1932

Abstract

A philosophy for defining what constitutes a virtual high-throughput screen is discussed, and the choices that influence decisions at each stage of the computational funnel are investigated, including an in-depth discussion of the generation of molecular libraries. Additionally, we provide advice on the storing, analysis, and visualization of data on the basis of extensive experience in our research group.

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2015-07-01
2024-04-18
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Literature Cited

  1. Reymond J-L, van Deursen R, Blum LC, Ruddigkeit L. 1.  2010. Chemical space as a source for new drugs. Med. Chem. Commun. 1:30 [Google Scholar]
  2. Cedar G, Persson K. 2.  2013. How supercomputers will yield a golden age of materials science. Sci. Am.Nov. 19
  3. Lipinski C, Hopkins A. 3.  2004. Navigating chemical space for biology and medicine. Nature 432:855–61 [Google Scholar]
  4. Wermuth C. 4.  2006. Selective optimization of side activities: the SOSA approach. Drug Discov. Today 11:160–64 [Google Scholar]
  5. Wang M, Hu X, Beratan DN, Yang W. 5.  2006. Designing molecules by optimizing potentials. J. Am. Chem. Soc. 128:3228–32 [Google Scholar]
  6. Balawender R, Welearegay MA, Lesiuk M, Proft FD, Geerlings P. 6.  2013. Exploring chemical space with the alchemical derivatives. J. Chem. Theory Comput. 9:5327–40 [Google Scholar]
  7. Tu M, Rai BK, Mathiowetz AM, Didiuk M, Pfefferkorn JA. 7.  et al. 2012. Exploring aromatic chemical space with NEAT: Novel and Electronically equivalent Aromatic Template. J. Chem. Inform. Model. 52:1114–23 [Google Scholar]
  8. Virshup AM, Contreras-García J, Wipf P, Yang W, Beratan DN. 8.  2013. Stochastic voyages into uncharted chemical space produce a representative library of all possible drug-like compounds. J. Am. Chem. Soc. 135:7296–303 [Google Scholar]
  9. Ehrlich HC, Henzler AM, Rarey M. 9.  2013. Searching for recursively defined generic chemical patterns in nonenumerated fragment spaces. J. Chem. Inform. Model. 53:1676–88 [Google Scholar]
  10. Hoksza D, Škoda P, Voršilák M, Svozil D. 10.  2014. Molpher: a software framework for systematic chemical space exploration. J. Cheminform. 6:7 [Google Scholar]
  11. Fink T, Bruggesser H, Reymond J-L. 11.  2005. Virtual exploration of the small-molecule chemical universe below 160 Daltons. Angew. Chem. Int. Ed. 44:1504–8 [Google Scholar]
  12. Blum LC, Reymond J-L. 12.  2009. 970 million druglike small molecules for virtual screening in the chemical universe database GDB-13. J. Am. Chem. Soc. 131:8732–33 [Google Scholar]
  13. Ruddigkeit L, van Deursen R, Blum LC, Reymond J-L. 13.  2012. Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J. Chem. Inform. Model. 52:2864–75 [Google Scholar]
  14. Taniguchi M, Du H, Lindsey JS. 14.  2011. Virtual libraries of tetrapyrrole macrocycles. Combinatorics isomers, product distributions, and data mining. J. Chem. Inform. Model. 51:2233–47 [Google Scholar]
  15. Yu MJ. 15.  2011. Natural product-like virtual libraries: recursive atom-based enumeration. J. Chem. Inform. Model. 51:541–57 [Google Scholar]
  16. Massarotti A, Brunco A, Sorba G, Tron GC. 16.  2014. ZINClick: a database of 16 million novel patentable, and readily synthesizable 1,4-disubstituted triazoles. J. Chem. Inform. Model. 54:396–406 [Google Scholar]
  17. Koutsoukas A, Paricharak S, Galloway WRJD, Spring DR, IJzerman AP. 17.  et al. 2014. How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space. J. Chem. Inform. Model. 54:230–42 [Google Scholar]
  18. Roth HJ. 18.  2005. There is no such thing as ‘diversity’!. Curr. Opin. Chem. Biol. 9:293–95 [Google Scholar]
  19. Riniker S, Landrum GA. 19.  2013. Open-source platform to benchmark fingerprints for ligand-based virtual screening. J. Cheminform. 5:26 [Google Scholar]
