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Lead Optimization in Drug Discovery

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Research Topics in Bioactivity, Environment and Energy

Part of the book series: Engineering Materials ((ENG.MAT.))

Abstract

The Discovery of a drug with pharmaceutical actions goes through several stages, such as Hit to Lead and Lead Optimization. Hit to Lead comprises the phase in which small molecules are evaluated about their activity and their interaction with the target to generate lead compounds. Data analysis such as potency, selectivity, and other physicochemical properties play an important role in this step, as they form the basis for optimizing the next leads. The final stage of drug discovery is called Lead Optimization, whose function is to maintain or improve the desired properties present in selected compounds and, at the same time, reduce any deficiencies found in their structure. Studies of modifications in compounds for improvement can be carried out in experimental ways such as magnetic resonance and mass spectrometry or also by computational methods. Computational methods used in this phase include pharmacophore studies, molecular docking, molecular dynamics, QSAR, among others. This chapter reports the computational techniques used for the lead optimization stage to present which paths can be followed and used for the rational discovery of new drugs.

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References

  1. Deprez-Poulain, R., Deprez, B.: Facts, figures, and trends in lead generation. Curr. Top. Med. Chem. 4(6), 569–580 (2004). https://doi.org/10.2174/1568026043451168

    Article  CAS  Google Scholar 

  2. Hughes, J., Rees, S., Kalindjian, S., Philpott, K.: Principles of early drug discovery. Br. J. Pharmacol. 162(6), 1239–1249 (2011). https://doi.org/10.1111/j.1476-5381.2010.01127.x

    Article  CAS  Google Scholar 

  3. Keserű, G.M., Makara, G.M.: Hit discovery and hit-to-lead approaches. Drug Discov. Today 11(15–16), 741–748 (2006). https://doi.org/10.1016/j.drudis.2006.06.016

    Article  Google Scholar 

  4. Bleicher, K.H., Böhm, H.-J., Müller, K., Alanine, A.I.: Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug. Discov. 2(5), 369–378 (2003). https://doi.org/10.1038/nrd1086

    Article  CAS  Google Scholar 

  5. Heifetz, A., James, T., Morao, I., Bodkin, M.J., Biggin, P.C.: Guiding lead optimization with GPCR structure modeling and molecular dynamics. Curr. Opin. Pharmacol. 30, 14–21 (2016). https://doi.org/10.1016/j.coph.2016.06.004

    Article  CAS  Google Scholar 

  6. Leelananda, S.P., Lindert, S.: Computational methods in drug discovery. Beilstein. J. Org. Chem. 12, 2694–2718 (2016). https://doi.org/10.3762/bjoc.12.267

    Article  CAS  Google Scholar 

  7. de Souza Neto, L.R., Moreira-Filho, J.T., Neves, B.J., et al.: In silico strategies to support fragment-to-lead optimization in drug discovery. Front. Chem. 8(February), 1–18 (2020). https://doi.org/10.3389/fchem.2020.00093

    Article  CAS  Google Scholar 

  8. Thomas, S.E., Mendes, V., Kim, S.Y., et al.: Structural biology and the design of new therapeutics: from HIV and cancer to mycobacterial infections: a paper dedicated to John Kendrew. J. Mol. Biol. 429(17), 2677–2693 (2017). https://doi.org/10.1016/j.jmb.2017.06.014

    Article  CAS  Google Scholar 

  9. Patel, D.D., Bauman, J., Arnold, E.: Advantages of crystallographic fragment screening: functional and mechanistic insights from a powerful platform for efficient drug discovery. Prog. Biophys. Mol. Biol. 116, 92–100 (2014)

    Google Scholar 

  10. Fischer, M., Hubbard, R.: Fragment-based ligand discovery. Mol. Interv. 9(1), 22–30 (2009)

    Article  CAS  Google Scholar 

  11. Ichihara, O., Barker, J., Law, R.J., Whittaker, M.: Compound design by fragment-linking. Mol. Inform. 30(4), 298–306 (2011). https://doi.org/10.1002/minf.201000174

    Article  CAS  Google Scholar 

  12. De Fusco, C., Brear, P., Segre, J., et al.: A fragment-based approach leading to the discovery of a novel binding site and the selective CK2 inhibitor CAM4066. Bioorganic Med. Chem. 25(13), 3471–3482 (2017). https://doi.org/10.1016/j.bmc.2017.04.037

