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Review
. 2022 Jun;26(3):1875-1892.
doi: 10.1007/s11030-021-10264-w. Epub 2021 Oct 20.

The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds

Arif Mermer  1
Affiliations
Review

The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds

Arif Mermer. Mol Divers. 2022 Jun.

Abstract

Machine learning (ML) methods have attracted increasing interest in chemistry as in all fields of science in recent years. This method is of great importance for the design of targeted bioactive compounds, especially by avoiding loss of time, money, and chemicals. There are lots of online web-based platforms such as LibSVM and OCHEM for the application of ML methods. In this paper, it has been examined the literature data on the activity predictions of heterocyclic compounds, biological activity results such as antiurease, HIV-1 Integrase, E. Coli DNA Gyrase B, and antifungal, pharmacophore-based studies, synthesis, and finding possible inhibitors using different machine learning methods.

Keywords: Biological activity; Heterocyclic compound; Machine Learning; QSAR; Statistical coefficient.

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References

    1. Kubat M (2017) An Introduction to Machine Learning. Springer International Publishing Second Edition, Coral Gables, FL, USA - DOI
    1. Ekins S, Puhl AC, Zorn KM, Lane TR, Russo DP, Klein JJ, Hickey AJ, Clark AM (2019) Exploiting machine learning for end-to-end drug discovery and development. Nat Mater 18:435–441. https://doi.org/10.1038/s41563-019-0338-z - DOI - PubMed - PMC
    1. Berhanu WM, Pillai GG, Oliferenko AA, Katritzky AR (2012) Quantitative Structure-Activity/Property Relationships: the ubiquitous links between cause and effect. ChemPlusChem 77:507–517. https://doi.org/10.1002/cplu.201200038 - DOI
    1. Bosc N, Atkinson F, Felix E, Gaulton A, Hersey A, Leach AR (2019) Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery. J Cheminform 11:4. https://doi.org/10.1186/s13321-018-0325-4 - DOI - PubMed - PMC
    1. Neves BJ, Braga RC, Melo-Filho CC, Moreira-Filho JT, Muratov EN, Andrade CH (2018) QSAR-based virtual screening: advances and applications in drug discovery. Front Pharmacol 9:1275. https://doi.org/10.3389/fphar.2018.01275 - DOI - PubMed - PMC

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