  20. Maggiora G, Vogt M, Stumpfe D, Bajorath J. 20.  2014. Molecular similarity in medicinal chemistry. J. Med. Chem. 57:3186–204 [Google Scholar]
  21. Gillet V, Johnson A, Mata P, Sike S, Williams P. 21.  1993. SPROUT: a program for structure generation. J. Comput. Aided Mol. Des. 7:127–53 [Google Scholar]
  22. Pearlman D, Murcko M. 22.  1996. CONCERTS: dynamic connection of fragments as an approach to de novo ligand design. J. Med. Chem. 39:1651–63 [Google Scholar]
  23. Schneider G, Lee M, Stahl M, Schneider P. 23.  2000. De novo design of molecular architectures by evolutionary assembly of drug-derived building blocks. J. Comput. Aided Mol. Des. 14:487–94 [Google Scholar]
  24. Gillet V, Willett P, Fleming P, Green D. 24.  2002. Designing focused libraries using MoSELECT. J. Mol. Graph. Model. 20:491–98 [Google Scholar]
  25. Vinkers H, de JM, Daeyaert F, Heeres J, Koymans L. 25.  et al. 2003. SYNOPSIS: SYNthesize and OPtimize System in Silico. J. Med. Chem. 46:2765–73 [Google Scholar]
  26. Brown N, McKay B, Gasteiger J. 26.  2004. The de novo design of median molecules within a property range of interest. J. Comput. Aided Mol. Des. 18:761–71 [Google Scholar]
  27. Nicolaou C, Brown N, Pattichis C. 27.  2007. Molecular optimization using computational multi-objective methods. Curr. Opin. Drug Discov. Devel. 10:316–24 [Google Scholar]
  28. Liu Q, Masek B, Smith K, Smith J. 28.  2007. Tagged fragment method for evolutionary structure-based de novo lead generation and optimization. J. Med. Chem. 50:5392–402 [Google Scholar]
  29. Dey F, Caflisch A. 29.  2008. Fragment-based de novo ligand design by multiobjective evolutionary optimization. J. Chem. Inform. Model. 48:679–90 [Google Scholar]
  30. Besnard J, Ruda GF, Setola V, Abecassis K, Rodriguiz RM. 30.  et al. 2012. Automated design of ligands to polypharmacological profiles. Nature 492:215–20 [Google Scholar]
  31. Osedach TP, Andrew TL, Bulović V. 31.  2013. Effect of synthetic accessibility on the commercial viability of organic photovoltaics. Energy Environ. Sci. 6:711–18 [Google Scholar]
  32. O'Boyle NM, Campbell CM, Hutchison GR. 32.  2011. Computational design and selection of optimal organic photovoltaic materials. J. Phys. Chem. C 115:16200–10 [Google Scholar]
  33. Kanal IY, Owens SG, Bechtel JS, Hutchison GR. 33.  2013. Efficient computational screening of organic polymer photovoltaics. J. Phys. Chem. Lett. 4:1613–23 [Google Scholar]
  34. Bertz SH. 34.  1981. The first general index of molecular complexity. J. Am. Chem. Soc. 103:3599–601 [Google Scholar]
  35. Boda K, Johnson A. 35.  2006. Molecular complexity analysis of de novo designed ligands. J. Med. Chem. 49:5869–79 [Google Scholar]
  36. Bonnet P. 36.  2012. Is chemical synthetic accessibility computationally predictable for drug and lead-like molecules? A comparative assessment between medicinal and computational chemists. Eur. J. Med. Chem. 54:679–89 [Google Scholar]
  37. Podolyan Y, Walters MA, Karypis G. 37.  2010. Assessing synthetic accessibility of chemical compounds using machine learning methods. J. Chem. Inform. Model. 50:979–91 [Google Scholar]
  38. Warr WA. 38.  2014. A short review of chemical reaction database systems computer-aided synthesis design, reaction prediction and synthetic feasibility. Mol. Inf. 33:469–76 [Google Scholar]
  39. Hachmann J, Olivares-Amaya R, Atahan-Evrenk S, Amador-Bedolla C, Sánchez-Carrera RS. 39.  et al. 2011. The Harvard Clean Energy Project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2:2241–51 [Google Scholar]
  40. Olivares-Amaya R, Amador-Bedolla C, Hachmann J, Atahan-Evrenk S, Sánchez-Carrera RS. 40.  et al. 2011. Accelerated computational discovery of high-performance materials for organic photovoltaics by means of cheminformatics. Energy Environ. Sci. 4:4849–61 [Google Scholar]
