Activity Classification of Small Drug Molecules Using Deep Neural Networks and Classical Machine Learning Models
| dc.contributor.author | Kanberiz, Hatice | |
| dc.contributor.author | Korkmaz, Selçuk | |
| dc.contributor.author | Süt, Necdet | |
| dc.date.accessioned | 2024-06-12T10:05:30Z | |
| dc.date.available | 2024-06-12T10:05:30Z | |
| dc.date.issued | 2022 | |
| dc.department | Trakya Üniversitesi | en_US |
| dc.description.abstract | Objective: The main goal in the early phase of drug discovery studies is to detect small drug molecules that show activity against a specific receptor. For this purpose, small drug molecules are classified as actives or inactives by performing high-throughput screening (HTS) experiments. The datasets obtained from these experiments are uploaded to the PubChem database. This database contains more than one million bioassays that are obtained through HTS experiments. Alternatively, classification models can be developed using datasets in the PubChem database. Material and Methods: In this study, we obtained 5 datasets with different degrees of imbalance structure from the PubChem database. We trained these datasets using deep neural networks (DNN) for the classification of small drug molecules as actives or inactives. The test set performances of DNN models were compared with the support vector machines (SVM) and random forest (RF) algorithms. Results: The DNN achieved better balanced accuracy (minimum-maximum: 0.764-0.865), recall (minimum-maximum: 0.630-0.823), F1-score (minimum-maximum: 0.496-0.843) and Matthews correlation coefficient (minimum-maximum: 0.439- 0.721) compared to the SVM and RF. Conclusion: Our results showed that the DNN is a well-performed machine learning algorithm that can be in the early phase of drug discovery studies since it performs better than traditional machine learning algorithms in the case of imbalanced class structures. | en_US |
| dc.identifier.doi | 10.5336/biostatic.2022-88704 | |
| dc.identifier.endpage | 80 | en_US |
| dc.identifier.issn | 1308-7894 | |
| dc.identifier.issn | 2146-8877 | |
| dc.identifier.issue | 2 | en_US |
| dc.identifier.startpage | 70 | en_US |
| dc.identifier.trdizinid | 1125572 | en_US] |
| dc.identifier.uri | https://doi.org/10.5336/biostatic.2022-88704 | |
| dc.identifier.uri | https://search.trdizin.gov.tr/yayin/detay/1125572 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14551/13484 | |
| dc.identifier.volume | 14 | 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 | Activity Classification of Small Drug Molecules Using Deep Neural Networks and Classical Machine Learning Models | en_US |
| dc.type | Article | en_US |
