38. 38.
    Ehrlich P, Morgenroth J. Über Haemolysine (1900) Dritte Mitteilung. Berl Klin Wochnschr 37:453–457Google Scholar
  39. 39.
    Wermuth CG, Ganellin CR, Lindberg P et al (1998) Glossary of terms used in medicinal chemistry (IUPAC recommendations 1997). Annu Rep Med Chem 33:385–395Google Scholar
  40. 40.
    Güner OF (ed) (2000) Pharmacophore perception, development, and use in drug design, vol 2. Internat’l University LineGoogle Scholar
  41. 41.
    Bush B, Sheridan RJ (1993) PATTY: a programmable atom type and language for automatic classification of atoms in molecular databases. Chem Inf Comput Sci 33:756–762CrossRefGoogle Scholar
  42. 42.
    Greene J, Kahn S, Savoj H et al (1994) Chemical function queries for 3D database search. J Chem Inf Comput Sci 34:1297–1308CrossRefGoogle Scholar
  43. 43.
    Molecular Simulations Inc. (MSI), “Catalyst Software”.
  44. 44.
    Wang T, Zhou JJ (1998) 3DFS: a new 3D flexible searching system for use in drug design. Chem Inf Comput Sci 38:71–77CrossRefGoogle Scholar
  45. 45.
    Pickett S, Mason J, McLay IJ (1996) Diversity profiling and design using 3D pharmacophores: pharmacophore-derived queries (PDQ). Chem Inf Comput Sci 36:1214–1223CrossRefGoogle Scholar
  46. 46.
    Dixon SL, Smondyrev AM, Knoll EH et al (2006) PHASE: a new engine for pharmacophore perception, 3D QSAR model development, and 3D database screening: 1. Methodology and preliminary results. J Comput Aided Mol Des 20:647–671PubMedCrossRefPubMedCentralGoogle Scholar
  47. 47.
    Schueler FW (1946) Sex hormonal action and chemical constitution. Science 103:221–223PubMedCrossRefPubMedCentralGoogle Scholar
  48. 48.
    Beckett AH (1959) Stereochemical factors in biological activity. In: Fortschritte der Arzneimittel Forschung. Birkhäuser Verlag, Basel, pp 455–530CrossRefGoogle Scholar
  49. 49.
    Kier LB (1967) Molecular orbital calculation of preferred conformations of acetylcholine, muscarine, and muscarone. Mol Pharmacol 3:487–494PubMedPubMedCentralGoogle Scholar
  50. 50.
    Kier LB (1970) Receptor mapping using mo theory. In: Danielli JF, Moran JF, Triggle DJ (eds) Fundamental concepts in drug-receptor interactions, Academic Press: New YorkGoogle Scholar
  51. 51.
    Kier LB (ed) (1971) MO theory in drug research. Academic Press, New York, pp 164–169Google Scholar
  52. 52.
    Evans BE, Rittle KE, Bock MG et al (1988) Methods for drug discovery: development of potent, selective, orally effective cholecystokinin antagonists. J Med Chem 31:2235–2246PubMedCrossRefGoogle Scholar
  53. 53.
    Thompson LA, Ellman JA (1966) Synthesis and applications of small molecule libraries. Chem Rev 96:555–600CrossRefGoogle Scholar
  54. 54.
    Wermuth CG (1998) Search for new lead compounds: the example of the chemical and pharmacological dissection of aminopyridazines. J Heterocycl Chem 35:1091–1100CrossRefGoogle Scholar
  55. 55.
    Woods DD, Fildes P (1940) The anti-sulphanilamide activity (in vitro) ofp-aminobenzoic acid and related compounds. Chem Ind 59:133–134Google Scholar
  56. 56.
    Easson LH, Stedman E (1933) Studies on the relationship between chemical constitution and physiological action. V. Molecular dissymmetry and physiological activity. Biochem J 27:1257–1266PubMedPubMedCentralCrossRefGoogle Scholar
  57. 57.
    Peroutka SJ, U’Prichard DC, Greenberg DA et al (1977) Neuroleptic drug interactions with norepinephrine alpha receptor binding sites in rat brain. Neuropharmacology 16:549–556PubMedCrossRefGoogle Scholar
  58. 58.
