Skip to main content
SpringerLink
Log in
Book cover

Bioinformatics and Drug Discovery pp 87–124Cite as

Applications and Limitations of In Silico Models in Drug Discovery

  • Protocol
  • First Online:

Part of the book series: Methods in Molecular Biology ((MIMB,volume 910))

Abstract

Drug discovery in the late twentieth and early twenty-first century has witnessed a myriad of changes that were adopted to predict whether a compound is likely to be successful, or conversely enable identification of molecules with liabilities as early as possible. These changes include integration of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches. The in silico models are facilitated by the availability of large datasets associated with high-throughput screening, bioinformatics algorithms to mine and annotate the data from a target perspective, and chemoinformatics methods to integrate chemistry methods into lead design process. This chapter highlights the applications of some of these methods and their limitations. We hope this serves as an introduction to in silico drug discovery.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   159.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Salemme FR, Spurlino J, Bone R (1997) Serendipity meets precision: the integration of structure-based drug design and combinatorial chemistry for efficient drug discovery. Structure 5:319–324

    PubMed  CAS  Google Scholar 

  2. Kubinyi H (1999) Chance favors the prepared mind–from serendipity to rational drug design. J Recept Signal Transduct Res 19:15–39

    PubMed  CAS  Google Scholar 

  3. Schlueter PJ, Peterson RT (2009) Systematizing serendipity for cardiovascular drug discovery. Circulation 120:255–263

    PubMed  Google Scholar 

  4. Chanda SK, Caldwell JS (2003) Fulfilling the promise: drug discovery in the post-genomic era. Drug Discov Today 8:168–174

    PubMed  CAS  Google Scholar 

  5. Voit EO (2002) Metabolic modeling: a tool of drug discovery in the post-genomic era. Drug Discov Today 7:621–628

    PubMed  CAS  Google Scholar 

  6. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br J Pharmacol 152:9–20

    PubMed  CAS  Google Scholar 

  7. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: applications to targets and beyond. Br J Pharmacol 152:21–37

    PubMed  CAS  Google Scholar 

  8. Ekins S (2006) Computer applications in pharmaceutical research and development. Wiley, Hoboken, NJ

    Google Scholar 

  9. Kubinyi H (2006) Success stories of ­computer-aided design. In: Ekins S (ed) Computer applications in pharmaceutical research and development. Wiley, Hoboken, NJ, pp 377–424

    Google Scholar 

  10. Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682–690

    PubMed  CAS  Google Scholar 

  11. Metz JT, Hajduk PJ (2010) Rational approaches to targeted polypharmacology: creating and navigating protein-ligand interaction networks. Curr Opin Chem Biol 14:498–504

    PubMed  CAS  Google Scholar 

  12. Morrow JK, Tian L, Zhang S (2010) Molecular networks in drug discovery. Crit Rev Biomed Eng 38:143–156

    PubMed  Google Scholar 

  13. Scheibye-Alsing K, Hoffmann S, Frankel A, Jensen P, Stadler PF, Mang Y, Tommerup N, Gilchrist MJ, Nygard AB, Cirera S, Jorgensen CB, Fredholm M, Gorodkin J (2009) Sequence assembly. Comput Biol Chem 33:121–136

    PubMed  CAS  Google Scholar 

  14. Huang X (2002) Bioinformatics support for genome sequencing projects. In: Lengauer T (ed) Bioinformatics - from genomes to drugs. Wiley-VCH, Weinheim

    Google Scholar 

  15. Mihara M, Itoh T, Izawa T (2010) SALAD database: a motif-based database of protein annotations for plant comparative genomics. Nucleic Acids Res 38:D835–D842

    PubMed  CAS  Google Scholar 

  16. Katayama S, Kanamori M, Hayashizaki Y (2004) Integrated analysis of the genome and the transcriptome by FANTOM. Brief Bioinform 5:249–258

    PubMed  CAS  Google Scholar 

  17. Blanchette M (2007) Computation and analysis of genomic multi-sequence alignments. Annu Rev Genomics Hum Genet 8:193–213

    PubMed  CAS  Google Scholar 

  18. Mungall CJ, Misra S, Berman BP, Carlson J, Frise E, Harris N, Marshall B, Shu S, Kaminker JS, Prochnik SE, Smith CD, Smith E, Tupy JL, Wiel C, Rubin GM, Lewis SE (2002) An integrated computational pipeline and database to support whole-genome sequence annotation. Genome Biol 3:RESEARCH0081

    PubMed  CAS  Google Scholar 

  19. Lewis SE, Searle SM, Harris N, Gibson M, Lyer V, Richter J, Wiel C, Bayraktaroglir L, Birney E, Crosby MA, Kaminker JS, Matthews BB, Prochnik SE, Smithy CD, Tupy JL, Rubin GM, Misra S, Mungall CJ, Clamp ME (2002) Apollo: a sequence annotation editor. Genome Biol 3:RESEARCH0082

    PubMed  CAS  Google Scholar 

  20. Bernstein FC, Koetzle TF, Williams GJ, Meyer EF, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M (1977) The protein data bank: a computer-based archival file for macromolecular structures. J Mol Biol 112:535–542

    PubMed  CAS  Google Scholar 

  21. Altschul S, Madden T, Schaffer A, Zhang J, Zhang Z, Miller W, Lipman D (1997) Gapped BLAST and PSIBLAST: a New generation of protein database search programs. Nucleic Acids Res 25:3389–3402

    PubMed  CAS  Google Scholar 

  22. Chayen NE (2004) Turning protein crystallisation from an art into a science. Curr Opin Struct Biol 14:577–583

    PubMed  CAS  Google Scholar 

  23. Walsh MA, Evans G, Sanishvili R, Dementieva I, Joachimiak A (1999) MAD data collection - current trends. Acta Crystallogr D Biol Crystallogr 55:1726–1732

    PubMed  CAS  Google Scholar 

  24. Read RJ (2001) Pushing the boundaries of molecular replacement with maximum likelihood. Acta Crystallogr D Biol Crystallogr 57:1373–1382

    PubMed  CAS  Google Scholar 

  25. Drenth J (2002) Principles of Protein X-ray Crystallography. Springer, New York

    Google Scholar 

  26. Brünger AT, Nilges M (1993) Computational challenges for macromolecular structure determination by X-ray crystallography and solution NMR-spectroscopy. Q Rev Biophys 26:49–125

    PubMed  Google Scholar 

  27. Carson M (2007) Macromolecular Crystallography: conventional and high-throughput methods. Oxford University Press, Oxford, pp 191–199

