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Cheminformatics Approaches in Modern Drug Discovery

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Drug Design: Principles and Applications

Abstract

The large amount of costs, time, effort and failures involved in the process of drug discovery and development made it difficult for the researchers to discover drugs and prompted the need for methods which could improve the productivity and efficiency of drug design. Cheminformatics is an emerging field which acts as an interface between chemistry and computers and helps in processing, managing and analysis of large chemical information using computer methods. In this chapter, we have outlined the applications of cheminformatics in the field of drug discovery, such as identification of lead compounds, virtual library generation, high throughput screening and data mining, prediction of biological activities of compounds and in silico ADMET prediction. Various cheminformatics approaches that include data mining, representation of chemical compounds via descriptors, similarity and substructures searching and classification algorithms have also been discussed.

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References

  1. Xu J, Hagler A (2002) Chemoinformatics and drug discovery. Molecules 7:566–600

    Article  CAS  Google Scholar 

  2. Hecht P (2002) High-throughput screening: beating the odds with informatics-driven chemistry. Curr Drug Discov:21–24

    Google Scholar 

  3. Gallop MA, Barrett RW, Dower WJ, Fodor SP, Gordon EM (1994) Applications of combinatorial technologies to drug discovery. 1. Background and peptide combinatorial libraries. J Med Chem 37:1233–1251

    Article  CAS  PubMed  Google Scholar 

  4. Brown FK (1998) Chemoinformatics: what is it and how does it impact drug discovery. Annu Rep Med Chem 33:375–384

    Article  CAS  Google Scholar 

  5. Engel T (2006) Basic overview of chemoinformatics. J Chem Inf Model 46:2267–2277

    Article  CAS  PubMed  Google Scholar 

  6. Hann M, Green R (1999) Chemoinformatics—a new name for an old problem? Curr Opin Chem Biol 3:379–383

    Article  CAS  PubMed  Google Scholar 

  7. Gasteiger J, Engel T (2006) Chemoinformatics: a textbook. Wiley

    Google Scholar 

  8. James CA Cheminformatics 101. An introduction to the computer science and chemistry of chemical information systems. eMolecules Inc., Del Mar

    Google Scholar 

  9. Todeschini R, Consonni V (2008) Handbook of molecular descriptors, vol 11. Wiley, NewYork

    Google Scholar 

  10. Valla A, Giraud M, Dore JC (1993) Descriptive modeling of the chemical structure-biological activity relations of a group of malonic polyethylenic acids as shown by different pharmacotoxicologic tests. Pharmazie 48:295–301

    CAS  PubMed  Google Scholar 

  11. Liu K, Feng J, Young SS (2005) Power MV: a software environment for molecular viewing, descriptor generation, data analysis and hit evaluation. J Chem Inf Model 45:515–522

    Article  CAS  PubMed  Google Scholar 

  12. Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32:1466–1474

    Article  CAS  PubMed  Google Scholar 

  13. Mitchell JB (2014) Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci 4:468–481

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Alpaydin E (2014) Introduction to machine learning. MIT Press, Cambridge

    Google Scholar 

  15. Daumé H (2012) A course in machine learning (ciml.Info), p. 189

  16. Brown RD, Martin YC (1996) Use of structure−activity data to compare structure-based clustering methods and descriptors for use in compound selection. J Chem Inf Comput Sci 36:572–584

    Article  CAS  Google Scholar 

  17. Mitchell TM (1997) Machine learning. McGraw-Hill Science/Engineering/Math, Maidenhead, p. 432

    Google Scholar 

  18. Simon P (2013) Too big to ignore: the business case for big data. Wiley, Hoboken, p. 89

    Google Scholar 

  19. Mitchell JBO (2014) Machine learning methods in chemoinformatics. Wiley Interdiscip Rev Comput Mol Sci 4:468–481

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. So S-S, Karplus M (1997) Three-dimensional quantitative structure− activity relationships from molecular similarity matrices and genetic neural networks. 1. Method and validations. J Med Chem 40:4347–4359

    Article  CAS  PubMed  Google Scholar 

  21. Li H et al (2006) Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods. J Mol Graph Model 25:313–323

    Article  CAS  PubMed  Google Scholar 

  22. Briem H, Günther J (2005) Classifying “kinase inhibitor-likeness” by using machine-learning methods. Chembiochem 6:558–566

    Article  CAS  PubMed  Google Scholar 

  23. Jehad Ali RK, Ahmad N, Maqsood I (2012) Random forests and decision trees. Int J Comput Sci Issues 9

    Google Scholar 

  24. Marchese Robinson RL, Glen RC, Mitchell JB (2011) Development and comparison of hERG blocker classifiers: assessment on different datasets yields markedly different results. Mol Informat 30:443–458

    Article  CAS  Google Scholar 

  25. Kuz'min VE, Polishchuk PG, Artemenko AG, Andronati SA (2011) Interpretation of QSAR models based on random forest methods. Mol Informat 30:593–603

    Article  Google Scholar 

  26. Li S, Fedorowicz A, Singh H, Soderholm SC (2005) Application of the random forest method in studies of local lymph node assay based skin sensitization data. J Chem Inf Model 45:952–964

    Article  CAS  PubMed  Google Scholar 

  27. Friedman N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 29:131–163

    Article  Google Scholar 

  28. Koutsoukas A et al (2013) In silico target predictions: defining a benchmarking data set and comparison of performance of the multiclass Naïve Bayes and Parzen-Rosenblatt window. J Chem Inf Model 53:1957–1966

