Knowledge discovery through chemical space networks: the case of organic electronics

  • Christian Kunkel
  • Christoph Schober
  • Harald OberhoferEmail author
  • Karsten Reuter
Original Paper
Part of the following topical collections:
  1. Tim Clark 70th Birthday Festschrift


Modern materials discovery and design studies often rely on the computational screening of large databases. Complementing experimental databases, virtual databases are thereby increasingly established through the first-principles calculation of computationally inexpensive, but for a given application, decisive microscopic quantities of the system. These so-called descriptors are calculated for vast numbers of candidate materials. In general, the sheer volume of datapoints generated in such studies precludes an in depth human analysis. To this end, smart visualization techniques, based e.g., on so-called chemical space networks (CSN), have been developed to extract general design rules connecting structural modifications to changes in the target functionality. In this work, we generate and visualize the CSN of possible crystalline organic semiconductors based on an in-house database of > 64,000 molecular crystals that we extracted from the exhaustive Cambridge Structural Database and for which we computed prominent charge-mobility descriptors. Our CSN thereby links clusters of molecular crystals based on the chemical similarity of the scaffolds of their molecular building blocks and thus groups communities of similar molecules. Including each cluster’s median descriptor values, the CSN visualization not only reproduces known trends of good organic semiconductors but also allows us to extract general design rules for organic molecular scaffolds. Finally, the local environment of each scaffold in our visualization shows how thoroughly its local chemical space has already been explored synthetically. Of special interest here are those clusters with promising descriptor values, yet with little or no connections in the sampled chemical space, as these offer the most room for scaffold optimization.


Organic electronics Materials design Chemical space networks 



We acknowledge support from the Solar Technologies Go Hybrid initiative of the State of Bavaria and the Leibniz Supercomputing Centre for high-performance computing time at the SuperMUC facility. We further acknowledge support by Deutsche Forschungsgemeinschaft (DFG) through TUM International Graduate School of Science and Engineering (IGSSE), GSC 81.

Supplementary material

894_2019_3950_MOESM1_ESM.pdf (707 kb)
(PDF 707 KB)


