The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds.
Arif MermerPublished in: Molecular diversity (2021)
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.
Keyphrases
- machine learning
- big data
- artificial intelligence
- antiretroviral therapy
- hiv positive
- systematic review
- public health
- human immunodeficiency virus
- hepatitis c virus
- hiv infected
- escherichia coli
- deep learning
- social media
- molecular dynamics
- molecular docking
- electronic health record
- hiv testing
- hiv aids
- single molecule
- healthcare
- cancer therapy
- health information
- cell free
- drug delivery