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Drug Design and Discovery: Theory, Applications, Open Issues and Challenges

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Metaheuristics in Machine Learning: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 967))

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  • The original version of this chapter was revise. The author M. Hassaballah’s affiliation has been updated with new affiliation. The correction to this chapter can be found at https://doi.org/10.1007/978-3-030-70542-8_31

Abstract

Cheminformatics has the major research factors that lead to the importance of similarity measurements for drugs and increase the chemical compound on databases search. The previous method is used for predicting the design of drugs and is comparatively efficient and less weak. Drug design is the process for finding new medications depended on the collected knowledge about a biological target. This chapter introduces several Metaheuristics Algorithms (MA), and Machine Learning (ML) techniques are used to design and discover new drug compounds in Cheminformatics. The balance of exploration and exploitation processes of MA’s are convenient for selecting the significant features as preprocessing to ML step for accomplishing high classification accuracy to the active chemical compound. In consequence, the results of the drug designed aim an optimal compound and enhanced the chemical descriptor.

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Change history

  • 11 October 2021

    The original version of the book was published with incorrect affiliation for the author M. Hassaballah. Affiliation has been updated with correct affiliation for the following chapters:

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Authors and Affiliations

  1. Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein & Waleed M. Mohamed

  2. Faculty of Computers and Information, Luxor University, Luxor, Egypt

    Mosa E. Hosney

  3. Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadalajara, Jal, Mexico

    Diego Oliva & No Ortega-Sánchez

  4. Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, 83523, Egypt

    M. Hassaballah

Authors
  1. Essam H. Houssein
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  2. Mosa E. Hosney
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  3. Diego Oliva
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  4. No Ortega-Sánchez
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  5. Waleed M. Mohamed
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  6. M. Hassaballah
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Correspondence to Essam H. Houssein .

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Editors and Affiliations

  1. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Diego Oliva

  2. Department of Computer Science, Faculty of Computers and Information, Minia University, Minia, Egypt

    Essam H. Houssein

  3. Computer Sciences Department, CUCEI, University of Guadalajara, Guadajalara, Jalisco, Mexico

    Salvador Hinojosa

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Houssein, E.H., Hosney, M.E., Oliva, D., Ortega-Sánchez, N., Mohamed, W.M., Hassaballah, M. (2021). Drug Design and Discovery: Theory, Applications, Open Issues and Challenges. In: Oliva, D., Houssein, E.H., Hinojosa, S. (eds) Metaheuristics in Machine Learning: Theory and Applications. Studies in Computational Intelligence, vol 967. Springer, Cham. https://doi.org/10.1007/978-3-030-70542-8_15

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