  41. Huskinson B, Marshak MP, Suh C, Er S, Gerhardt MR. 41.  et al. 2014. A metal-free organic–inorganic aqueous flow battery. Nature 505:195–98 [Google Scholar]
  42. Er S, Suh C, Marshak MP, Aspuru-Guzik A. 41a.  2015. A computational design of molecules for an all-quinone redox flow battery. Chem. Sci. In press
  43. Goushi K, Yoshida K, Sato K, Adachi C. 42.  2012. Organic light-emitting diodes employing efficient reverse intersystem crossing for triplet-to-singlet state conversion. Nat. Photonics 6:253–58 [Google Scholar]
  44. Zhang Q, Li B, Huang S, Nomura H, Tanaka H, Adachi C. 43.  2014. Efficient blue organic light-emitting diodes employing thermally activated delayed fluorescence. Nat. Photonics 8:326–32 [Google Scholar]
  45. Korth M. 44.  2014. Large-scale virtual high-throughput screening for the identification of new battery electrolyte solvents: evaluation of electronic structure theory methods. Phys. Chem. Chem. Phys. 16:7919–26 [Google Scholar]
  46. Ong SP, Richards WD, Jain A, Hautier G, Kocher M. 45.  et al. 2013. Python Materials Genomics (pymatgen): a robust open-source python library for materials analysis. Comput. Mater. Sci. 68:314–19 [Google Scholar]
  47. Kresse G, Furthmüller J. 46.  1996. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 6:15–50 [Google Scholar]
  48. Blöchl PE. 47.  1994. Projector augmented-wave method. Phys. Rev. B 50:17953–79 [Google Scholar]
  49. Perdew JP, Burke K, Ernzerhof M. 48.  1996. Generalized gradient approximation made simple. Phys. Rev. Lett. 77:3865–68 [Google Scholar]
  50. Anisimov VI, Zaanen J, Andersen OK. 49.  1991. Band theory and Mott insulators: Hubbard U instead of Stoner I. Phys. Rev. B 44:943–54 [Google Scholar]
  51. Jain A, Hautier G, Moore CJ, Ong SP, Fischer CC. 50.  et al. 2011. A high-throughput infrastructure for density functional theory calculations. Comput. Mater. Sci. 50:2295–310 [Google Scholar]
  52. Jain A, Ong SP, Hautier G, Chen W, Richards WD. 51.  et al. 2013. Commentary: The Materials Project: a materials genome approach to accelerating materials innovation. APL Mater. 1:011002 [Google Scholar]
  53. Hachmann J, Olivares-Amaya R, Jinich A, Appleton AL, Blood-Forsythe MA. 52.  et al. 2014. Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry—the Harvard Clean Energy Project. Energy Environ. Sci. 7:698–704 [Google Scholar]
  54. Scharber MC, Mühlbacher D, Koppe M, Denk P, Waldauf C. 53.  et al. 2006. Design rules for donors in bulk-heterojunction solar cells—towards 10% energy-conversion efficiency. Adv. Mater. 18:789–94 [Google Scholar]
  55. Shockley W, Queisser HJ. 54.  1961. Detailed balance limit of efficiency of p-n junction solar cells. J. Appl. Phys. 32:510 [Google Scholar]
  56. Kolossváry I, Guida WC. 55.  1996. Low mode search. An efficient automated computational method for conformational analysis: application to cyclic and acyclic alkanes and cyclic peptides. J. Am. Chem. Soc. 118:5011–19 [Google Scholar]
  57. Sadowski J, Gasteiger J, Klebe G. 56.  1994. Comparison of automatic three-dimensional model builders using 639 X-ray structures. J. Chem. Inform. Model. 34:1000–8 [Google Scholar]
  58. Mayo SL, Olafson BD, Goddard WA. 57.  1990. DREIDING: a generic force field for molecular simulations. J. Phys. Chem. 94:8897–909 [Google Scholar]
  59. Parker CN, Shamu CE, Kraybill B, Austin CP, Bajorath J. 58.  2006. Measure, mine, model, and manipulate: the future for HTS and chemoinformatics?. Drug Discov. Today 11:863–65 [Google Scholar]
  60. Tamura SY, Bacha PA, Gruver HS, Nutt RF. 59.  2002. Data analysis of high-throughput screening results: application of multidomain clustering to the NCI anti-HIV data set. J. Med. Chem. 45:3082–93 [Google Scholar]