    Google Scholar 

  28. Rhodes G (2006) Crystallography Made Crystal Clear: A Guide for Users of Macromolecular Models, 3rd edn. Academic, New York

    Google Scholar 

  29. Kleywegt GJ, Jones TA (1997) Model building and refinement practice. Methods Enzymol 277:208–230

    PubMed  CAS  Google Scholar 

  30. Luzzati PV (1952) Traitement statistique des erreurs dans la determination des structures cristallines. Acta Cryst 5:802–810

    Google Scholar 

  31. Hirano Y, Yoshinaga S, Takeya R, Suzuki NN, Horiuchi M, Kohjima M, Sumimoto H, Inagaki F (2005) Structure of a cell polarity regulator, a complex between atypical PKC and Par6 PB1 domains. J Biol Chem 280:9653–9661

    PubMed  CAS  Google Scholar 

  32. Morris AL, MacArthur MW, Hutchinson EG, Thornton JM (1992) Stereochemical quality of protein structure coordinates. Proteins 12:345–364

    PubMed  CAS  Google Scholar 

  33. Vriend G, Sander C (1993) Quality control of protein models: directional atomic contact analysis. J Appl Cryst 26:47–60

    CAS  Google Scholar 

  34. Hooft RW, Vriend G, Sander C, Abola EE (1996) Errors in protein structures. Nature 381:272

    PubMed  CAS  Google Scholar 

  35. Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Cryst 26:283–291

    CAS  Google Scholar 

  36. Feng Z, Westbrook J, Berman HM (1998) NUCheck. Rutgers University, New Brunswick, NJ

    Google Scholar 

  37. Vaguine AA, Richelle J, Wodak SJ (1999) SFCHECK: a unified set of procedures for evaluating the quality of macromolecular structure-factor data and their agreement with the atomic model. Acta Crystallogr D Biol Crystallogr 55:191–205

    PubMed  CAS  Google Scholar 

  38. Chen VB, Arendall WB, Headd JJ, Keedy DA, Immormino RM, Kapral GJ, Murray LW, Richardson JS, Richardson DC (2010) MolProbity: all-atom structure validation for macromolecular crystallography. Acta Crystallogr D Biol Crystallogr 66:12–21

    PubMed  Google Scholar 

  39. Ramachandran GN, Ramakrishnan C, Sasisekharan V (1963) Stereochemistry of polypeptide chain configuration. J Mol Biol 7:95–99

    PubMed  CAS  Google Scholar 

  40. Wüthrich K (1990) Protein structure determination in solution by NMR spectroscopy. J Biol Chem 265:22059–22062

    PubMed  Google Scholar 

  41. Schwieters CD, Kuszewski JJ, Tjandra N, Clore GM (2003) The Xplor-NIH NMR molecular structure determination package. J Magn Reson 160:65–73

    PubMed  CAS  Google Scholar 

  42. Topf M, Lasker K, Webb B, Wolfson H, Chiu W, Sali A (2008) Protein structure fitting and refinement guided by cryo-EM density. Structure 16:295–307

    PubMed  CAS  Google Scholar 

  43. Lasker K, Sali A, Wolfson HJ (2010) Determining macromolecular assembly structures by molecular docking and fitting into an electron density map. Proteins 78:3205–3211

    PubMed  CAS  Google Scholar 

  44. Kortagere S, Ekins S (2010) Troubleshooting computational methods in drug discovery. J Pharmacol Toxicol Methods 61:67–75

    PubMed  CAS  Google Scholar 

  45. Kortagere S, Schetz JA (2007) Structure activity relationships. In: Enna SJ, Bylund DB (ed) XPharm, Amsterdam, Elsevier Inc

    Google Scholar 

  46. Hillisch A, Pineda LF, Hilgenfeld R (2004) Utility of homology models in the drug discovery process. Drug Discov Today 9:659–669

    PubMed  CAS  Google Scholar 

  47. Qu X, Swanson R, Day R, Tsai J (2009) A guide to template based structure prediction. Curr Protein Pept Sci 10:270–285

    PubMed  CAS  Google Scholar 

  48. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res 22:4673–4680

    PubMed  CAS  Google Scholar 

  49. Leach AR, Prout K, Dolata DP (1988) An investigation into the construction of ­molecular models by the template joining method. J Comput Aided Mol Des 2:16

    Google Scholar 

  50. Dunbrack RL Jr, Karplus M (1993) ­Backbone-dependent rotamer library for proteins. Application to side-chain prediction. J Mol Biol 230:543–574

    PubMed  CAS  Google Scholar 

  51. Dunbrack RL Jr (2002) Rotamer libraries in the 21st century. Curr Opin Struct Biol 12:431–440

    PubMed  CAS  Google Scholar 

  52. Levitt M (1992) Accurate modeling of protein conformation by automatic segment matching. J Mol Biol 226:507–533

    PubMed  CAS  Google Scholar 

  53. Bruccoleri RE, Karplus M (1990) Conformational sampling using high-­temperature molecular dynamics. Biopolymers 29:1847–1862

    PubMed  CAS  Google Scholar 

  54. van Gelder CW, Leusen FJ, Leunissen JA, Noordik JH (1994) A molecular dynamics approach for the generation of complete protein structures from limited coordinate data. Proteins 18:174–185

    PubMed  Google Scholar 

  55. Sali A, Overington JP, Johnson MS, Blundell TL (1990) From comparisons of protein sequences and structures to protein modelling and design. Trends Biochem Sci 15:235–240

    PubMed  CAS  Google Scholar 

  56. Sali A, Blundell TL (1993) Comparative protein modelling by satisfaction of spatial restraints. J Mol Biol 234:779–815

    PubMed  CAS  Google Scholar 

  57. Sali A, Overington JP (1994) Derivation of rules for comparative protein modeling from a database of protein structure alignments. Protein Sci 3:1582–1596

    PubMed  CAS  Google Scholar 

  58. Fiser A, Sali A (2003) ModLoop: automated modeling of loops in protein structures. Bioinformatics 19:2500–2501

    PubMed  CAS  Google Scholar 

  59. Deane CM, Blundell TL (2001) CODA: a combined algorithm for predicting the structurally variable regions of protein models. Protein Sci 10:599–612

    PubMed  CAS  Google Scholar 

  60. Hornak V, Simmerling C (2003) Generation of accurate protein loop conformations through low-barrier molecular dynamics. Proteins 51:577–590

