Inductive Queries for a Drug Designing Robot Scientist

  • Ross D. KingEmail author
  • Amanda Schierz
  • Amanda Clare
  • Jem Rowland
  • Andrew Sparkes
  • Siegfried Nijssen
  • Jan Ramon


It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments.


Inductive Logic Programming Data Mining Algorithm Subgraph Isomorphism Outerplanar Graph Graph Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    C. Borgelt and M.R. Berthold. Mining molecular fragments: Finding relevant substructures of molecules. In ICDM, pages 51–58. IEEE Computer Society, 2002.Google Scholar
  2. 2.
    H. Blockeel, L. De Raedt. Top-Down Induction of First-Order Logical Decision Trees. Artif. Intell. 101(1–2): 285–297 (1998).zbMATHCrossRefGoogle Scholar
  3. 3.
    H. Blockeel, S. Dzeroski, B. Kompare, S. Kramer, B. Pfahringer, and W. Van Laer. Experiments in predicting biodegradability. In Appl. Art. Int. 18, pages 157–181, 2004.Google Scholar
  4. 4.
    B. Bringmann, A. Zimmermann, L. De Raedt, and S. Nijssen. Don’t be afraid of simpler patterns. In J. Fürnkranz, T. Scheffer, and M. Spiliopoulou, editors, PKDD, volume 4213 of Lecture Notes in Computer Science, pages 55–66. Springer, 2006.Google Scholar
  5. 5.
    E.F. Codd. Recent Investigations into Relational Data Base Systems. IBM Research Report RJ1385 (April 23rd, 1974). Republished in Proc. 1974 Congress (Stockholm, Sweden, 1974). New York, N.Y.: North–Holland, 1974.Google Scholar
  6. 6.
    Dennis D. Cox and Susan John. SDO: a statistical method for global optimization. In Multidisciplinary design optimization (Hampton, VA, 1995), pages 315–329. SIAM, 1997.Google Scholar
  7. 7.
    R.D. III Cramer, D.E. Patterson, and Bunce J.D. Comparative Field Analysis (CoMFA). The effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110: 5959–5967, 1988.Google Scholar
  8. 8.
    L. Dehaspe, H. Toivonen, and R.D. King. Finding frequent substructures in chemical compounds. In: The Fourth International Conference on Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, Ca. 30–36, 1998.Google Scholar
  9. 9.
    L. Dehaspe, L. De Raedt. Mining Association Rules in Multiple Relations. In: ILP 1997: 125–132.Google Scholar
  10. 10.
    L. De Raedt. Statistical and Relational Learning. Springer, 2008.Google Scholar
  11. 11.
    L. De Raedt, J. Ramon. Deriving distance metrics from generality relations. Pattern Recognition Letters 30(3): 187–191 (2009).CrossRefGoogle Scholar
  12. 12.
    R.O.Duda, P.E. Hart, and D.G. Stork. Pattern Classification. Wiley, 2001.Google Scholar
  13. 13.
    D. Enot and R.D. King. Application of inductive logic programming to structure-based drug design. Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2003.Google Scholar
  14. 14.
    D. Eppstein. Subgraph isomorphism in planar graphs and related problems. In Symposium on Discrete Algorithms, pages 632–640, 1995.Google Scholar
  15. 15.
    P. Frasconi, A. Passerini. Learning with Kernels and Logical Representations. Probabilistic Inductive Logic Programming, 2008: 56–91.Google Scholar
  16. 16.
    T. Gärtner. A survey of kernels for structured data. SIGKDD Explorations, 5(18.1):49–58, 2003.CrossRefGoogle Scholar
  17. 17.
    T. Gärtner, Peter A. Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. In B. Schölkopf and M.K. Warmuth, editors, COLT, volume 2777 of Lecture Notes in Computer Science, pages 129–143. Springer, 2003.Google Scholar
  18. 18.
    J. Gasteiger and T. Engel. Chemoinformatics: A Textbook. Wiley-VCH, 2003.Google Scholar
  19. 19.
    C. Hansch, P.P. Malony, T. Fujiya, and R.M. Muir. Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature 194, 178–180, 1965.CrossRefGoogle Scholar
  20. 20.
