Small Drug Molecule Classification Using Deep Neural Networks
| dc.contributor.author | Korkmaz, Selçuk | |
| dc.date.accessioned | 2024-06-12T10:05:30Z | |
| dc.date.available | 2024-06-12T10:05:30Z | |
| dc.date.issued | 2019 | |
| dc.department | Trakya Üniversitesi | en_US |
| dc.description.abstract | Objective: Early phase of drug discovery studies include a virtual screening phaseof detecting active molecules among a large number of small drug molecules. The number ofpublicly available datasets for drug molecules are growing exponentially every year thanks to thedatabases, such as PubChem and ChEMBL. Therefore, there is a strong need for analyzing andretrieving useful information from these datasets using automated processes. For this purpose,machine learning algorithms are often used for activity prediction of small drug compounds, since they are faster and comparatively cheaper. Deep neural networks has emerged as a powerfulmachine learning method with great advantages to deal with high-dimensional big datasets. Material and Methods: In this study, we applied different settings of deep neural networks modelsto reveal the effects of learning rate, batch size and minority class weight on performance of thenetwork. Results: Small learning rate and large batch size are found to be the most importantfactors that improve performance of the deep neural network. The best performed model yielded89% accuracy and 0.78 area under the curve value. Conclusion: Findings of this study is promising for use of deep neural networks in virtual screening of small drug compounds from publiclyavailable databases. | en_US |
| dc.identifier.doi | 10.5336/biostatic.2019-64948 | |
| dc.identifier.endpage | 101 | en_US |
| dc.identifier.issn | 1308-7894 | |
| dc.identifier.issn | 2146-8877 | |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.startpage | 93 | en_US |
| dc.identifier.trdizinid | 334293 | en_US] |
| dc.identifier.uri | https://doi.org/10.5336/biostatic.2019-64948 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/yayin/detay/334293 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14551/13483 | |
| dc.identifier.volume | 11 | en_US |
| dc.indekslendigikaynak | TR-Dizin | en_US |
| dc.language.iso | en | en_US |
| dc.relation.ispartof | Türkiye Klinikleri Biyoistatistik Dergisi | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.title | Small Drug Molecule Classification Using Deep Neural Networks | en_US |
| dc.type | Article | en_US |