    PubMed  CAS  Google Scholar 

  61. Coutsias EA, Seok C, Jacobson MP, Dill KA (2004) A kinematic view of loop closure. J Comput Chem 25:510–528

    PubMed  CAS  Google Scholar 

  62. Collura V, Higo J, Garnier J (1993) Modeling of protein loops by simulated annealing. Protein Sci 2:1502–1510

    PubMed  CAS  Google Scholar 

  63. van Vlijmen HW, Karplus M (1997) PDB-based protein loop prediction: parameters for selection and methods for optimization. J Mol Biol 267:975–1001

    PubMed  Google Scholar 

  64. Singh R, Bergert B (2005) Chaintweak: sampling from the neighbourhood of a protein conformation. Pac Symp Biocomput 52–63

    Google Scholar 

  65. de Bakker PI, DePristo MA, Burke DF, Blundell TL (2003) Ab initio construction of polypeptide fragments: accuracy of loop decoy discrimination by an all-atom statistical potential and the AMBER force field with the generalized born solvation model. Proteins 51:21–40

    PubMed  Google Scholar 

  66. Zheng Q, Kyle DJ (1996) Accuracy and reliability of the scaling-relaxation method for loop closure: an evaluation based on extensive and multiple copy conformational samplings. Proteins 24:209–217

    PubMed  CAS  Google Scholar 

  67. Rohl CA, Strauss CE, Chivian D, Baker D (2004) Modeling structurally variable regions in homologous proteins with rosetta. Proteins 55:656–677

    PubMed  CAS  Google Scholar 

  68. Mehler EL, Hassan SA, Kortagere S, Weinstein H (2006) Ab initio computational modeling of loops in G-protein-coupled receptors: lessons from the crystal structure of rhodopsin. Proteins 64:673–690

    PubMed  CAS  Google Scholar 

  69. Xiang Z, Soto CS, Honig B (2002) Evaluating conformational free energies: the colony energy and its application to the problem of loop prediction. Proc Natl Acad Sci USA99:7432–7437

    PubMed  CAS  Google Scholar 

  70. Vriend G (1990) WHAT IF: a molecular modeling and drug design program. J Mol Graph 8:52–56, 29

    PubMed  CAS  Google Scholar 

  71. Laskowski RA, MacArthur MW, Thornton JM (1998) Validation of protein models derived from experiment. Curr Opin Struct Biol 8:631–639

    PubMed  CAS  Google Scholar 

  72. Hastrup H, Sen N, Javitch JA (2003) The human dopamine transporter forms a tetramer in the plasma membrane: cross-linking of a cysteine in the fourth transmembrane segment is sensitive to cocaine analogs. J Biol Chem 278:45045–45048

    PubMed  CAS  Google Scholar 

  73. Szklarz GD, Halpert JR (1997) Use of homology modeling in conjunction with site-directed mutagenesis for analysis of structure-function relationships of mammalian cytochromes P450. Life Sci 61:2507–2520

    PubMed  CAS  Google Scholar 

  74. Kubala M, Obsil T, Obsilova V, Lansky Z, Amler E (2004) Protein modeling combined with spectroscopic techniques: an attractive quick alternative to obtain structural information. Physiol Res 53(Suppl 1):S187–S197

    PubMed  CAS  Google Scholar 

  75. Muller G (2000) Towards 3D structures of G protein-coupled receptors: a multidisciplinary approach. Curr Med Chem 7:861–888

    PubMed  CAS  Google Scholar 

  76. di Luccio E, Koehl P (2011) A quality metric for homology modeling: the H-factor. BMC Bioinformatics 12:48

    PubMed  Google Scholar 

  77. Dong M, Ladaviere L, Penin F, Deleage G, Baggetto LG (1998) Secondary structure of P-glycoprotein investigated by circular dichroism and amino acid sequence analysis. Biochim Biophys Acta 1371:317–334

    PubMed  CAS  Google Scholar 

  78. Pazos F, Helmer-Citterich M, Ausiello G, Valencia A (1997) Correlated mutations contain information about protein-protein interaction. J Mol Biol 271:511–523

    PubMed  CAS  Google Scholar 

  79. Sanchez R, Sali A (1997) Evaluation of comparative protein structure modeling by MODELLER-3. Proteins (Suppl 1):50–58

    Google Scholar 

  80. Sanchez R, Sali A (1997) Advances in comparative protein-structure modelling. Curr Opin Struct Biol 7:206–214

    PubMed  CAS  Google Scholar 

  81. Kabsch W (1976) A solution for the best rotation to relate two sets of vectors. Acta Cryst 32A:922–923

    Google Scholar 

  82. Lathrop RH (1994) The protein threading problem with sequence amino acid interaction preferences is NP-complete. Protein Eng 7:1059–1068

    PubMed  CAS  Google Scholar 

  83. Holm L, Sander C (1993) Protein structure comparison by alignment of distance matrices. J Mol Biol 233:123–138

    PubMed  CAS  Google Scholar 

  84. Shindyalov IN, Bourne PE (1998) Protein structure alignment by incremental combinatorial extension (CE) of the optimal path. Protein Eng 11:739–747

    PubMed  CAS  Google Scholar 

  85. Orengo CA, Taylor WR (1996) SSAP: sequential structural alignment program for protein structure comparison. Methods Enzymol 266:617–635

    PubMed  CAS  Google Scholar 

  86. Akutsu T (1996) Protein structure alignment using dynamic programing and iterative improvement. IEICE Trans Inf Syst 12:1629–1636

    Google Scholar 

  87. Gerstein M, Levitt M (1996) Using iterative dynamic programming to obtain accurate pairwise and multiple alignments of protein structures. Proc Int Conf Intell Syst Mol Biol 4:59–67

    PubMed  CAS  Google Scholar 

  88. Toh H (1997) Introduction of a distance cut-off into structural alignment by the double dynamic programming algorithm. Comput Appl Biosci 13:387–396

    PubMed  CAS  Google Scholar 

  89. Taylor WR (1999) Protein structure comparison using iterated double dynamic programming. Protein Sci 8:654–665

    PubMed  CAS  Google Scholar 

  90. Zhang Y, Skolnick J (2005) TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res 33:2302–2309

    PubMed  CAS  Google Scholar 

  91. Sacan A, Toroslu IH, Ferhatosmanoglu H (2008) Integrated search and alignment of protein structures. Bioinformatics 24:2872–2879

    PubMed  CAS  Google Scholar 

  92. Subbiah S, Laurents DV, Levitt M (1993) Structural similarity of DNA-binding domains of bacteriophage repressors and the globin core. Curr Biol 3:141–148