    Pullmann B, Coubeils JL, Courrière P et al (1972) Quantum mechanical study of the conformational properties of phenethylamines of biochemical and medicinal interest. J Med Chem 15:17–23CrossRefGoogle Scholar
  59. 59.
    Leach AR, Gillet VJ, Lewis RA (2010) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53:539–558PubMedCrossRefGoogle Scholar
  60. 60.
    Güner OF (2002) History and evolution of the pharmacophore concept in computer-aided drug design. Curr Top Med Chem 2:1321–1332PubMedCrossRefGoogle Scholar
  61. 61.
    Maynard AJ (2004) HypoGenRefine and HipHopRefine: pharmacophore refinement using steric information from inactive compounds. Presented at the ACS national meeting, Spring, 2004Google Scholar
  62. 62.
    Jones G, Willett P, Glen R (2000) GASP: genetic algorithm superposition program. In: Pharmacophore perception, development, and use in drug design, vol 2. International University Line, La Jolla, CA, USA, pp 85–106Google Scholar
  63. 63.
    Jones G, Willett P, Glen RC (1995) A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des 9:532–549PubMedCrossRefGoogle Scholar
  64. 64.
    GALAHAD. Tripos, St. Louis, MO.
  65. 65.
    Lin A, Overview of pharmacophore applications in MOE.
  66. 66.
    Vlachakis D, Fakourelis P, Makris C, Kossida S (2015) DrugOn: a fully integrated pharmacophore modeling and structure optimization toolkit. PeerJ 3:e725PubMedPubMedCentralCrossRefGoogle Scholar
  67. 67.
    Khedkar SA, Malde AK, Coutinho EC et al (2007) Pharmacophore modeling in drug discovery and development: an overview. Med Chem 3:187–197PubMedCrossRefGoogle Scholar
  68. 68.
    Langer T, Hoffmann RD (eds) (2006) Pharmacophores and pharmacophore searches, pharmacophores and pharmacophore searches. Wiley-VCH Verlag GmbH & Co. KGaA, WeinheimGoogle Scholar
  69. 69.
    Finn PW, Kavraki LE, Latombe JC et al (1997) Rapid: randomized pharmacophore identification for drug design. Comput Geom Theor Appl 10:263–272CrossRefGoogle Scholar
  70. 70.
    Crandell C, Smith D (1983) Computer-assisted examination of compounds for common three-dimensional substructures. J Chem Inf Comp Sci 23:186–197CrossRefGoogle Scholar
  71. 71.
    Martin YC (2000) DISCO: what we did right and what we missed. In: Pharmacophore perception, development, and use in drug design. International University Line, pp 49–68Google Scholar
  72. 72.
    Dolata D, Parrill A, Walters W (1998) CLEW: the generation of pharmacophore hypotheses through machine learning. SAR QSAR Environ Res 9:53–81CrossRefGoogle Scholar
  73. 73.
    Chen X, Rusinko A III, Tropsha A et al (1999) Automated pharmacophore Identification for large chemical data sets. J Chem Inf Comput Sci 39:887–896PubMedCrossRefGoogle Scholar
  74. 74.
    Handschuh S, Wagener M, Gasteiger JJ (1998) Superposition of three-dimensional chemical structures allowing for conformational flexibility by a hybrid method. J Chem Inf Comput Sci 38:220–232PubMedCrossRefGoogle Scholar
  75. 75.
    Holliday J, Willet P (1997) Using a genetic algorithm to identify common structural features in sets of ligands. J Mol Graph Model 15:203–253CrossRefGoogle Scholar
  76. 76.
    Bron C, Kerbosch J (1973) Algorithm 457: finding all cliques of an undirected graph. Commun ACM 16:575–577CrossRefGoogle Scholar
  77. 77.
    Brint A, Willett P (1987) Algorithms for the identification of three-dimensional maximal common substructures. J Chem Inf Comp Sci 27:152–158CrossRefGoogle Scholar
  78. 78.
    Guilloux VL, Schmidtke P, Tuffery P (2009) Fpocket: an open source platform for ligand pocket detection. BMC Bioinform 10:168–178CrossRefGoogle Scholar
  79. 79.
    Schmidtke P, Bidon-Chanal A, Luque FJ et al (2011) MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories. Bioinformatics 27:3276–3285PubMedCrossRefGoogle Scholar
  80. 80.