  1. 1.
    Gómez-Bombarelli R, Aguilera-Iparraguirre J, Hirzel TD, Duvenaud D, Maclaurin D, Blood-Forsythe MA, Chae HS, Einzinger M, Ha D-G, Wu T, Markopoulos G, Jeon S, Kang H, Miyazaki H, Numata M, Kim S, Huang W, Hong SI, Baldo M, Adams RP, Aspuru-Guzik A (2016) vol 15Google Scholar
  2. 2.
    Agrawal A, Choudhary A (2016) APL Mater 4:053208CrossRefGoogle Scholar
  3. 3.
    Lo Y-C, Senese S, Li C-M, Hu Q, Huang Y, Damoiseaux R, Torres JZ (2015) PLoS Comput Biol 11:1CrossRefGoogle Scholar
  4. 4.
    Ferguson A, Hachmann J (2018) Mol Syst Des Eng 3:429CrossRefGoogle Scholar
  5. 5.
    Isayev O, Fourches D, Muratov EN, Oses C, Rasch K, Tropsha A, Curtarolo S (2015) Chem Mater 27:735CrossRefGoogle Scholar
  6. 6.
    Olivares-Amaya R, Amador-Bedolla C, Hachmann J, Atahan-Evrenk S, Sanchez-Carrera RS, Vogt L, Aspuru-Guzik A (2011) Energy Environ Sci 4:4849CrossRefGoogle Scholar
  7. 7.
    Akimov AV, Prezhdo OV (2015) Chem Rev 115:5797CrossRefGoogle Scholar
  8. 8.
    Schober C, Reuter K, Oberhofer H (2016) J Phys Chem Lett 7:3973CrossRefGoogle Scholar
  9. 9.
    Reymond J-L, van Deursen R, Blum LC, Ruddigkeit L (2010) Med Chem Commun 1:30CrossRefGoogle Scholar
  10. 10.
    Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Science 323:892CrossRefGoogle Scholar
  11. 11.
    Barabási A-L, Gulbahce N, Loscalzo J (2010) Nat Rev Gen 12:56 EPCrossRefGoogle Scholar
  12. 12.
    Cotacallapa M, Hase MO (2016) J Phys A 49:065001CrossRefGoogle Scholar
  13. 13.
    Ideker T, Nussinov R (2017) PLOS Comput Biol 13:1CrossRefGoogle Scholar
  14. 14.
    Barabási A, Psfai M (2016) Network science. Cambridge University PressGoogle Scholar
  15. 15.
    Hopkins AL (2008) Nat Chem Biol. 4:682 EPCrossRefGoogle Scholar
  16. 16.
    Shelat AA, Guy RK (2007) Nat Chem Biol 3:442CrossRefGoogle Scholar
  17. 17.
    Kontijevskis A (2017) J Chem Inf Model 57:680CrossRefGoogle Scholar
  18. 18.
    Sandefur CI, Mincheva M, Schnell S (2013) Mol BioSyst 9:2189CrossRefGoogle Scholar
  19. 19.
    Simm GN, Reiher M (2017) J Chem Theory Comput 13:6108CrossRefGoogle Scholar
  20. 20.
    Opassi G, Ges A, Massarotti A (2018) Drug Discov Today 23:565CrossRefGoogle Scholar
  21. 21.
    Osolodkin DI, Radchenko EV, Orlov AA, Voronkov AE, Palyulin VA, Zefirov NS (2015) Expert Opin Drug Discov 10:959CrossRefGoogle Scholar
  22. 22.
    Gütlein M, Karwath A, Kramer S (2014) J Cheminform 6:41CrossRefGoogle Scholar
  23. 23.
    Gonzlez-Medina M, Medina-Franco JL (2017) J Chem Inf Model 57:1735CrossRefGoogle Scholar
  24. 24.
    Maggiora GM, Bajorath J (2014) J Comput Aided Mol Des 28:795CrossRefGoogle Scholar
  25. 25.
    Wawer M, Peltason L, Weskamp N, Teckentrup A, Bajorath J (2008) J Med Chem 51:6075CrossRefGoogle Scholar
  26. 26.
    Minemawari H, Yamada T, Matsui H, Tsutsumi J, Haas S, Chiba R, Kumai R, Hasegawa T (2011) Nature 475:364CrossRefGoogle Scholar
  27. 27.
    Stavrinidou E, Gabrielsson R, Gomez E, Crispin X, Nilsson O, Simon DT, Berggren M (2015) Sci Adv 1:e1501136CrossRefGoogle Scholar
  28. 28.
    Xu J, Wang S, Wang G-JN, Zhu C, Luo S, Jin L, Gu X, Chen S, Feig VR, To JW et al (2017) Science 355:59CrossRefGoogle Scholar
  29. 29.
    Nikolka M, Nasrallah I, Rose B, Ravva MK, Broch K, Sadhanala A, Harkin D, Charmet J, Hurhangee M, Brown A et al (2017) Nat Mater 16:356CrossRefGoogle Scholar
  30. 30.
    Wang C, Dong H, Jiang L, Hu W (2018) Chem Soc Rev 47:422CrossRefGoogle Scholar
  31. 31.
    Fischer JR, Lessel U, Rarey M (2010) J Chem Inf Model 50:1CrossRefGoogle Scholar
  32. 32.
    Bian Y, Xie X-QS (2018) AAPS J 20:59CrossRefGoogle Scholar
  33. 33.
    Hall RJ, Murray CW, Verdonk ML (2017) J Med Chem 60:6440CrossRefGoogle Scholar
  34. 34.
    Misra M, Andrienko D, Baumeier B, Faulon J-L, von Lilienfeld OA (2011) J Chem Theory Comput 7:2549CrossRefGoogle Scholar
  35. 35.
    Sahu H, Rao W, Troisi A, Ma H (2018) Adv Energy Mater 8:1801032CrossRefGoogle Scholar
  36. 36.
    Sokolov AN, Atahan-Evrenk S, Mondal R, Akkerman HB, Sánchez-Carrera RS, Granados-Focil S, Schrier J, Mannsfeld SCB, Zoombelt AP, Bao Z, Aspuru-Guzik A (2011) Nat Commun, 2Google Scholar
  37. 37.
    Moral M, Garzón-Ruiz A, Castro M, Canales-Vázquez J, Sancho-García JC (2017) J Phys Chem C 121:28249CrossRefGoogle Scholar