    PubMed  CAS  Google Scholar 

  93. Kolodny R, Koehl P, Levitt M (2005) Comprehensive evaluation of protein structure alignment methods: scoring by geometric measures. J Mol Biol 346:1173–1188

    PubMed  CAS  Google Scholar 

  94. Zhang Y, Skolnick J (2004) Scoring function for automated assessment of protein structure template quality. Proteins 57:702–710

    PubMed  CAS  Google Scholar 

  95. Sierk ML, Pearson WR (2004) Sensitivity and selectivity in protein structure comparison. Protein Sci 13:773–785

    PubMed  CAS  Google Scholar 

  96. Mayr G, Domingues FS, Lackner P (2007) Comparative analysis of protein structure alignments. BMC Struct Biol 7:50

    PubMed  Google Scholar 

  97. Gerstein M, Levitt M (1998) Comprehensive assessment of automatic structural alignment against a manual standard, the scop classification of proteins. Protein Sci 7:445–456

    PubMed  CAS  Google Scholar 

  98. Krissinel E, Henrick K (2004) Secondary-structure matching (SSM), a new tool for fast protein structure alignment in three dimensions. Acta Crystallogr D Biol Crystallogr 60:2256–2268

    PubMed  CAS  Google Scholar 

  99. Murzin AG, Brenner SE, Hubbard T, Chothia C (1995) SCOP: a structural classification of proteins database for the investigation of sequences and structures. J Mol Biol 247:536–540

    PubMed  CAS  Google Scholar 

  100. Orengo CA, Michie AD, Jones S, Jones DT, Swindells MB, Thornton JM (1997) CATH- a hierarchic classification of protein domain structures. Structure 5:1093–1108

    PubMed  CAS  Google Scholar 

  101. Holm L, Sander C (1998) Touring protein fold space with Dali/FSSP. Nucleic Acids Res 26:316–319

    PubMed  CAS  Google Scholar 

  102. Chandonia JM, Hon G, Walker NS, Conte LL, Koehl P, Levitt M, Brenner SE (2004) The ASTRAL compendium in 2004. Nucleic Acids Res 32:189–192

    Google Scholar 

  103. Hobohm U, Sander C (1994) Enlarged representative set of protein structures. Protein Sci 3:522–524

    PubMed  CAS  Google Scholar 

  104. Wang G, Dunbrack RLJ (2003) PISCES: a protein sequence culling server. Bioinformatics 19:1589–1591

    PubMed  CAS  Google Scholar 

  105. Bhattacharya A, Can T, Kahveci T, Singh AK, Wang Y-F (2004) ProGreSS: simultaneous searching of protein databases by sequence and structure. Pac Symp Biocomput 9:264–275

    Google Scholar 

  106. Plewczynski D, Pas J, von Grotthuss M, Rychlewski L (2002) 3d-hit: fast structural comparison of proteins. Appl Bioinformatics 1:233–235

    Google Scholar 

  107. Budowski-Tal I, Nov Y, Kolodny R (2010) FragBag, an accurate representation of protein structure, retrieves structural neighbors from the entire PDB quickly and accurately. Proc Natl Acad Sci USA107:3481–3486

    PubMed  CAS  Google Scholar 

  108. Tyagi M, Sharma P, Swamy CS, Cadet F, Srinivasan N, de Brevern AG, Offmann B (2006) Protein block expert (PBE): a web-based protein structure analysis server using a structural alphabet. Nucleic Acids Res 34:W119–W123

    PubMed  CAS  Google Scholar 

  109. Tung C-H, Huang J-W, Yang J-M (2007) Kappa-alpha plot derived structural alphabet and BLOSUM-like substitution matrix for rapid search of protein structure database. Genome Biol 8:R31.31–R31.16

    Google Scholar 

  110. Hjaltason GR, Samet H (2003) Index-driven similarity search in metric spaces (survey article). ACM Trans Database Syst 28:517–580

    Google Scholar 

  111. Wolfson HJ, Rigoutsos I (1997) Geometric hashing: an overview. Comput Sci Eng, IEEE 4:10–21

    Google Scholar 

  112. Bachar O, Fischer D, Nussinov R, Wolfson H (1993) A computer vision based technique for 3-D sequence-independent structural comparison of proteins. Protein Eng 6:279–288

    PubMed  CAS  Google Scholar 

  113. Milledge, T., Zheng, G., Mullins, T., and Narasimhan, G. (2007) SBLAST: Structural Basic Local Alignment Searching Tools using Geometric Hashing, Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering, 1343-1347.

    Google Scholar 

  114. Leach AR, Gillet VJ, Lewis RA, Taylor R (2010) Three-dimensional pharmacophore methods in drug discovery. J Med Chem 53:539–558

    PubMed  CAS  Google Scholar 

  115. Spriggs RV, Argymiuk PJ, Willett P (2003) Searching for patterns of amino acids in 3D protein structures. J Chem Inf Comput Sci 43:412–421

    PubMed  CAS  Google Scholar 

  116. Huan J, Bandyopadhyay D, Wang W, Snoeyink J, Prins J, Tropsha A (2005) Comparing graph representations of protein structure for mining family-specific residue-based packing motifs. J Comput Biol 12(6):657–671

    PubMed  CAS  Google Scholar 

  117. Jonassen I, Eidhammer I, Conklin D, Taylor WR (2002) Structure motif discovery and mining the PDB. Bioinformatics 18:362–367

    PubMed  CAS  Google Scholar 

  118. Kleywegt GJ (1999) Recognition of spatial motifs in protein structures. J Mol Biol 285:1887–1897

    PubMed  CAS  Google Scholar 

  119. Wallace AC, Borkakoti N, Thornton JM (1997) TESS: a geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases. Application to enzyme active sites. Protein Sci 6:2308–2323

    PubMed  CAS  Google Scholar 

  120. Barker JA, Thornton JM (2003) An algorithm for constraint-based structural template matching: application to 3D templates with statistical analysis. Bioinformatics 19:1644–1649

    PubMed  CAS  Google Scholar 

  121. Bagley SC, Altman RB (1995) Characterizing the microenvironment surrounding protein sites. Protein Sci 4:622–635

    PubMed  CAS  Google Scholar 

  122. Liang MP, Banatao DR, Klein TE, Brutlag DL (2003) WebFEATURE: an interactive web tool for identifying and visualizing functional sites on macromolecular structures. Nucleic Acids Res 31:3324–3327

    PubMed  CAS  Google Scholar 

  123. Sacan A, Ozturk O, Ferhatosmanoglu H, Wang Y (2007) LFM-Pro: a tool for detecting significant local structural sites in proteins. Bioinformatics 23:709–716

