Use of Machine Learning in Chemoinformatics

1. Use of Machine Learning in Chemoinformatics Irene Kouskoumvekaki Associate Professor December 12th, 2012 Biological Sequence Analysis course

2. Major Aspects of Chemoinformatics • Databases: Development of databases for storage and retrieval of small molecule structures and their properties. • Machine learning: Training of Decision Trees, Neural Networks, Self Organizing Maps, etc. on molecular data. • Predictions: Molecular properties relevant to drugs, virtual screening of chemical libraries, system chemical biology networks…

3. Machine Learning

18. Machine learning classifiers

19. Clustering: Self Organizing Maps Distinguishing molecules of different biological activities and finding a new lead structure

20. Clustering: Self Organizing Maps Distinguishing molecules of different biological activities and finding a new lead structure

21. Clustering: Self Organizing Maps Distinguishing molecules of different biological activities and finding a new lead structure

22. Clustering: Self Organizing Maps Distinguishing molecules of different biological activities and finding a new lead structure

23. Machine Learning

24. Machine Learning QSAR Virtual Screening Clustering Classification Molecular Structures Properties Molecular Descriptors

25. Different descriptor types • Simple feature counts (such as number of rotatable bonds or molecular weight) • Fragmental descriptors which indicate the presence or absence (or count) of groups of atoms and substructures • Physicochemical properties (density, solubility, vdWaals volume) • Topological indices (size, branching, overall shape)

26. Major Aspects of Chemoinformatics • Databases: Development of databases for storage and retrieval of small molecule structures and their properties. • Machine learning: Training of Decision Trees, Neural Networks, Self Organizing Maps, etc. on molecular data. • Predictions: Molecular properties relevant to drugs, virtual screening of chemical libraries, system chemical biology networks…

27. Quantitative Structure-Activity Relationships (QSAR) In QSAR models structural parameters (descriptors) are fitted to experimental data for biological activity (or another given property, P)

28. Prediction of Solubility, ADME & Toxicity

29. hERG Classification with SVM

30. Evaluation of the data set

31. Performance of SVM

32. Performance of SVM

33. Virtual screening • Computational techniques for a rapid assessment of large libraries of chemical structures in order to guide the selection of likely drug candidates.

34. Similarity Search • Similar Property Principle – Molecules having similar structures and properties are expected to exhibit similar biological activity. • Thus, molecules that are located closely together in the chemical space are often considered to be functionally related.

35. Fingerprints-based Similarity Search • widely used similarity search tool • consists of descriptors encoded as bit strings • Bit strings of query and database are compared using similarity metric such as Tanimoto coefficient • MACCS fingerprints: 166 structural keys • that answer questions of the type: • Is there a ring of size 4? • Is at least one F, Br, Cl, or I present? • where the answer is either • TRUE (1) or FALSE (0)

36. Tanimoto Similarity or 90% similarity

37. Similarity Search

38. Questions?

39. Molecular editors and viewers http://www.chemaxon.com/products/marvin/

40. Molecular editors and viewers http://jmol.sourceforge.net/

41. Format conversion http://cactus.nci.nih.gov/translate/