Article ; Online: The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds.
2021 Volume 26, Issue 3, Page(s) 1875–1892
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 ... ...
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. |
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MeSH term(s) | DNA Gyrase ; Escherichia coli ; Heterocyclic Compounds/pharmacology ; Machine Learning ; Quantitative Structure-Activity Relationship |
Chemical Substances | Heterocyclic Compounds ; DNA Gyrase (EC 5.99.1.3) |
Language | English |
Publishing date | 2021-10-20 |
Publishing country | Netherlands |
Document type | Journal Article ; Review |
ZDB-ID | 1376507-3 |
ISSN | 1573-501X ; 1381-1991 |
ISSN (online) | 1573-501X |
ISSN | 1381-1991 |
DOI | 10.1007/s11030-021-10264-w |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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