    Carlson HA, Masukawa KM, Rubins K et al (2000) Developing a dynamic pharmacophore model for HIV-1 integrase. J Med Chem 43:2100–2114PubMedCrossRefGoogle Scholar
  81. 81.
    Masukawa KM, Carlson HA, McCammon JA (2000) Technique for developing a pharmacophore model that accommodates inherent protein flexibility: an application to HIV-1 integrase. In: Guner OF (ed) Pharmacophore perception, development, and use in drug design. International University LineGoogle Scholar
  82. 82.
    Clark DE, Westhead DR, Sykes RA et al (1996) Active-site-directed 3D database searching: pharmacophore extraction and validation of hits. J Comput Aided Mol Des 10:397–416PubMedCrossRefGoogle Scholar
  83. 83.
    Mahadevi AS, Sastry GN (2013) Cation-p interaction: its role and relevance in chemistry, biology and material science. Chem Rev 113:2100–2138PubMedCrossRefGoogle Scholar
  84. 84.
    Chourasia M, Sastry GM, Sastry GN (2011) Aromatic—aromatic database, A2ID: an analysis of aromatic networks in proteins. Int J Biol Macromol 48:540–552PubMedCrossRefGoogle Scholar
  85. 85.
    Saha S, Sastry GN (2015) Cooperative or anticooperative: how noncovalent interactions influence each other. J Phys Chem B 119:11121–11135PubMedCrossRefGoogle Scholar
  86. 86.
    Badrinarayan P, Choudhury C, Sastry GN (2015) Molecular modeling. In: Dhar PK and Singh V (eds) Systems and synthetic biology (S2B2). Springer Press, pp 93–128Google Scholar
  87. 87.
    Wolber G, Langer T (2005) LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J Chem Inf Model 45:160–169PubMedCrossRefGoogle Scholar
  88. 88.
    Salam NK, Nuti R, Sherman W (2009) Novel method for generating structure-based pharmacophores using energetic analysis. J Chem Inf Model 49:2356–2368PubMedCrossRefGoogle Scholar
  89. 89.
    Friesner R, Murphy RB, Repasky MP et al (2006) Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. J Med Chem 49:6177–6196PubMedCrossRefGoogle Scholar
  90. 90.
    Carlson HA, Masukawa KM, McCammon JA (1999) Method for including the dynamic fluctuations of a protein in computer-aided drug design. J Phys Chem A 103:10213–10219CrossRefGoogle Scholar
  91. 91.
    McGregor MJ, Muskal SM (1999) Pharmacophore fingerprinting. 1. Application to QSAR and focused library design. J Chem Inf Comput Sci 39:569–574PubMedCrossRefPubMedCentralGoogle Scholar
  92. 92.
    McGregor MJ, Muskal SM (2000) Pharmacophore fingerprinting. 2. Application to primary library design. J Chem Inf Comput Sci 40:117–125PubMedCrossRefGoogle Scholar
  93. 93.
    Voet AR, Kumar A, Berenger F et al (2014) Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4. J Comput Aided Mol Des 28:363–373PubMedCrossRefGoogle Scholar
  94. 94.
    Voet A, Helsen C, Zhang KY et al (2013) The discovery of novel human androgen receptor antagonist chemotypes using a combined pharmacophore screening procedure. Chem Med Chem 8:644–651PubMedCrossRefGoogle Scholar
  95. 95.
    Loving K, Salam NK, Sherman W (2009) Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation. J Comput Aided Mol Des 23:541–554PubMedCrossRefGoogle Scholar
  96. 96.
    Anuradha A, Trivelli X, Guérardel Y et al (2007) Thiacetazone, an antitubercular drug that inhibits cyclopropanation of cell wall mycolic acids in mycobacteria. PLoS ONE 12:e1343Google Scholar
  97. 97.
    Guner OF, Bowen JP (2013) Pharmacophore modeling for ADME. Curr Top Med Chem 13:1327–1342PubMedCrossRefGoogle Scholar
  98. 98.