    Article  CAS  PubMed  Google Scholar 

  29. Cannon EO et al (2007) Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J Comput Aided Mol Des 21:269–280

    Article  CAS  PubMed  Google Scholar 

  30. von Korff M, Sander T (2006) Toxicity-indicating structural patterns. J Chem Inf Model 46:536–544

    Article  Google Scholar 

  31. Platt JCSequential minimal optimization. A fast algorithm for training support vector machines. Report no. MSR-TR-98-14, 21 (Microsoft Research), 1998)

    Google Scholar 

  32. Liao Q, Yao J, Yuan S (2007) Prediction of mutagenic toxicity by combination of recursive partitioning and support vector machines. Mol Divers 11:59–72

    Article  CAS  PubMed  Google Scholar 

  33. Kinnings SL et al (2011) A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J Chem Inf Model 51:408–419

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46:175–185

    Google Scholar 

  35. Ajmani S, Jadhav K, Kulkarni SA (2006) Three-dimensional QSAR using the k-nearest neighbor method and its interpretation. J Chem Inf Model 46:24–31

    Article  CAS  PubMed  Google Scholar 

  36. Honório KM, da Silva AB (2005) A study on the influence of molecular properties in the psychoactivity of cannabinoid compounds. J Mol Model 11:200–209

    Article  PubMed  Google Scholar 

  37. Basak SC, Grunwald GD (1995) Predicting mutagenicity of chemicals using topological and quantum chemical parameters: a similarity based study. Chemosphere 31:2529–2546

    Article  CAS  PubMed  Google Scholar 

  38. Begam BF, Kumar JS (2012) A study on cheminformatics and its applications on modern drug discovery. Proced Eng 38:1264–1275

    Article  CAS  Google Scholar 

  39. Aktar MW, Murmu S (2008) Chemoinformatics: principles and applications. 1 Pesticide Residue Laboratory, Department of Agricultural Chemicals, 2 Department of Agricultural Chemistry and Soil Science, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur-741252, Nadia, West Bengal, India.

    Google Scholar 

  40. Nantasenamat C, Isarankura-Na-Ayudhya C, Naenna T, Prachayasittikul V (2009) A practical overview of quantitative structure-activity relationship. EXCLI J 8:74–88

    Google Scholar 

  41. Walters WP, Stahl MT, Murcko MA (1998) Virtual screening—an overview. Drug Discov Today 3:160–178

    Article  CAS  Google Scholar 

  42. Diller DJ, Merz KM (2001) High throughput docking for library design and library prioritization. Proteins 43:113–124

    Article  CAS  PubMed  Google Scholar 

  43. Willett P (2000) Chemoinformatics–similarity and diversity in chemical libraries. Curr Opin Biotechnol 11:85–88

    Article  CAS  PubMed  Google Scholar 

  44. Gedeck P, Willett P (2001) Visual and computational analysis of structure–activity relationships in high-throughput screening data. Curr Opin Chem Biol 5:389–395

    Article  CAS  PubMed  Google Scholar 

  45. Halford B (2014) Reflections on CHEMDRAW. Chem Eng News 92:26–27

    Google Scholar 

  46. Park J et al (2009) Automated extraction of chemical structure information from digital raster images. Chem Cent J 3:4

    Article  PubMed  PubMed Central  Google Scholar 

  47. Hunter AD (1997) ACD/ChemSketch 1.0 (freeware); ACD/ChemSketch 2.0 and its Tautomers, Dictionary, and 3D Plug-ins; ACD/HNMR 2.0; ACD/CNMR 2.0. ACS Publications.

    Google Scholar 

  48. Steinbeck C et al (2003) The chemistry development kit (CDK): an open-source Java library for chemo-and bioinformatics. J Chem Inf Comput Sci 43:493–500

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cao Y, Charisi A, Cheng L-C, Jiang T, Girke T (2008) Chemmine R: a compound mining framework for R. Bioinformatics 24:1733–1734

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ertl P (2010) Molecular structure input on the web. J Cheminform 2(1)

    Google Scholar 

  51. O'Boyle NM et al (2011) Open babel: an open chemical toolbox. J Chem 3:33

    Article  Google Scholar 

  52. Wang Y et al (2009) PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res 37:W623–W633

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

AG is thankful to Jawaharlal Nehru University for usage of all computational facilities. AG is grateful to University Grants Commission, India for the Faculty Recharge Position. Salma Jamal acknowledges a Senior Research Fellowship from Indian Council of Medical Research (ICMR), New Delhi.

Competing Interests

The authors declare that they have no competing interests.

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Authors and Affiliations

  1. School of Biotechnology, Jawaharlal Nehru University, New Delhi, 110067, India

    Salma Jamal & Abhinav Grover

  2. Department of Bioscience and Biotechnology, Banasthali University, Tonk, Rajasthan, 304022, India

    Salma Jamal

Authors
  1. Salma Jamal
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  2. Abhinav Grover
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Correspondence to Abhinav Grover .

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Editors and Affiliations

  1. School of Biotechnology, Jawaharlal Nehru University, New Delhi, India

    Abhinav Grover

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Jamal, S., Grover, A. (2017). Cheminformatics Approaches in Modern Drug Discovery. In: Grover, A. (eds) Drug Design: Principles and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-10-5187-6_9

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