    H. Hofer, C. Borgelt, and M.R. Berthold. Large scale mining of molecular fragments with wildcards. In M.R. Berthold, H-J. Lenz, E. Bradley, R. Kruse, and C. Borgelt, editors, IDA, volume 2810 of Lecture Notes in Computer Science, pages 376–385. Springer, 2003.Google Scholar
  21. 21.
    C. Helma, T. Cramer, S. Kramer, and L. De Raedt. Data mining and machine learning techniques for the identification of mutagenicity inducing substructures and structure activity relationships of noncongeneric compounds. In Journal of Chemical Information and Computer Systems 44, pages 1402–1411, 2004.Google Scholar
  22. 22.
    T. Horváth and J. Ramon. Efficient frequent connected subgraph mining in graphs of bounded treewidth. In W. Daelemans, B. Goethals, and K. Morik, editors, ECML/PKDD (18.1), volume 5211 of Lecture Notes in Computer Science, pages 520–535. Springer, 2008.Google Scholar
  23. 23.
    T. Horváth, J. Ramon, and S. Wrobel. Frequent subgraph mining in outerplanar graphs. In KDD, pages 197–206. ACM, 2006.Google Scholar
  24. 24.
    J. Huan, W. Wang, and J. Prins. Efficient mining of frequent subgraphs in the presence of isomorphism. In Proceedings of the Third IEEE International Conference on Data Mining (ICDM), pages 549–552. IEEE Press, 2003.Google Scholar
  25. 25.
    Jun Huan, Wei Wang, Jan Prins, and Jiong Yang. Spin: mining maximal frequent subgraphs from graph databases. In Won Kim, Ron Kohavi, Johannes Gehrke, and William DuMouchel, editors, KDD, pages 581–586. ACM, 2004.Google Scholar
  26. 26.
    Akihiro Inokuchi. Mining generalized substructures from a set of labeled graphs. In ICDM, pages 415–418. IEEE Computer Society, 2004.Google Scholar
  27. 27.
    A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In Proceedings of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), volume 1910 of Lecture Notes in Artificial Intelligence, pages 13–23. Springer-Verlag, 2000.Google Scholar
  28. 28.
    D.R. Jones. A taxonomy of global optimization methods based on response surfaces. Journal of Global Optimization, 21:345–383, 2001.zbMATHCrossRefGoogle Scholar
  29. 29.
    D.R. Jones and M. Schonlau. Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13(4):455–492, December 1998.zbMATHCrossRefMathSciNetGoogle Scholar
  30. 30.
    M. Kuramochi and G. Karypis. Frequent subgraph discovery. In Proceedings of the First IEEE International Conference on Data Mining (ICDM), pages 313–320. IEEE Press, 2001.Google Scholar
  31. 31.
    J. Kazius, S. Nijssen, J.N. Kok, T. Bäck, and A. IJzerman. Substructure mining using elaborate chemical representation. In Journal of Chemical Information and Modeling 46, 2006.Google Scholar
  32. 32.
    R.D. King, S. Muggleton, R.A Lewis, and M.J.E Sternberg. Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proc. Nat. Acad. Sci. U.S.A. 89, 11322–11326, 1992.Google Scholar
  33. 33.
    R.D. King, S. Muggleton, A. Srinivasan, and M.J.E. Sternberg. Structure-activity relationships derived by machine learning: The use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming. Proc. Nat. Acad. Sci. USA 93, 438–442, 1996.Google Scholar
  34. 34.
    R.D. King, J. Rowland, S.G. Oliver, M. Young, W. Aubrey, E. Byrne, M. Liakata, M. Markham, P. Pir, L.N. Soldatova, A. Sparkes, K.E. Whelan, A. Clare. The Automation of Science. Science. Vol. 324, no. 5923, pp. 85 – 89.Google Scholar
  35. 35.
    S. Kramer and L. De Raedt. Feature construction with version spaces for biochemical applications. In ICML, pages 258–265. Morgan Kaufmann, 2001.Google Scholar
  36. 36.
    S. Kramer, L. De Raedt, and C. Helma. Molecular feature mining in hiv data. In KDD, pages 136–143, 2001.Google Scholar
  37. 37.
    M. Kearns and S. Singh. Near-optimal reinforcement learning in polynomial time. In Proc. 15th International Conf. on Machine Learning, pages 260–268. Morgan Kaufmann, 1998.Google Scholar
  38. 38.
