The role of machine learning method in the synthesis and biological ınvestigation of heterocyclic compounds
Molecular Diversity, cilt.26, sa.3, ss.1875-1892, 2022 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Derleme
- Cilt numarası: 26 Sayı: 3
- Basım Tarihi: 2022
- Doi Numarası: 10.1007/s11030-021-10264-w
- Dergi Adı: Molecular Diversity
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE
- Sayfa Sayıları: ss.1875-1892
- Anahtar Kelimeler: Machine Learning, Heterocyclic compound, Biological activity, Statistical coefficient, QSAR
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
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. Graphic abstract: [Figure not available: see fulltext.].