    PubMed  CAS  Google Scholar 

  124. Nussinov R, Wolfson HJ (1991) Efficient detection of three-dimensional structural motifs in biological macromolecules by computer vision techniques. Proc Natl Acad Sci USA88:10495–10499

    PubMed  CAS  Google Scholar 

  125. Guttman A (1984) R-trees: a dynamic index structure for spatial searching. ACM SIGMOD, 419–429

    Google Scholar 

  126. Koshland DE Jr (ed) (1970) Enzymes, vol 1, 3rd edn. New York, Academic

    Google Scholar 

  127. Halperin I, Ma B, Wolfson H, Nussinov R (2002) Principles of docking: an overview of search algorithms and a guide to scoring functions. Proteins 47:409–443

    PubMed  CAS  Google Scholar 

  128. Lorber DM, Shoichet BK (2005) Hierarchical docking of databases of multiple ligand conformations. Curr Top Med Chem 5:739–749

    PubMed  CAS  Google Scholar 

  129. Koca J (1998) Travelling through conformational space: an approach for analyzing the conformational behaviour of flexible molecules. Prog Biophys Mol Biol 70:137–173

    PubMed  CAS  Google Scholar 

  130. Miller MD, Kearsley SK, Underwood DJ, Sheridan RP (1994) FLOG: a system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure. J Comput Aided Mol Des 8:153–174

    PubMed  CAS  Google Scholar 

  131. Sousa SF, Fernandes PA, Ramos MJ (2006) Protein-ligand docking: current status and future challenges. Proteins 65:15–26

    PubMed  CAS  Google Scholar 

  132. Kuntz ID, Blaney JM, Oatley SJ, Langridge R, Ferrin TE (1982) A geometric approach to macromolecule-ligand interactions. J Mol Biol 161:269–288

    PubMed  CAS  Google Scholar 

  133. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489

    PubMed  CAS  Google Scholar 

  134. Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, Pollard WT, Banks JL (2004) Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. J Med Chem 47:1750–1759

    PubMed  CAS  Google Scholar 

  135. Welch W, Ruppert J, Jain AN (1996) Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chem Biol 3:449–462

    PubMed  CAS  Google Scholar 

  136. Junmei Wang TH, Chen L, Xiaojie Xu (1999) Conformational analysis of peptides using Monte Carlo simulations combined with the genetic algorithm. Chemom Intell Lab Syst 45:5

    Google Scholar 

  137. Goodsell DS, Olson AJ (1990) Automated docking of substrates to proteins by simulated annealing. Proteins 8:195–202

    PubMed  CAS  Google Scholar 

  138. Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267:727–748

    PubMed  CAS  Google Scholar 

  139. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949

    PubMed  CAS  Google Scholar 

  140. Verkhivker GM, Bouzida D, Gehlhaar DK, Rejto PA, Arthurs S, Colson AB, Freer ST, Larson V, Luty BA, Marrone T, Rose PW (2000) Deciphering common failures in molecular docking of ligand-protein complexes. J Comput Aided Mol Des 14:731–751

    PubMed  CAS  Google Scholar 

  141. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM Jr, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J Am Chem Soc 117:5179–5197

    CAS  Google Scholar 

  142. Halgren T (1996) Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J Comput Chem 29:490–519

    Google Scholar 

  143. Clark M, Crammer RD, Van Opdenbosch N (1989) Validation of the general purpose tripos 5.2 force field. J Comput Chem 10:30

    Google Scholar 

  144. Bohm HJ (1992) LUDI: rule-based automatic design of new substituents for enzyme inhibitor leads. J Comput Aided Mol Des 6:593–606

    PubMed  CAS  Google Scholar 

  145. Eldridge MD, Murray CW, Auton TR, Paolini GV, Mee RP (1997) Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes. J Comput Aided Mol Des 11:425–445

    PubMed  CAS  Google Scholar 

  146. Gohlke H, Hendlich M, Klebe G (2000) Knowledge-based scoring function to predict protein-ligand interactions. J Mol Biol 295:337–356

    PubMed  CAS  Google Scholar 

  147. Muegge I, Martin YC (1999) A general and fast scoring function for protein-ligand interactions: a simplified potential approach. J Med Chem 42:791–804

    PubMed  CAS  Google Scholar 

  148. DeWitte RS, Shakhnovich EI (1996) SMoG: de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidence. J Am Chem Soc 118:11

    Google Scholar 

  149. Charifson PS, Corkery JJ, Murcko MA, Walters WP (1999) Consensus scoring: a method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J Med Chem 42:5100–5109

    PubMed  CAS  Google Scholar 

  150. Stahura FL, Bajorath J (2004) Virtual screening methods that complement HTS. Comb Chem High Throughput Screen 7:259–269

    PubMed  CAS  Google Scholar 

  151. Shoichet BK (2004) Virtual screening of chemical libraries. Nature 432:862–865

    PubMed  CAS  Google Scholar 

  152. Oprea TI, Matter H (2004) Integrating virtual screening in lead discovery. Curr Opin Chem Biol 8:349–358

    PubMed  CAS  Google Scholar 

  153. Hansch C, Fujita T (1964) Rho-sigma-pi analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616–1626

    CAS  Google Scholar 

  154. Todeschini R, Consonni V (2000) Handbook of molecular descriptors, vol 11. Wiley-VCH, Weinheim

    Google Scholar 

  155. Gupta RR, Gifford EM, Liston T, Waller CL, Bunin B, Ekins S (2010) Using open source computational tools for predicting human metabolic stability and additional ADME/TOX properties. Drug Metab Dispos 38:2083–2090

    PubMed  CAS  Google Scholar 

  156. Ekins S, Williams AJ (2010) When pharmaceutical companies publish large datasets: an abundance of riches or fool’s gold? Drug Discov Today 15:812–815

    PubMed  Google Scholar 

  157. Zhang L, Zhu H, Oprea TI, Golbraikh A, Tropsha A (2008) QSAR modeling of the blood-brain barrier permeability for diverse organic compounds. Pharm Res 25:1902–1914

    PubMed  CAS  Google Scholar 

  158. Kortagere S, Chekmarev DS, Welsh WJ, Ekins S (2008) New predictive models for blood brain barrier permeability of drug-like molecules. Pharm Res 25:1836–1845

    PubMed  CAS  Google Scholar 

  159. Kortagere S, Chekmarev D, Welsh WJ, Ekins S (2009) Hybrid scoring and classification approaches to predict human pregane X receptor activiators. Pharm Res 26:1001–1011