  61. Harper G, Pickett SD. 60.  2006. Methods for mining HTS data. Drug Discov. Today 11:694–99 [Google Scholar]
  62. Ling X. 61.  2008. High throughput screening informatics. Comb. Chem. High Throughput Screen. 11:249–57 [Google Scholar]
  63. Medina-Franco J, Martínez-Mayorga K, Giulianotti M, Houghten R, Pinilla C. 62.  2008. Visualization of the chemical space in drug discovery. Comput. Aided Drug Des. 4:322–33 [Google Scholar]
  64. Goktug AN, Chai SC, Chen T. 63.  2013. Drug discovery. Pharmacology and Therapeutics S Gowder, Chapter 7 Rijeka, Croatia: InTech [Google Scholar]
  65. García-Domenech R, Gálvez J, de Julián-Ortiz JV, Pogliani L. 64.  2008. Some new trends in chemical graph theory. Chem. Rev. 108:1127–69 [Google Scholar]
  66. Suh C, Sieg SC, Heying MJ, Oliver JH, Maier WF, Rajan K. 65.  2009. Visualization of high-dimensional combinatorial catalysis data. J. Comb. Chem. 11:385–92 [Google Scholar]
  67. Awale M, van Deursen R, Reymond J-L. 66.  2013. MQN-Mapplet: visualization of chemical space with interactive maps of DrugBank, ChEMBL, PubChem, GDB-11, and GDB-13. J. Chem. Inform. Model. 53:509–18 [Google Scholar]
  68. Klopmand G. 67.  1992. Concepts and applications of molecular similarity, by Mark A. Johnson and Gerald M. Maggiora. John Wiley & Sons, New York, 1990 393 Price: $65.00 J. Comput. Chem. 13539–40 [Google Scholar]
  69. Willett P, Barnard J, Downs G. 68.  1998. Chemical similarity searching. J. Chem. Inform. Model. 38:983–96 [Google Scholar]
  70. Chen X, Reynolds C. 69.  2002. Performance of similarity measures in 2D fragment-based similarity searching: comparison of structural descriptors and similarity coefficients. J. Chem. Inform. Model. 42:1407–14 [Google Scholar]
  71. Godden JW, Bajorath J. 70.  2006. A distance function for retrieval of active molecules from complex chemical space representations. J. Chem. Inform. Model. 46:1094–97 [Google Scholar]
  72. Haranczyk M, Holliday J. 71.  2008. Comparison of similarity coefficients for clustering and compound selection. J. Chem. Inform. Model. 48:498–508 [Google Scholar]
  73. Coifman RR, Lafon S. 72.  2006. Diffusion maps. Appl. Comput. Harmon. Anal. 21:5–30 [Google Scholar]
  74. Platts J, Butina D, Abraham M, Hersey A. 73.  1999. Estimation of molecular linear free energy relation descriptors using a group contribution approach. J. Chem. Inform. Model. 39:835–45 [Google Scholar]
  75. Liu ZK, Chen LQ, Rajan K. 74.  2006. Linking length scales via materials informatics. JOM 58:42–50 [Google Scholar]
  76. Balabin RM, Lomakina EI. 75.  2011. Support vector machine regression—an alternative to artificial neural networks for the analysis of quantum chemistry data?. Phys. Chem. Chem. Phys. 13:11710 [Google Scholar]
  77. Balabin RM, Lomakina EI. 76.  2009. Neural network approach to quantum-chemistry data: accurate prediction of density functional theory energies. J. Chem. Phys. 131:074104 [Google Scholar]
  78. Pilania G, Wang C, Jiang X, Rajasekaran S, Ramprasad R. 77.  2013. Accelerating materials property predictions using machine learning. Sci. Rep. 3:2810 [Google Scholar]
  79. Rajan K, Suh C, Mendez PF. 78.  2009. Principal component analysis and dimensional analysis as materials informatics tools to reduce dimensionality in materials science and engineering. Stat. Anal. Data Min. 1:361–71 [Google Scholar]
  80. Dewar MJS, Trinajstic N. 79.  1969. Ground states of conjugated molecules—XIV. Tetrahedron 25:4529–34 [Google Scholar]
  81. Bajorath J. 80.  2001. Selected concepts and investigations in compound classification molecular descriptor analysis, and virtual screening. J. Chem. Inform. Model. 41:233–45 [Google Scholar]
  82. Searls DB. 81.  2005. Data integration: challenges for drug discovery. Nat. Rev. Drug Discov. 4:45–58 [Google Scholar]
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