    H.J. Kushner. A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. Journal of Basic Engineering, pages 97–106, March 1964.Google Scholar
  39. 39.
    A.R. Leach, and V.J. Gillet. An Introduction to Chemoinformatics, Kluwer, 2003.Google Scholar
  40. 40.
    A. Lingas. Subgraph isomorphism for biconnected outerplanar graphs in cubic time. Theoretical Computer Science 63, 295–302, 1989.zbMATHCrossRefMathSciNetGoogle Scholar
  41. 41.
    C.A. Lipinski, F. Lombardo, B.W. Dominy, and P. J. Feeney. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev., 23(1–3), pp. 3–25, 1997.CrossRefGoogle Scholar
  42. 42.
    D. Lizotte, T. Wang, M. Bowling, and D. Schuurmans. Automatic gait optimization with gaussian process regression. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, pages 944–949, 2007.Google Scholar
  43. 43.
    Y.C. Martin. Quantitative Drug Design: A Critical Introduction, Marcel Dekker, 1978.Google Scholar
  44. 44.
    J. Matousek and R. Thomas. On the complexity of finding iso- and other morphisms for partial k–trees. Discrete mathemathics, 108(1–3), 343–364, 1992.zbMATHCrossRefMathSciNetGoogle Scholar
  45. 45.
    P.B. Medewar. Advice to a Young Scientist. BasicBooks. 1979.Google Scholar
  46. 46.
    S. Nijssen. Mining interpretable subgraphs. In Proceedings of the International Workshop on Mining and Learning with Graphs (MLG), 2006.Google Scholar
  47. 47.
    S. Nijssen and J.N. Kok. A quickstart in frequent structure mining can make a difference. In Proceedings of the 2004 International Conference on Knowledge Discovery and Data Mining (KDD), pages 647–652. ACM Press, 2004.Google Scholar
  48. 48.
    J. Ramon and S. Nijssen. Polynomial-delay enumeration of monotonic graph classes. Journal of Machine Learning Research, 2009.Google Scholar
  49. 49.
    M. J. Sasena. Flexibility and Efficiency Enhancements for Constrained Global Design Optimization with Kriging Approximations. PhD thesis, University of Michigan, 2002.Google Scholar
  50. 50.
    A. Schierz, and R.D. King. Drugs and Drug-like compounds: Discriminating Approved Pharmaceuticals from Screening Library Compounds. In Pattern Recognition in Bioinformatics, pages 331–343, 2009.Google Scholar
  51. 51.
    L. Schietgat, J. Ramon, M. Bruynooghe, H. Blockeel. An Efficiently Computable Graph- Based Metric for the Classification of Small Molecules. In Discovery Science 2008: 197–209.Google Scholar
  52. 52.
    S. V. N. Vishwanathan, N.N. Schraudolph, I.R. Kondor, and K.M. Borgwardt. Graph Kernels. Journal of Machine Learning Research, 2009.Google Scholar
  53. 53.
    N. Wale and G. Karypis. Comparison of descriptor spaces for chemical compound retrieval and classification. In ICDM, pages 678–689. IEEE Computer Society, 2006.Google Scholar
  54. 54.
    X. Yan and J. Han. gSpan: Graph-based substructure pattern mining. In Proc. of the Second IEEE International Conference on Data Mining (ICDM), pages 721–724. IEEE Press, 2002.Google Scholar
  55. 55.
    X. Yan and J. Han. Closegraph: mining closed frequent graph patterns. In KDD, pages 286–295. ACM, 2003.Google Scholar
  56. 56.
    B. Zenko, and S. Dzeroski. Learning Classification Rules for Multiple Target Attributes. In PAKDD, pages 454–465, 2008.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Ross D. King
    • 1
    Email author
  • Amanda Schierz
    • 2
  • Amanda Clare
    • 1
  • Jem Rowland
    • 1
  • Andrew Sparkes
    • 1
  • Siegfried Nijssen
    • 3
  • Jan Ramon
    • 3
  1. 1.Department of Computer Science, Llandinam BuildingAberystwyth UniversityAberystwythUnited Kingdom
  2. 2.2DEC, Poole HouseBournemouth UniversityPooleUnited Kingdom
  3. 3.Departement ComputerwetenschappenKatholieke Universiteit LeuvenLeuvenBelgium

Personalised recommendations