    PubMed  CAS  Google Scholar 

  160. Chekmarev D, Kholodovych V, Kortagere S, Welsh WJ, Ekins S (2009) Predicting inhibitors of acetylcholinesterase by regression and ClassificationMachine learning approaches with combinations of molecular descriptors. Pharm Res 26:2216–2224

    PubMed  CAS  Google Scholar 

  161. Torrens F (2003) Structural, chemical topological, electrotopological and electronic structure hypotheses. Comb Chem High Throughput Screen 6:801–809

    PubMed  CAS  Google Scholar 

  162. Polanski J (2009) Receptor dependent multidimensional QSAR for modeling drug–­receptor interactions. Curr Med Chem 16:3243–3257

    PubMed  CAS  Google Scholar 

  163. Consonni V, Todeschini R, Pavan M (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42:682–692

    PubMed  CAS  Google Scholar 

  164. Cramer RD, Wendt B (2007) Pushing the boundaries of 3D-QSAR. J Comput Aided Mol Des 21:23–32

    PubMed  CAS  Google Scholar 

  165. Cruciani G, Pastor M, Guba W (2000) VolSurf: a new tool for the pharmacokinetic optimization of lead compounds. Eur J Pharm Sci 11(Suppl 2):S29–S39

    PubMed  CAS  Google Scholar 

  166. Tetko IV, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P, Palyulin VA, Radchenko EV, Zefirov NS, Makarenko AS, Tanchuk VY, Prokopenko VV (2005) Virtual computational chemistry laboratory–design and description. J Comput Aided Mol Des 19:453–463

    PubMed  CAS  Google Scholar 

  167. Vilar S, Cozza G, Moro S (2008) Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Curr Top Med Chem 8:1555–1572

    PubMed  CAS  Google Scholar 

  168. Hong H, Xie Q, Ge W, Qian F, Fang H, Shi L, Su Z, Perkins R, Tong W (2008) Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model 48:1337–1344

    PubMed  CAS  Google Scholar 

  169. Wang N, DeLisle RK, Diller DJ (2005) Fast small molecule similarity searching with multiple alignment profiles of molecules represented in one-dimension. J Med Chem 48:6980–6990

    PubMed  CAS  Google Scholar 

  170. Cheng A, Diller DJ, Dixon SL, Egan WJ, Lauri G, Merz KMJ Jr (2002) Computation of the physico-chemical properties and data mining of large molecular collections. J Comput Chem 23:172–183

    PubMed  CAS  Google Scholar 

  171. McGaughey GB, Sheridan RP, Bayly CI, Culberson JC, Kreatsoulas C, Lindsley S, Maiorov V, Truchon JF, Cornell WD (2007) Comparison of topological, shape, and docking methods in virtual screening. J Chem Inf Model 47:1504–1519

    PubMed  CAS  Google Scholar 

  172. Wang J, Hou T, Xu X (2009) Aqueous solubility prediction based on weighted atom type counts and solvent accessible surface areas. J Chem Inf Model 49:571–581

    PubMed  CAS  Google Scholar 

  173. Hou TJ, Zhang W, Xia K, Qiao XB, Xu XJ (2004) ADME evaluation in drug discovery. 5. Correlation of caco-2 permeation with simple molecular properties. J Chem Inf Comput Sci 44:1585–1600

    PubMed  CAS  Google Scholar 

  174. Jorgensen WL, Duffy EM (2002) Prediction of drug solubility from structure. Adv Drug Deliv Rev 54:355–366

    PubMed  CAS  Google Scholar 

  175. Delaney JS (2005) Predicting aqueous solubility from structure. Drug Discov Today 10:289–295

    PubMed  CAS  Google Scholar 

  176. Wang J, Hou T (2009) Recent advances on in silico ADME modeling. Annu Rep Comput Chem 5:101–127

    Google Scholar 

  177. Ivanenkov YA, Savchuk NP, Ekins S, Balakin KV (2009) Computational mapping tools for drug discovery. Drug Discov Today 14:767–775

    PubMed  CAS  Google Scholar 

  178. Christianini N, Shawe-Taylor J (2000) Support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge, MA

    Google Scholar 

  179. Burbidge R, Trotter M, Buxton B, Holden S (2001) Drug design by machine learning: support vector machines for pharmaceutical analysis. Comput Chem 26:5–14

    PubMed  CAS  Google Scholar 

  180. Zernov VV, Balakin KV, Ivashchenko AA, Savchuk NP, Pletnev IV (2003) Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J Chem Inf Comput Sci 43:2048–2056

    PubMed  CAS  Google Scholar 

  181. Fernandez M, Caballero J, Fernandez L, Sarai A (2010) Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM). Mol Divers 2011(15):269–289

    Google Scholar 

  182. Rogers D, Brown RD, Hahn M (2005) Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J Biomol Screen 10:682–686

    PubMed  CAS  Google Scholar 

  183. Chekmarev DS, Kholodovych V, Balakin KV, Ivanenkov Y, Ekins S, Welsh WJ (2008) Shape signatures: new descriptors for predicting cardiotoxicity in silico. Chem Res Toxicol 21:1304–1314

    PubMed  CAS  Google Scholar 

  184. Ivanenkov YA, Savchuk NP, Ekins S, Balakin KV (2009) Computational mapping tools for drug discovery. Drug Discov Today 14:765–775

    Google Scholar 

  185. Tropsha A, Golbraikh A (2007) Predictive QSAR modeling workflow, model applicability domains, and virtual screening. Curr Pharm Des 13:3494–3504

    PubMed  CAS  Google Scholar 

  186. Abshear T, Banik GM, D’Souza ML, Nedwed K, Peng C (2006) A model validation and consensus building environment. SAR QSAR Environ Res 17:311–321

    PubMed  CAS  Google Scholar 

  187. Gavaghan CL, Arnby CH, Blomberg N, Strandlund G, Boyer S (2007) Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data. J Comput Aided Mol Des 21:189–206

    PubMed  CAS  Google Scholar 

  188. Tetko IV, Bruneau P, Mewes HW, Rohrer DC, Poda GI (2006) Can we estimate the accuracy of ADME-Tox predictions? Drug Discov Today 11:700–707

    PubMed  CAS  Google Scholar 

  189. Dimitrov S, Dimitrova G, Pavlov T, Dimitrova N, Patlewicz G, Niemela J, Mekenyan O (2005) A stepwise approach for defining the applicability domain of SAR and QSAR models. J Chem Inf Model 45:839–849

