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Computational Toxicology pp 327–345Cite as

Computational Approaches in Multitarget Drug Discovery

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1800))

Abstract

Current therapeutic strategies entail identifying and characterizing a single protein receptor whose inhibition is likely to result in the successful treatment of a disease of interest, and testing experimentally large libraries of small molecule compounds “in vitro” and “in vivo” to identify promising inhibitors in model systems and determine if the findings are extensible to humans. This highly complex process is largely based on tests, errors, risk, time, and intensive costs. The virtual computational study of compounds simulates situations predicting possible drug linkages with multiple protein target atomic structures, taking into account the dynamic protein inhibitor, and can help identify inhibitors efficiently, particularly for complex drug-resistant diseases. Some discussions will relate to the potential benefits of this approach, using HIV-1 and Plasmodium falciparum infections as examples. Some authors have proposed a virtual drug discovery that not only identifies efficient inhibitors but also helps to minimize side effects and toxicity, thus increasing the likelihood of successful therapies. This chapter discusses concepts and research of bioactive multitargets related to toxicology.

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Author information

Authors and Affiliations

  1. Postgraduate Program in Natural Products and Synthetic Bioactive, Federal University of Paraíba, João Pessoa, PB, Brazil

    Luciana Scotti & Marcus T. Scotti

  2. Teaching and Research Management - University Hospital, Federal University of Paraíba, João Pessoa, PB, Brazil

    Luciana Scotti

  3. University of Western São Paulo (Unoeste), Presidente Prudente, SP, Brazil

    Hamilton Mitsugu Ishiki

  4. Federal University of Sergipe, Sergipe, Brazil

    Marcelo Cavalcante Duarte & Tiago Branquinho Oliveira

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  1. Luciana Scotti
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  2. Hamilton Mitsugu Ishiki
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  3. Marcelo Cavalcante Duarte
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  4. Tiago Branquinho Oliveira
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  5. Marcus T. Scotti
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  1. Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy

    Orazio Nicolotti

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Scotti, L., Ishiki, H.M., Duarte, M.C., Oliveira, T.B., Scotti, M.T. (2018). Computational Approaches in Multitarget Drug Discovery. In: Nicolotti, O. (eds) Computational Toxicology. Methods in Molecular Biology, vol 1800. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7899-1_16

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  • DOI: https://doi.org/10.1007/978-1-4939-7899-1_16

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