    Yamashita F, Hashida M (2004) In silico approaches for predicting ADME properties of drugs. Drug Metab Pharmacokinet 19:327–338PubMedCrossRefGoogle Scholar
  99. 99.
    de Groot MJ, Ekins S (2002) Pharmacophore modeling of cytochromes P450. Adv Drug Deliv Rev 54:367–383PubMedCrossRefGoogle Scholar
  100. 100.
    Ekins S, de Groot MJ, Jones JP (2001) Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome p450 active sites. Drug Metab Dispos 29:936–944PubMedGoogle Scholar
  101. 101.
    Sorich MJ, Miners JO, McKinnon RA et al (2004) Multiple pharmacophores for the investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Mol Pharmacol 65:301–308PubMedCrossRefGoogle Scholar
  102. 102.
    Hu Y, Bajorath J (2010) Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. J Chem Inf Model 50:2112–2118PubMedCrossRefGoogle Scholar
  103. 103.
    Keiser MJ, Roth BL, Armbruster BN (2007) Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25:197–206PubMedCrossRefGoogle Scholar
  104. 104.
    Koutsoukas A, Simms B, Kirchmair J et al (2011) From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 74:2554–2574PubMedCrossRefGoogle Scholar
  105. 105.
    Xu Y, Liu X, Li S (2013) Combinatorial pharmacophore modeling of organic cation transporter 2 (OCT2) inhibitors: insights into multiple inhibitory mechanisms. 10:4611–4619Google Scholar
  106. 106.
    Rollinger JM, Schuster D, Danzl B et al (2009) In silico target fishing for rationalized ligand discovery exemplified on constituents of ruta graveolens. Planta Med 75:195–204PubMedCrossRefGoogle Scholar
  107. 107.
    Tschinke V, Cohen NJ (1993) The NEWLEAD program: a new method for the design of candidate structures from pharmacophoric hypotheses. Med Chem 36:3863–3870CrossRefGoogle Scholar
  108. 108.
    Bohm HJ (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6:61–78PubMedCrossRefGoogle Scholar
  109. 109.
    Roe D, Kuntz IJ (1995) BUILDER v.2: improving the chemistry of a de novo design strategy. J Comput Aided Mol Des 9:269–282PubMedCrossRefGoogle Scholar
  110. 110.
    Joseph-McCarthy D (1999) Computational approaches to structure-based ligand design. Pharmacol Ther 84:179–191PubMedCrossRefGoogle Scholar
  111. 111.
    Schneider G, Bohm HJ (2002) Virtual screening and fast automated docking methods. Drug Discov Today 7:64–70PubMedCrossRefGoogle Scholar
  112. 112.
    Scior T, Bender A, Tresadern G et al (2012) Recognizing pitfalls in virtual screening: a critical review. J Chem Inf Model 52:867–881PubMedCrossRefGoogle Scholar
  113. 113.
    Vancraenenbroeck R, De Raeymaecker J, Lobbestael E et al (2014) In silico, in vitro and cellular analysis with a kinome-wide inhibitor panel correlates cellular LRRK2 dephosphorylation to inhibitor activity on LRRK2. Front Mol Neurosci 7:51PubMedPubMedCentralCrossRefGoogle Scholar
  114. 114.
    Schomburg KT, Bietz S, Briem H et al (2014) Facing the challenges of structure-based target prediction by inverse virtual screening. J Chem Inf Model 54:1676–1686PubMedCrossRefGoogle Scholar
  115. 115.
    Kirchmair J, Wolber G, Laggner C et al (2006) Comparative performance assessment of the conformational model generators omega and catalyst: a large-scale survey on the retrieval of protein-bound ligand conformations. J Chem Inf Model 46:1848–1861PubMedCrossRefGoogle Scholar
  116. 116.
    Kirchmair J, Laggner C, Wolber G et al (2005) Comparative analysis of protein-bound ligand conformations with respect to catalyst’s conformational space subsampling algorithms. J Chem Inf Model 45:422–430PubMedCrossRefGoogle Scholar
  117. 117.
    Nagamani S, Gaur AS, Tanneeru K et al (2017) Molecular property diagnostic suite (MPDS): development of disease-specific open source web portals for drug discovery. SAR QSAR Environ Res Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Center for Molecular ModellingIndian Institute of Chemical TechnologyHyderabadIndia
  2. 2.Department of BiochemistryAll India Institute of Medical SciencesBasni, JodhpurIndia

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