    PubMed  CAS  Google Scholar 

  190. Dearden JC, Cronin MT, Kaiser KL (2009) How not to develop a quantitative structure-activity or structure-property relationship (QSAR/QSPR). SAR QSAR Environ Res 20:241–266

    PubMed  CAS  Google Scholar 

  191. Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, Oberg T, Todeschini R, Fourches D, Varnek A (2008) Critical assessment of QSAR models of environmental toxicity against tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J Chem Inf Model 48:1733–1746

    PubMed  CAS  Google Scholar 

  192. Ehrlich P (1909) Present status of chemotherapy. Berl Dtsch Chem Ges 42:17–47

    CAS  Google Scholar 

  193. Guner OF (ed) (2000) Pharmacophore, perception, development, and use in drug design. University International Line, San Diego, CA

    Google Scholar 

  194. Langer T, Hoffman RD (2006) Pharmacophores and pharmacophore searches. Wiley-VCH, Weinheim

    Google Scholar 

  195. Martin YC (1992) 3D Database searching in drug design. J Med Chem 35:2145–2154

    PubMed  CAS  Google Scholar 

  196. Martin YC, Bures MG, Danaher EA, DeLazzer J, Lico I, Pavlik PA (1993) A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. J Comput Aided Mol Des 7:83–102

    PubMed  CAS  Google Scholar 

  197. Kurogi Y, Guner OF (2001) Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem 8:1035–1055

    PubMed  CAS  Google Scholar 

  198. Guner OF (2002) History and evolution of the pharmacophore concept in computer-aided drug design. Curr Top Med Chem 2:1269–1277

    Google Scholar 

  199. Guner O, Clement O, Kurogi Y (2004) Pharmacophore modeling and three dimensional database searching for drug design using catalyst: recent advances. Curr Med Chem 11:2991–3005

    PubMed  Google Scholar 

  200. Chang C, Ekins S, Bahadduri P, Swaan PW (2006) Pharmacophore-based discovery of ligands for drug transporters. Adv Drug Deliv Rev 58:1431–1450

    PubMed  CAS  Google Scholar 

  201. Barnum D, Greene J, Smellie A, Sprague P (1996) Identification of common functional configurations among molecules. J Chem Inf Comput Sci 36:563–571

    PubMed  CAS  Google Scholar 

  202. Sprague PW (1995) Automated chemical hypothesis generation and database searching with catalyst. Perspect Drug Discov Des 3:1–20

    CAS  Google Scholar 

  203. Sprague PW, Hoffman R (1997) CATALYST pharmacophore models and their utility as queries for searching 3D databases. In: van de Waterbeemd H, Testa B, Folkers G (eds) Computer-assisted lead finding and optimization. Verlag Helvetica Chimica Acta, Basel, pp 225–240

    Google Scholar 

  204. Kaminski JJ, Rane DF, Snow ME, Weber L, Rothofsky ML, Anderson SD, Lin SL (1997) Identification of novel farnesyl protein transferase inhibitors using three-dimensional searching methods. J Med Chem 40:4103–4112

    PubMed  CAS  Google Scholar 

  205. Wang S, Zaharevitz DW, Sharma R, Marquez VE, Lewin NE, Du L, Blumberg PM, Milne GWA (1994) The discovery of novel, structurally diverse protein kinase C agonists through computer 3D-database pharmacophore search. Molecular modeling studies. J Med Chem 37:4479–4489

    PubMed  CAS  Google Scholar 

  206. Nicklaus MC, Neamati N, Hong H, Mazumder A, Sunder S, Chen J, Milne GWA, Pommier Y (1997) HIV-1 integrase pharmacophore: discovery of inhibitors through three-dimensional database searching. J Med Chem 40:920–929

    PubMed  CAS  Google Scholar 

  207. Chang C, Swaan PW (2006) Computational approaches to modeling drug transporters. Eur J Pharm Sci 27:411–424

    PubMed  CAS  Google Scholar 

  208. Jones G, Willett P, Glen RC (1995) A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J Comput Aided Mol Des 9:532–549

    PubMed  CAS  Google Scholar 

  209. Patel Y, Gillet VJ, Bravi G, Leach AR (2002) A comparison of the pharmacophore identification programs: catalyst. DISCO and GASP. J Comput Aided Mol Des 16:653–681

    PubMed  CAS  Google Scholar 

  210. Clement OA, Mehi AT (2000) HipHop: pharmacophore based on multiple common-feature alignments. IUL, San Diego, CA

    Google Scholar 

  211. Evans DA, Doman TN, Thorner DA, Bodkin MJ (2007) 3D QSAR methods: phase and catalyst compared. J Chem Inf Model 47:1248–1257

    PubMed  CAS  Google Scholar 

  212. Ekins S, Johnston JS, Bahadduri P, D’Souza VM, Ray A, Chang C, Swaan PW (2005) In vitro and pharmacophore-based discovery of novel hPEPT1 inhibitors. Pharm Res 22:512–517

    PubMed  CAS  Google Scholar 

  213. Chang C, Bahadduri PM, Polli JE, Swaan PW, Ekins S (2006) Rapid identification of P-glycoprotein substrates and inhibitors. Drug Metab Dispos 34:1976–1984

    PubMed  CAS  Google Scholar 

  214. Ekins S, Kim RB, Leake BF, Dantzig AH, Schuetz EG, Lan LB, Yasuda K, Shepard RL, Winter MA, Schuetz JD, Wikel JH, Wrighton SA (2002) Application of three-dimensional quantitative structure-activity relationships of P-glycoprotein inhibitors and substrates. Mol Pharmacol 61:974–981

    PubMed  CAS  Google Scholar 

  215. Ekins S, Kim RB, Leake BF, Dantzig AH, Schuetz EG, Lan LB, Yasuda K, Shepard RL, Winter MA, Schuetz JD, Wikel JH, Wrighton SA (2002) Three-dimensional quantitative structure-activity relationships of inhibitors of P-glycoprotein. Mol Pharmacol 61:964–973

    PubMed  CAS  Google Scholar 

  216. Bednarczyk D, Ekins S, Wikel JH, Wright SH (2003) Influence of molecular structure on substrate binding to the human organic cation transporter, hOCT1. Mol Pharmacol 63:489–498

    PubMed  CAS  Google Scholar 

  217. Chang C, Pang KS, Swaan PW, Ekins S (2005) Comparative pharmacophore modeling of organic anion transporting polypeptides: a meta-analysis of rat Oatp1a1 and human OATP1B1. J Pharmacol Exp Ther 314:533–541