    Article  CAS  Google Scholar 

  13. Hall, D.R., Ngan, C.H., Zerbe, B.S., Kozakov, D., Vajda, S.: Hot spot analysis for driving the development of hits into leads in fragment-based drug discovery. J. Chem. Inf. Model. 52(1), 199–209 (2012). https://doi.org/10.1021/ci200468p

    Article  CAS  Google Scholar 

  14. Torres, P.H.M., Sodero, A.C.R., Jofily, P., Silva-Jr, F.P.: Key topics in molecular docking for drug design. Int. J. Mol. Sci. 20(18), 1–29 (2019). https://doi.org/10.3390/ijms20184574

    Article  CAS  Google Scholar 

  15. Durrant, J., Amaro, R.E., McCammon, J.A.: AutoGrow: a novel algorithm for protein inhibitor design. Chem. Biol. Drug Des. 73(2), 168–178 (2009)

    Article  CAS  Google Scholar 

  16. Durrant, J.D., Lindert, S., McCammon, J.A.: AutoGrow 3.0: an improved algorithm for chemically tractable, semi-automated protein inhibitor design. J. Mol. Graph Model 44, 104–112 (2013). https://doi.org/10.1016/j.jmgm.2013.05.006

  17. Dey, F., Caflisch, A.: Fragment-based de Novo Ligand design by multiobjective evolutionary optimization. J. Chem. Inf. Model. 48(3), 679–690 (2008). https://doi.org/10.1021/ci700424b

    Article  CAS  Google Scholar 

  18. Pierce, A.C., Rao, G., Bemis, G.W.: BREED: generating novel inhibitors through hybridization of known ligands. application to CDK2, P38, and HIV protease. J. Med. Chem. 47(11), 2768–2775 (2004).https://doi.org/10.1021/jm030543u

  19. Gund, P.: Evolution of the pharmacophore concept in pharmaceutical research. In: Güner, O.F., (ed.) Pharmacophore Perception, Development, and Use in Drug Design. International University Line, La Jolla (2000)

    Google Scholar 

  20. Gund, P.: Pharmacophoric pattern searching and receptor mapping. In: Hess H.-J. (ed.) Annual Reports in Medicinal Chemistry. Academic, vol. 14 (1979)

    Google Scholar 

  21. Gund, P., Wipke, W.T., Langridge, R.: Computer searching of a molecular structure file for pharmacophoric patterns. In: Proceedings of International Conference on Comp. in Chem. Res. and Edu. Elsevier (1974)

    Google Scholar 

  22. Güner, O.F., Hughes, D.W., Dumont, L.M.: An integrated approach to three-dimensional information management with MACCS-3D. J. Chem. Inf. Comput. Sci. 31(3), 408–414 (1991). https://doi.org/10.1021/ci00003a007

    Article  Google Scholar 

  23. Khedkar, S.A., Malde, A.K., Coutinho, E.C., Srivastava, S.: Pharmacophore modeling in drug discovery and development: an overview. Med. Chem. (Los Angeles). 3, 187–197 (2007)

    CAS  Google Scholar 

  24. Guner, O.: History and evolution of the pharmacophore concept in computer-aided drug design. Curr. Top. Med. Chem. 2(12), 1321–1332 (2005). https://doi.org/10.2174/1568026023392940

    Article  Google Scholar 

  25. Martin, Y.C.: Distance Comparisons (DISCO): a new strategy for examining 3d structure-activity relationships. In: Hansch, C., Fujita, T., (eds.) Classical and 3D QSAR in Agrochemistry. American Chemical Society, Washington D.C. (1995)

    Google Scholar 

  26. Jones, G., Willett, P., Glen, R.C.: A genetic algorithm for flexible molecular overlay and pharmacophore elucidation. J. Comput. Aided Mol. Des. 9(6), 532–549 (1995). https://doi.org/10.1007/BF00124324

    Article  CAS  Google Scholar 

  27. Barnum, D., Greene, J., Smellie, A., Sprague, P.: Identification of common functional configurations among molecules. J. Chem. Inf Comput. Sci. 36(3), 563–571 (1996). https://doi.org/10.1021/ci950273r

    Article  CAS  Google Scholar 

  28. Cramer, R.D., Patterson, D.E., Bunce, J.D.: Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110(18), 5959–5967 (1988)

    Google Scholar 

  29. Golender, V.E., Vorpagel, E.R.: Computer-assisted pharmacophore identification. In: Kubinyi, H (ed.) Drug Design: Theory. ESCOM, Leiden (1993)

    Google Scholar 

  30. Li, H., Sutter, J., Hoffmann, R.: HypoGen: an automated system fro generating 3D predictive pharmacophore models. In: Guner, O., (ed.) Pharmacophore Perception, Development, and Use in Drug Design. IUL Biotechnology Series, La Jolla (2000)

    Google Scholar 

  31. Yang, S.Y.: Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discov. Today 15(11–12), 444–450 (2010). https://doi.org/10.1016/j.drudis.2010.03.013

    Article  CAS  Google Scholar 

  32. Wolber, G., Langer, T.: LigandScout: 3-D pharmacophores derived from protein-bound ligands and their use as virtual screening filters. J. Chem. Inf. Model. 45(1), 160–169 (2005). https://doi.org/10.1021/ci049885e

    Article  CAS  Google Scholar 

  33. Chen, J., Lai, L.: Pocket vol 2: further developments on receptor-based pharmacophore modeling. J. Chem. Inf Model. 46(6), 2684–2691 (2006). https://doi.org/10.1021/ci600246s

    Article  CAS  Google Scholar 

  34. Ortuso, F., Langer, T., Alcaro, S.: GBPM: GRID-based pharmacophore model: concept and application studies to protein-protein recognition. Bioinformatics 22(12), 1449–1455 (2006). https://doi.org/10.1093/bioinformatics/btl115

    Article  CAS  Google Scholar 

  35. 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. 48(7), 1396–1410 (2008). https://doi.org/10.1021/ci800064z

    Article  CAS  Google Scholar 

  36. Tintori, C., Corradi, V., Magnani, M., Manetti, F., Botta, M.: Targets looking for drugs: a multistep computational protocol for the development of structure-based pharmacophores and their applications for hit discovery. J. Chem. Inf. Model. 48(11), 2166–2179 (2008). https://doi.org/10.1021/ci800105p

    Article  CAS  Google Scholar 

  37. Burger, A.: Medicinal Chemistry, 3d edn. Wiley-Interscience, New York (1970)

    Google Scholar 

  38. Todeschini, R., Consonni, V.: Molecular descriptors for chemoinformatics. In: Methods and Principles in Medicinal Chemistry. Wiley-VCH, Weinheim, Germany (2009)

    Google Scholar 

  39. Devereux, M., LA Popelier, P.: In silico techniques for the identification of bioisosteric replacements for drug design. Curr. Top. Med. Chem. 10(6), 657–668 (2010). https://doi.org/10.2174/156802610791111470

  40. Ujváry, I.: BIOSTER—A Database of Structurally Analogous Compounds, vol 46, pp. 92–95 (1995)

    Google Scholar 

  41. Gaulton, A., Hersey, A., Nowotka, M.L., et al.: The ChEMBL database in 2017. Nucleic Acids Res. 45(D1), D945–D954 (2017). https://doi.org/10.1093/nar/gkw1074

    Article  CAS  Google Scholar 

  42. Wirth, M., Zoete, V., Michielin, O., Sauer, W.H.B.: SwissBioisostere: a database of molecular replacements for ligand design. Nucleic Acids Res. 41(D1), 1137–1143 (2013). https://doi.org/10.1093/nar/gks1059

    Article  CAS  Google Scholar 

  43. Lengauer, T., Rarey, M.: Computational methods for biomolecular docking. Curr. Opin. Struct. Biol. 6(3), 402–406 (1996). https://doi.org/10.1016/S0959-440X(96)80061-3

    Article  CAS  Google Scholar 

  44. Kitchen, D.B., Decornez, H., Furr, J.R., Bajorath, J.: Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 3(11), 935–949 (2004). https://doi.org/10.1038/nrd1549

    Article  CAS  Google Scholar 

  45. Mostashari-Rad, T., Arian, R., Sadri, H., et al.: Study of CXCR4 chemokine receptor inhibitors using QSPR and molecular docking methodologies. J. Theor. Comput. Chem. 18(04), 1950018 (2019). https://doi.org/10.1142/S0219633619500184

    Article  CAS  Google Scholar 

  46. Berman, H.M., Battistuz, T., Bhat, T.N., et al.: The protein data bank. Acta Crystallogr Sect. D Biol. Crystallogr. 58(6), 899–907 (2002). https://doi.org/10.1107/S0907444902003451

    Article  CAS  Google Scholar 

  47. Berman, H., Henrick, K., Nakamura, H., Markley, J.L.: The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data. Nucleic Acids Res. 35(Database), D301–D303 (2007). https://doi.org/10.1093/nar/gkl971

  48. Wang, R., Fang, X., Lu, Y., Yang, C.-Y., Wang, S.: The PDBbind database: methodologies and updates. J. Med. Chem. 48(12), 4111–4119 (2005). https://doi.org/10.1021/jm048957q

    Article  CAS  Google Scholar 

  49. Wang, R., Fang, X., Lu, Y., Wang, S.: The PDBbind database: collection of binding affinities for protein−ligand complexes with known three-dimensional structures. J. Med. Chem. 47(12), 2977–2980 (2004). https://doi.org/10.1021/jm030580l

    Article  CAS  Google Scholar 

  50. Liu, T., Lin, Y., Wen, X., Jorissen, R.N., Gilson, M.K.: BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 35(suppl 1), D198–D201 (2007). https://doi.org/10.1093/nar/gkl999

    Article  CAS  Google Scholar 

  51. Chothia, C., Lesk, A.M.: The relation between the divergence of sequence and structure in proteins. EMBO J. 5(4), 823–826 (1986)

    Article  CAS  Google Scholar 

  52. Wang, G., Zhu, W.: Molecular docking for drug discovery and development: a widely used approach but far from perfect. Future Med. Chem. 8(14) (2016). https://doi.org/10.4155/fmc-2016-0143

  53. 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. 47(1), 45–55 (2004). https://doi.org/10.1021/jm030209y

    Article  CAS  Google Scholar 

  54. Lexa, K.W., Carlson, H.A.: Protein flexibility in docking and surface mapping. Q. Rev. Biophys. 45(3), 301–343 (2012). https://doi.org/10.1017/S0033583512000066

    Article  CAS  Google Scholar 

  55. Meng, X.-Y., Zhang, H.-X., Mezei, M., Cui, M.: Molecular docking: a powerful approach for structure-based drug discovery. Curr. Comput. Aided-Drug Des. 7(2), 146–157 (2011). https://doi.org/10.2174/157340911795677602

    Article  CAS  Google Scholar 

  56. Dias, R., de Azevedo Jr. W.: Molecular docking algorithms. Curr. Drug Targets. 9(12), 1040–1047 (2008). https://doi.org/10.2174/138945008786949432

    Article  CAS  Google Scholar 

  57. Rarey, M., Kramer, B., Lengauer, T., Klebe, G.: A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol. 261(3), 470–489 (1996). https://doi.org/10.1006/jmbi.1996.0477

    Article  CAS  Google Scholar 

  58. Kramer, B., Rarey, M., Lengauer, T.: Evaluation of the FlexX incremental construction algorithm for protein-ligand docking. Proteins Struct. Funct. Genet. 37(2), 228–241 (1999). https://doi.org/10.1002/(SICI)1097-0134(19991101)37:2%3c228::AID-PROT8%3e3.0.CO;2-8

    Article  CAS  Google Scholar 

  59. Morris, G.M., Goodsell, D.S., Halliday, R.S., et al.: Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J. Comput. Chem. 19(14), 1639–1662 (1998). https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14%3c1639::AID-JCC10%3e3.0.CO;2-B

    Article  CAS  Google Scholar 

  60. Jones, G., Willett, P., Glen, R.C., Leach, A.R., Taylor, R.: Development and validation of a genetic algorithm for flexible docking 1 1Edited by F. E. Cohen. J. Mol. Biol. 267(3), 727–748 (1997). https://doi.org/10.1006/jmbi.1996.0897

  61. Liu, M., Wang, S.: MCDOCK: a Monte Carlo simulation approach to the molecular docking problem. J. Comput. Aided Mol. Des. 13, 435–451 (1999)

    Article  CAS  Google Scholar 

  62. Graves, A.P., Brenk, R., Shoichet, B.K.: Decoys for docking. J. Med. Chem. 48(11), 3714–3728 (2005). https://doi.org/10.1021/jm0491187

    Article  CAS  Google Scholar 

  63. Betzi, S., Suhre, K., Chétrit, B., Guerlesquin, F., Morelli, X.: GFscore: a general nonlinear consensus scoring function for high-throughput docking. J. Chem. Inf. Model 46(4), 1704–1712 (2006). https://doi.org/10.1021/ci0600758

    Article  CAS  Google Scholar 

  64. Yang, C.-Y., Wang, R., Wang, S.: M-Score: a knowledge-based potential scoring function accounting for protein atom mobility. J. Med. Chem. 49(20), 5903–5911 (2006). https://doi.org/10.1021/jm050043w

    Article  CAS  Google Scholar 

  65. Krammer, A., Kirchhoff, P.D., Jiang, X., Venkatachalam, C.M., Waldman, M.: LigScore: a novel scoring function for predicting binding affinities. J. Mol. Graph Model 23(5), 395–407 (2005). https://doi.org/10.1016/j.jmgm.2004.11.007

    Article  CAS  Google Scholar 

  66. Velec, H.F.G., Gohlke, H., Klebe, G.: DrugScore CSD knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction. J. Med. Chem. 48(20), 6296–6303 (2005). https://doi.org/10.1021/jm050436v

    Article  CAS  Google Scholar 

  67. Wang, R., Lu, Y., Fang, X., Wang, S.: An extensive test of 14 scoring functions using the pdbbind refined set of 800 protein−ligand complexes. J. Chem. Inf. Comput. Sci. 44(6), 2114–2125 (2004). https://doi.org/10.1021/ci049733j

    Article  CAS  Google Scholar 

  68. Wang, R., Lu, Y., Wang, S.: Comparative evaluation of 11 scoring functions for molecular docking. J. Med. Chem. 46(12), 2287–2303 (2003). https://doi.org/10.1021/jm0203783

    Article  CAS  Google Scholar 

  69. Reddy, M., Reddy, C., Rathore, R., et al.: Free energy calculations to estimate ligand-binding affinities in structure-based drug design. Curr. Pharm. Des. 20(20), 3323–3337 (2014). https://doi.org/10.2174/13816128113199990604

    Article  CAS  Google Scholar 

  70. Yuriev, E., Agostino, M., Ramsland, P.A.: Challenges and advances in computational docking: 2009 in review. J. Mol. Recognit. 24(2), 149–164 (2011). https://doi.org/10.1002/jmr.1077

    Article  CAS  Google Scholar 

  71. Enyedy, I.J., Egan, W.J.: Can we use docking and scoring for hit-to-lead optimization? J. Comput. Aided Mol. Des. 22(3–4), 161–168 (2008). https://doi.org/10.1007/s10822-007-9165-4

    Article  CAS  Google Scholar 

  72. Vangrevelinghe, E., Zimmermann, K., Schoepfer, J., Portmann, R., Fabbro, D., Furet, P.: Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking. J. Med. Chem. 46(13), 2656–2662 (2003). https://doi.org/10.1021/jm030827e

    Article  CAS  Google Scholar 

  73. Bytheway, I., Cochran, S.: Validation of molecular docking calculations involving FGF-1 and FGF-2. J. Med. Chem. 47(7), 1683–1693 (2004). https://doi.org/10.1021/jm030447t

    Article  CAS  Google Scholar 

  74. Friesner, R.A., Murphy, R.B., Repasky, M.P., et al.: Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes. J. Med. Chem. 49(21), 6177–6196 (2006). https://doi.org/10.1021/jm051256o

    Article  CAS  Google Scholar 

  75. Durrant, J.D., McCammon, J.A.: Computer-aided drug-discovery techniques that account for receptor flexibility. Curr. Opin. Pharmacol. 10(6), 770–774 (2010). https://doi.org/10.1016/j.coph.2010.09.001

    Article  CAS  Google Scholar 

  76. McCammon, J.A., Gelin, B.R., Karplus, M.: Dynamics of folded proteins. Nature 267(5612), 585–590 (1977). https://doi.org/10.1038/267585a0

    Article  CAS  Google Scholar 

  77. Salo-Ahen, O.M.H., Alanko, I., Bhadane, R., et al.: Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes. 9(1), 71 (2020). https://doi.org/10.3390/pr9010071

    Article  CAS  Google Scholar 

  78. van Gunsteren, W.F., Berendsen, H.J.C.: Computer simulation of molecular dynamics: methodology, applications, and perspectives in chemistry. Angew. Chemie Int. Ed. English. 29(9), 992–1023 (1990). https://doi.org/10.1002/anie.199009921

    Article  Google Scholar 

  79. Berman, H.M., Westbrook, J., Feng, Z., et al.: The protein data bank. Nucleic Acids Res. 28(1), 235–242 (2000). https://doi.org/10.1093/nar/28.1.235

    Article  CAS  Google Scholar 

  80. Kuhlman, B., Bradley, P.: Advances in protein structure prediction and design. Nat. Rev. Mol. Cell. Biol. 20(11), 681–697 (2019). https://doi.org/10.1038/s41580-019-0163-x

    Article  CAS  Google Scholar 

  81. Cornell, W.D., Cieplak, P., Bayly, C.I., et al.: A second generation force field for the simulation of proteins, nucleic acids, and organic molecules. J. Am. Chem. Soc. 117(19), 5179–5197 (1995). https://doi.org/10.1021/ja00124a002

    Article  CAS  Google Scholar 

  82. Jones, J.E.: On the determination of molecular fields.—II. From the equation of the state of a gas. Proc. R. Soc. Lond. Ser. A, Contain Pap. Math. Phys. Character. 106(738), 463–477 (1924). https://doi.org/10.1098/rspa.1924.0082

  83. Durrant, J.D., McCammon, J.A.: Molecular dynamics simulations and drug discovery. BMC Biol. 9(1), 71 (2011). https://doi.org/10.1186/1741-7007-9-71

    Article  CAS  Google Scholar 

  84. Wang, J., Wolf, R.M., Caldwell, J.W., Kollman, P.A., Case, D.A.: Development and testing of a general amber force field. J. Comput. Chem. 25(9), 1157–1174 (2004). https://doi.org/10.1002/jcc.20035

    Article  CAS  Google Scholar 

  85. 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. 4(2), 187–217 (1983). https://doi.org/10.1002/jcc.540040211

    Article  CAS  Google Scholar 

  86. Christen, M., Hünenberger, P.H., Bakowies, D., et al.: The GROMOS software for biomolecular simulation: GROMOS05. J. Comput. Chem. 26(16), 1719–1751 (2005). https://doi.org/10.1002/jcc.20303

    Article  CAS  Google Scholar 

  87. van Gunsteren, W.F., Dolenc, J., Mark, A.E.: Molecular simulation as an aid to experimentalists. Curr. Opin. Struct. Biol. 18(2), 149–153 (2008). https://doi.org/10.1016/j.sbi.2007.12.007

    Article  CAS  Google Scholar 

  88. LaConte, L.E.W., Voelz, V., Nelson, W., Enz, M., Thomas, D.D.: Molecular dynamics simulation of site-directed spin labeling: experimental validation in muscle fibers. Biophys. J. 83(4), 1854–1866 (2002). https://doi.org/10.1016/S0006-3495(02)73950-7

    Article  CAS  Google Scholar 

  89. Showalter, S.A., Brüschweiler, R.: Validation of molecular dynamics simulations of biomolecules using NMR spin relaxation as benchmarks: application to the AMBER99SB force field. J. Chem. Theory Comput. 3(3), 961–975 (2007). https://doi.org/10.1021/ct7000045

    Article  CAS  Google Scholar 

  90. Markwick, P.R.L., Cervantes, C.F., Abel, B.L., Komives, E.A., Blackledge, M., McCammon, J.A.: Enhanced conformational space sampling improves the prediction of chemical shifts in proteins. J. Am. Chem. Soc. 132(4), 1220–1221 (2010). https://doi.org/10.1021/ja9093692

    Article  CAS  Google Scholar 

  91. Chodera, J.D., Mobley, D.L., Shirts, M.R., Dixon, R.W., Branson, K., Pande, V.S.: Alchemical free energy methods for drug discovery: progress and challenges. Curr. Opin. Struct. Biol. 21(2), 150–160 (2011). https://doi.org/10.1016/j.sbi.2011.01.011

    Article  CAS  Google Scholar 

  92. Hong, G., Cornish, A.J., Hegg, E.L., Pachter, R.: On understanding proton transfer to the biocatalytic [Fe–Fe]H sub-cluster in [Fe–Fe]H2ases: QM/MM MD simulations. Biochim. Biophys. Acta Bioenerg. 1807(5), 510–517 (2011). https://doi.org/10.1016/j.bbabio.2011.01.011

    Article  CAS  Google Scholar 

  93. Schames, J.R., Henchman, R.H., Siegel, J.S., Sotriffer, C.A., Ni, H., McCammon, J.A.: Discovery of a novel binding trench in HIV integrase. J. Med. Chem. 47(8), 1879–1881 (2004). https://doi.org/10.1021/jm0341913

    Article  CAS  Google Scholar 

  94. Durrant, J.D., Keränen, H., Wilson, B.A., McCammon, J.A.: Computational identification of uncharacterized cruzain binding sites. Geary, T.G., (ed.) PLoS Negl. Trop. Dis. 4(5), e676 (2010). https://doi.org/10.1371/journal.and.0000676

  95. Grant, B.J., Lukman, S., Hocker, H.J., et al.: Novel allosteric sites on ras for lead generation. Srivastava, R.K. (ed.) PLoS One 6(10), e25711 (2011). https://doi.org/10.1371/journal.pone.0025711

  96. Hazuda, D.J., Anthony, N.J., Gomez, R.P., et al.: A naphthyridine carboxamide provides evidence for discordant resistance between mechanistically identical inhibitors of HIV-1 integrase. Proc. Natl. Acad. Sci. 101(31), 11233–11238 (2004). https://doi.org/10.1073/pnas.0402357101

    Article  CAS  Google Scholar 

  97. Ivetac, A., Andrew, M.J.: Mapping the druggable allosteric space of G-protein coupled receptors: a fragment-based molecular dynamics approach. Chem. Biol. Drug. Des. 76(3), 201–217 (2010). https://doi.org/10.1111/j.1747-0285.2010.01012.x

    Article  CAS  Google Scholar 

  98. Arroio, A., Honório, K.M., da Silva, A.B.F.: Propriedades químico-quânticas empregadas em estudos das relações estrutura-atividade. Quim. Nova. 33(3), 694–699 (2010). https://doi.org/10.1590/s0100-40422010000300037

    Article  CAS  Google Scholar 

  99. Food US.: Annual review structure—activity relationship approaches and applications 22(8), 1680–1695 (2003)

    Google Scholar 

  100. Guha, R.: Chapter 6 On Exploring Structure-ActivityRelationships, p. 993 (2013). https://doi.org/10.1007/978-1-62703-342-8

  101. McKinney, J.D., Richard, A., Waller, C., Newman, M.C., Gerberick, F.: The practice of structure-activity relationships (SAR) in toxicology. Toxicol. Sci. 56(1), 8–17 (2000). https://doi.org/10.1093/toxsci/56.1.8

    Article  CAS  Google Scholar 

  102. Landscapes, A., Cliffs, A.: Structure-Activity Relationship Data Analysis, pp. 507–531 (2010)

    Google Scholar 

  103. Kubinyi, H.: QSAR and 3D QSAR in drug design. Part 1: methodology. Drug Discov. Today 2(11), 457–467 (1997). https://doi.org/10.1016/S1359-6446(97)01079-9

  104. Verma, J., Khedkar, V., Coutinho, E.: 3D-QSAR in drug design—a review. Curr. Top. Med. Chem. 10(1), 95–115 (2010). https://doi.org/10.2174/156802610790232260

    Article  CAS  Google Scholar 

  105. Gozalbes, R., Doucet, J.P., Derouin, F.: Application of topological descriptions in QSAR and drug design: history and new trends. Curr. Drug Targets Infect Disord. 2(1), 93–102 (2002). https://doi.org/10.2174/1568005024605909

    Article  CAS  Google Scholar 

  106. Neves, B.J., Braga, R.C., Melo-Filho, C.C., Moreira-Filho, J.T., Muratov, E.N., Andrade, C.H.: QSAR-based virtual screening: advances and applications in drug discovery. Front Pharmacol. 9, 1–7 (2018). https://doi.org/10.3389/fphar.2018.01275

  107. Lipinski, C.F., Maltarollo, V.G., Oliveira, P.R., da Silva, A.B.F., Honorio, K.M.: Advances and perspectives in applying deep learning for drug design and discovery. Front. Robot. AI 6(November), 1–6 (2019). https://doi.org/10.3389/frobt.2019.00108

    Article  Google Scholar 

  108. Vamathevan, J., Clark, D., Czodrowski, P., et al.: Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 18(6), 463–477 (2019). https://doi.org/10.1038/s41573-019-0024-5

    Article  CAS  Google Scholar 

  109. Lavecchia, A.: Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today. 20(3), 318–331 (2015). https://doi.org/10.1016/j.drudis.2014.10.012

    Article  Google Scholar 

  110. Stephenson, N., Shane, E., Chase, J., et al.: Survey of machine learning techniques in drug discovery. Curr. Drug Metab. 20(3), 185–193 (2018). https://doi.org/10.2174/1389200219666180820112457

    Article  CAS  Google Scholar 

  111. Hochreiter, S., Klambauer, G., Rarey, M.: Machine learning in drug discovery. J. Chem. Inf. Model. 58(9), 1723–1724 (2018). https://doi.org/10.1021/acs.jcim.8b00478

    Article  CAS  Google Scholar 

  112. Klambauer, G., Hochreiter, S., Rarey, M.: Machine learning in drug discovery. J. Chem. Inf. Model. 59(3), 945–946 (2019). https://doi.org/10.1021/acs.jcim.9b00136

    Article  CAS  Google Scholar 

  113. Gawehn, E., Hiss, J.A., Schneider, G.: Deep learning in drug discovery. Mol. Inform. 35(1), 3–14 (2016). https://doi.org/10.1002/minf.201501008

    Article  CAS  Google Scholar 

  114. Zhang, L., Tan, J., Han, D., Zhu, H.: From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today 22(11), 1680–1685 (2017). https://doi.org/10.1016/j.drudis.2017.08.010

    Article  Google Scholar 

  115. Ferreira, L.L.G., Andricopulo, A.D.: ADMET modeling approaches in drug discovery. Drug Discov. Today. 24(5), 1157–1165 (2019). https://doi.org/10.1016/j.drudis.2019.03.015

    Article  CAS  Google Scholar 

  116. Yu, H., Adedoyin, A.: ADME-Tox in drug discovery: integration of experimental and computational technologies. Drug Discov. Today. 8(18), 852–861 (2003). https://doi.org/10.1016/S1359-6446(03)02828-9

    Article  CAS  Google Scholar 

  117. Kar, S., Leszczynski, J.: Open access in silico tools to predict the ADMET profiling of drug candidates. Expert Opin. Drug Discov. 15(12), 1473–1487 (2020). https://doi.org/10.1080/17460441.2020.1798926

    Article  CAS  Google Scholar 

  118. Kazmi, S.R., Jun, R., Yu, M.S., Jung, C., Na, D.: In silico approaches and tools for the prediction of drug metabolism and fate: a review. Comput. Biol. Med. 2019(106), 54–64 (September 2018). https://doi.org/10.1016/j.compbiomed.2019.01.008

    Article  CAS  Google Scholar 

  119. Wishart, D.S.: Improving early drug discovery through ADME modelling: an overview. Drugs R D. 8(6), 349–362 (2007). https://doi.org/10.2165/00126839-200708060-00003

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Av. do Café, s/n, Ribeirão Preto, São Paulo, 14040-903, Brazil

    Mariana Pegrucci Barcelos, Isaque Antonio Galindo Francischini & Carlos Henrique Tomich de Paula da Silva

  2. Departamento de Química, Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto (FFCLRP), University of São Paulo, Av. Bandeirantes, Ribeirão Preto, São Paulo, 3900, 14090-901, Brazil

    Suzane Quintana Gomes, Guilherme Martins Silva & Carlos Henrique Tomich de Paula da Silva

  3. Computational Laboratory of Pharmaceutical Chemistry, School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Av. do Café, s/n, Ribeirão Preto, São Paulo, 14040-903, Brazil

    Leonardo Bruno Federico

  4. Laboratory of Pharmaceutical and Medicinal Chemistry (PharMedChem), Federal University of Amapá, Rodovia Juscelino Kubitisheck, km 02, Macapá, Amapá, 68903-419, Brazil

    Lorane Izabel da Silva Hage-Melim

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Correspondence to Mariana Pegrucci Barcelos .

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    Carlton A. Taft

  2. Departamento de Quimica, Federal University of São Carlos, São Carlos, Brazil

    Sergio R. de Lazaro

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Barcelos, M.P. et al. (2022). Lead Optimization in Drug Discovery. In: Taft, C.A., de Lazaro, S.R. (eds) Research Topics in Bioactivity, Environment and Energy. Engineering Materials. Springer, Cham. https://doi.org/10.1007/978-3-031-07622-0_19

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