  38. 38.
    Hutchison GR, Ratner MA, Marks TJ (2005) J Am Chem Soc 127:2339CrossRefGoogle Scholar
  39. 39.
    Li J, Zhao Y, Tan HS, Guo Y, Di C-A, Yu G, Liu Y, Lin M, Lim SH, Zhou Y, Su H, Ong BS (2012) Sci Rep 2:754 EPCrossRefGoogle Scholar
  40. 40.
    Blouin N, Michaud A, Gendron D, Wakim S, Blair E, Neagu-Plesu R, Belletête M, Durocher G, Tao Y, Leclerc M (2008) J Am Chem Soc 130:732CrossRefGoogle Scholar
  41. 41.
    Kunkel C, Schober C, Margraf JT, Reuter K, Oberhofer H (2018) submittedGoogle Scholar
  42. 42.
    Allen FH (2002) Acta Crystallogr B 58:380CrossRefGoogle Scholar
  43. 43.
    Oberhofer H, Reuter K, Blumberger J (2017) Chem Rev 117:10319CrossRefGoogle Scholar
  44. 44.
    Marcus RA (1956) J Chem Phys 24:966CrossRefGoogle Scholar
  45. 45.
    Marcus RA (1993) Rev Mod Phys 65:599CrossRefGoogle Scholar
  46. 46.
    Schober C, Reuter K, Oberhofer H (2016) J Chem Phys 144:054103CrossRefGoogle Scholar
  47. 47.
    Nelsen SF, Blackstock SC, Kim Y (1987) J Am Chem Soc 109:677CrossRefGoogle Scholar
  48. 48.
    Blum V, Gehrke R, Hanke F, Havu P, Havu V, Ren X, Reuter K, Scheffler M (2009) Comp Phys Commun 180:2175CrossRefGoogle Scholar
  49. 49.
    Zhang IY, Ren X, Rinke P, Blum V, Scheffler M (2013) J Phys 15:123033Google Scholar
  50. 50.
    Becke AD (1988) Phys Rev A 38:3098CrossRefGoogle Scholar
  51. 51.
    Lee C, Yang W, Parr RG (1988) Phys Rev B 37:785CrossRefGoogle Scholar
  52. 52.
    Hu Y, Stumpfe D, Bajorath J (2016) J Med Chem 59:4062CrossRefGoogle Scholar
  53. 53.
    Bemis GW, Murcko MA (1996) J Med Chem 39:2887CrossRefGoogle Scholar
  54. 54.
    Wang C, Dong H, Hu W, Liu Y, Zhu D (2012) Chem Rev 112:2208CrossRefGoogle Scholar
  55. 55.
    Jiang W, Li Y, Wang Z (2013) Chem Soc Rev 42:6113CrossRefGoogle Scholar
  56. 56.
    Landrum G (2018) RDKit: open-source cheminformatics, [Online; Accessed 07 Aug 2018]
  57. 57.
    Python software foundation. Python language reference, version 2.7. available at
  58. 58.
    Ertl P (2014) J Chem Inf Model 54:1617CrossRefGoogle Scholar
  59. 59.
    Rabal O, Amr FI, Oyarzabal J (2015) J Chem Inf Model 55:1CrossRefGoogle Scholar
  60. 60.
    Vogt M, Stumpfe D, Maggiora GM, Bajorath J (2016) J Comput Aided Mol Des 30:191CrossRefGoogle Scholar
  61. 61.
    Carhart RE, Smith DH, Venkataraghavan R (1985) J Chem Inf Comput Sci 25:64CrossRefGoogle Scholar
  62. 62.
    Rogers D, Hahn M (2010) J Chem Inf Model 50:742CrossRefGoogle Scholar
  63. 63.
    Bastian M, Heymann S, Jacomy M (2009) In: International AAAI conference on weblogs and social mediaGoogle Scholar
  64. 64.
    Jacomy M, Venturini T, Heymann S, Bastian M (2014) PLOS ONE 9:1CrossRefGoogle Scholar
  65. 65.
    ChemAxon (2017) Marvin 17.5.0,, [Online; Accessed 07 Aug 2018]
  66. 66.
    Bokeh Development Team (2018) Bokeh: Python library for interactive visualizationGoogle Scholar
  67. 67.
    Kunkel C, Schober C, Oberhofer H, Reuter K (2018) A chemical space network for organic electronics,, [Online, published 22 Dec 2018]
  68. 68.
    Webcsd (2019), [Online, Accessed 14 Jan 2019]
  69. 69.
    Agrafiotisand DK, Wiener JJM (2010) J Med Chem 53:5002CrossRefGoogle Scholar
  70. 70.
    Varin T, Schuffenhauer A, Ertl P, Renner S (2011) J Chem Inf Model 51:1528CrossRefGoogle Scholar
  71. 71.
    Shelat AA, Guy RK (2007) Nat Chem Biol 3:442 EPCrossRefGoogle Scholar
  72. 72.
    Lin Y, Li Y, Zhan X (2012) Chem Soc Rev 41:4245CrossRefGoogle Scholar
  73. 73.
    Kitamura M, Arakawa Y (2008) J Phys Condens Matter 20:184011CrossRefGoogle Scholar
  74. 74.
    de la Vega León A, Bajorath J (2016) Future Med Chem 8:1769CrossRefGoogle Scholar
  75. 75.
    Lin Y, Fan H, Li Y, Zhan X (2012) Adv Mater 24:3087CrossRefGoogle Scholar
  76. 76.
    Canevet D, Sallé M., Zhang G, Zhang D, Zhu D (2009) Chem Commun, 2245Google Scholar
  77. 77.
    Mei J, Diao Y, Appleton AL, Fang L, Bao Z (2013) J Am Chem Soc 135:6724CrossRefGoogle Scholar
  78. 78.
    Reig M, Bagdziunas G, Volyniuk D, Grazulevicius JV, Velasco D (2017) Phys Chem Chem Phys 19:6721CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Chair for Theoretical Chemistry and Catalysis Research CenterTechnische Universität MünchenGarchingGermany

Personalised recommendations