    PubMed  CAS  Google Scholar 

  218. Suhre WM, Ekins S, Chang C, Swaan PW, Wright SH (2005) Molecular determinants of substrate/inhibitor binding to the human and rabbit renal organic cation transporters hOCT2 and rbOCT2. Mol Pharmacol 67:1067–1077

    PubMed  CAS  Google Scholar 

  219. Ekins S, Swaan PW (2004) Computational models for enzymes, transporters, channels and receptors relevant to ADME/TOX. Rev Comp Chem 20:333–415

    CAS  Google Scholar 

  220. Bahadduri PM, Polli JE, Swaan PW, Ekins S (2010) Targeting drug transporters - ­combining in silico and in vitro approaches to predict in vivo. Methods Mol Biol 637:65–103

    PubMed  CAS  Google Scholar 

  221. Ekins S, Ecker GF, Chiba P, Swaan PW (2007) Future directions for drug transporter modeling. Xenobiotica 37:1152–1170

    PubMed  CAS  Google Scholar 

  222. Diao L, Ekins S, Polli JE (2010) Quantitative structure activity relationship for inhibition of human organic cation/carnitine transporter. Mol Pharm 7:2120–2131

    PubMed  CAS  Google Scholar 

  223. Zheng X, Ekins S, Rauffman J-P, Polli JE (2009) Computational models for drug inhibition of the human apical sodium-dependent bile acid transporter. Mol Pharm 6:1591–1603

    PubMed  CAS  Google Scholar 

  224. Diao L, Ekins S, Polli JE (2009) Novel inhibitors of human organic cation/carnitine transporter (hOCTN2) via computational modeling and in vitro testing. Pharm Res 26:1890–1900

    PubMed  CAS  Google Scholar 

  225. Irwin JJ, Shoichet BK (2005) ZINC–a free database of commercially available compounds for virtual screening. J Chem Inf Model 45:177–182

    PubMed  CAS  Google Scholar 

  226. Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 46:3–26

    PubMed  CAS  Google Scholar 

  227. Ekins S, Waller CL, Swaan PW, Cruciani G, Wrighton SA, Wikel JH (2000) Progress in predicting human ADME parameters in silico. J Pharmacol Toxicol Methods 44:251–272

    PubMed  CAS  Google Scholar 

  228. van de Waterbeemd H, Gifford E (2003) ADMET in silico modelling: towards prediction paradise? Nat Rev Drug Discov 2:192–204

    PubMed  Google Scholar 

  229. Aronov AM, Balakin KV, Kiselyov A, Varma-O’Brien S, Ekins S (2007) Applications of QSAR methods to ion channels. In: Ekins S (ed) Computational toxicology: risk assessment for pharmaceutical and environmental chemicals. Wiley, Hoboken, NJ, pp 353–389

    Google Scholar 

  230. Kortagere S, Krasowski MD, Reschly EJ, Venkatesh M, Mani S, Ekins S (2010) Evaluation of computational docking to identify PXR agonists in the ToxCastTM database. Environ Health Perspect 118:1412–1417

    PubMed  CAS  Google Scholar 

  231. Jolivette LJ, Ekins S (2007) Methods for predicting human drug metabolism. Adv Clin Chem 43:131–176

    PubMed  CAS  Google Scholar 

  232. Ekins S, Gupta RR, Gifford E, Bunin BA, Waller CL (2010) Chemical space: missing pieces in cheminformatics. Pharm Res 27:2035–2039

    PubMed  CAS  Google Scholar 

  233. Stewart KD, Shiroda M, James CA (2006) Drug guru: a computer software program for drug design using medicinal chemistry rules. Bioorg Med Chem 14:7011–7022

    PubMed  CAS  Google Scholar 

  234. Metz JT, Huth JR, Hajduk PJ (2007) Enhancement of chemical rules for predicting compound reactivity towards protein thiol groups. J Comput Aided Mol Des 21:139–144

    PubMed  CAS  Google Scholar 

  235. Gillet VJ, Khatib W, Willett P, Fleming PJ, Green DV (2002) Combinatorial library design using a multiobjective genetic algorithm. J Chem Inf Comput Sci 42: 375–385

    PubMed  CAS  Google Scholar 

  236. Ekins S, Honeycutt JD, Metz JT (2010) Evolving molecules using multi-objective optimization: applying to ADME. Drug Discov Today 15:451–460

    PubMed  CAS  Google Scholar 

  237. Krieger E, Koraimann G, Vriend G (2002) Increasing the precision of comparative models with YASARA NOVA-a self-parameterizing force field. Proteins 47:393–402

    PubMed  CAS  Google Scholar 

  238. Volkman BF, Alam SL, Satterlee JD, Markley JL (1998) Solution structure and backbone dynamics of component IV glycera dibranchiata monomeric hemoglobin-CO. Biochemistry 37:10906–10919

    PubMed  CAS  Google Scholar 

  239. Ionel A, Velazquez-Muriel JA, Luque D, Cuervo A, Caston JR, Valpuesta JM, Martin-Benito J, Carrascosa JL (2010) Molecular rearrangements involved in the capsid shell maturation of bacteriophage T7. J Biol Chem 286:234–242

    PubMed  Google Scholar 

  240. Jmol: an open-source Java viewer for chemical structures in 3D, v.

    Google Scholar 

Download references

Acknowledgment

We would like to thank Dr. Ronald Preez for generating figure 12.

Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Drexel University, Philadelphia, PA, USA

    Ahmet Sacan

  2. Collaborations in Chemistry, Jenkintown, PA, USA

    Sean Ekins

  3. Department of Pharmaceutical Sciences, University of Maryland, College Park, MD, USA

    Sean Ekins

  4. Department of Pharmacology, University of Medicine and Dentistry of New Jersey (UMDNJ)-Robert Wood Johnson Medical School, Piscataway, NJ, USA

    Sean Ekins

  5. Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, PA, USA

    Sandhya Kortagere

Authors
  1. Ahmet Sacan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sean Ekins
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sandhya Kortagere
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sandhya Kortagere .

Editor information

Editors and Affiliations

  1. , Department of Pathology, The University of New Mexico, Camino de Salud 915, Albuquerque, 87131, New Mexico, USA

    Richard S. Larson

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media New York

About this protocol

Cite this protocol

Sacan, A., Ekins, S., Kortagere, S. (2012). Applications and Limitations of In Silico Models in Drug Discovery. In: Larson, R. (eds) Bioinformatics and Drug Discovery. Methods in Molecular Biology, vol 910. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-61779-965-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-61779-965-5_6

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-61779-964-8

  • Online ISBN: 978-1-61779-965-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation