Theoretical and applied potential of artificial intelligence and machine learning in analysing molecular data
| dc.contributor.author | Avcu, Fatih Mehmet | |
| dc.date.accessioned | 2026-04-04T13:14:44Z | |
| dc.date.available | 2026-04-04T13:14:44Z | |
| dc.date.issued | 2025 | |
| dc.department | İnönü Üniversitesi | |
| dc.description.abstract | This article examines the theoretical potential and applications of artificial intelligence (AI) and machine learning (ML) in molecular analysis. AI and ML techniques allow accelerating and improving the accuracy of chemical and biological processes. In particular, these methods are used to predict the chemical structure, biological activity and protein structure of molecules. In this article, we discuss how various data types such as molecular dynamics simulations, spectroscopy and cheminformatics data can be processed with AI and ML algorithms. It also highlights the revolutionary contributions of deep learning algorithms in areas such as molecular representations, drug design and protein structure prediction. The effectiveness of reinforcement learning and graph-based models in the prediction and optimization of chemical reactions is also discussed. In conclusion, the use of AI and ML in molecular analyses is expected to expand into broader areas of scientific and industrial research in the future. | |
| dc.identifier.doi | 10.51435/turkjac.1607205 | |
| dc.identifier.endpage | 70 | |
| dc.identifier.issn | 2687-6698 | |
| dc.identifier.issue | 1 | |
| dc.identifier.startpage | 61 | |
| dc.identifier.trdizinid | 1302133 | |
| dc.identifier.uri | https://doi.org/10.51435/turkjac.1607205 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/tr/yayin/detay/1302133 | |
| dc.identifier.uri | https://hdl.handle.net/11616/107445 | |
| dc.identifier.volume | 7 | |
| dc.indekslendigikaynak | TR-Dizin | |
| dc.institutionauthor | Avcu, Fatih Mehmet | |
| dc.language.iso | en | |
| dc.relation.ispartof | Turkish Journal of Analytical Chemistry (Online) | |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_TR_20250329 | |
| dc.subject | Bilgisayar Bilimleri | |
| dc.subject | Yapay Zeka | |
| dc.title | Theoretical and applied potential of artificial intelligence and machine learning in analysing molecular data | |
| dc.type | Article |
