Browsing by Author "Onay, Aytun"

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Classification of Nervous System Withdrawn and Approved Drugs With Toxprint Features Via Machine Learning Strategies
(Elsevier Ireland Ltd, 2017) Onay, Aytun; Onay, Melih; Abul, Osman
Background and objectives: Early-phase virtual screening of candidate drug molecules plays a key role in pharmaceutical industry from data mining and machine learning to prevent adverse effects of the drugs. Computational classification methods can distinguish approved drugs from withdrawn ones. We focused on 6 data sets including maximum 110 approved and 110 withdrawn drugs for all and nervous system diseases to distinguish approved drugs from withdrawn ones. Methods: In this study, we used support vector machines (SVMs) and ensemble methods (EMs) such as boosted and bagged trees to classify drugs into approved and withdrawn categories. Also, we used CORINA Symphony program to identify Toxprint chemotypes including over 700 predefined chemotypes for determination of risk and safety assesment of candidate drug molecules. In addition, we studied nervous system withdrawn drugs to determine the key fragments with The ParMol package including gSpan algorithm. Results: According to our results, the descriptors named as the number of total chemotypes and bond CN_amine_aliphatic_generic were more significant descriptors. The developed Medium Gaussian SVM model reached 78% prediction accuracy on test set for drug data set including all disease. Here, bagged tree and linear SVM models showed 89% of accuracies for phycholeptics and psychoanaleptics drugs. A set of discriminative fragments in nervous system withdrawn drug (NSWD) data sets was obtained. These fragments responsible for the drugs removed from market were benzene, toluene, N,N-dimethylethylamine, crotylamine, 5-methyl-2,4-heptadiene, octatriene and carbonyl group. Conclusion: This paper covers the development of computational classification methods to distinguish approved drugs from withdrawn ones. In addition, the results of this study indicated the identification of discriminative fragments is of significance to design a new nervous system approved drugs with interpretation of the structures of the NSWDs. (C) 2017 Elsevier B.V. All rights reserved.
A Drug Decision Support System for Developing a Successful Drug Candidate Using Machine Learning Techniques
(Bentham Science Publ Ltd, 2020) Onay, Aytun; Onay, Melih
Background: Virtual screening of candidate drug molecules using machine learning techniques plays a key role in pharmaceutical industry to design and discovery of new drugs. Computational classification methods can determine drug types according to the disease groups and distinguish approved drugs from withdrawn ones. Introduction: Classification models developed in this study can be used as a simple filter in drug modelling to eliminate potentially inappropriate molecules in the early stages. In this work, we developed a Drug Decision Support System (DDSS) to classify each drug candidate molecule as potentially drug or non-drug and to predict its disease group. Methods: Molecular descriptors were identified for the determination of a number of rules in drug molecules. They were derived using ADRIANA.Code program and Lipinski's rule of five. We used Artificial Neural Network (ANN) to classify drug molecules correctly according to the types of diseases. Closed frequent molecular structures in the form of subgraph fragments were also obtained with Gaston algorithm included in ParMol Package to find common molecular fragments for withdrawn drugs. Results: We observed that TPSA, XlogP Natoms, HDon_O and TPSA arc the most distinctive features in the pool of the molecular descriptors and evaluated the performances of classifiers on all datasets and found that classification accuracies are very high on all the datasets. Neural network models achieved 84.6% and 83.3% accuracies on test sets including cardiac therapy, anti-epileptics and anti-parkinson drugs with approved and withdrawn drugs for drug classification problems. Conclusion: The experimental evaluation shows that the system is promising at determination of potential drug molecules to classify drug molecules comedy according to the types of diseases.
Enhancing the Content of Phycoerythrin Through the Application of Microplastics From Porphyridium Cruentum Produced in Wastewater Using Machine Learning Methods
(Academic Press Ltd- Elsevier Science Ltd, 2024) Onay, Aytun; Onay, Melih
Microalgae can produce secondary metabolites like phycoerythrin (Phy). The effects of some microplastics (MPs), wastewater (WW), and light intensity (LI) parameters, including complex data sets, on Phy concentration from Porphyridium cruentum were investigated using machine learning methods in this study. Also, the deep learning (DL) model was developed to get the maximum phy concentration from the dataset. The dataset (232 data groups), including a feature set, polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), WW, LI, and an output variable, Phy, were randomly divided into training and test sets to create and evaluate the models. The highest experimental and predicted Phy concentrations were 52.3 mg/g and 58.32 mg/ g in a scenario with 15% WW, 80 mg/L PE, PP, PS, and PVC, and a LI of 175 mu molm- 2 s-1, respectively. The Pearson correlation coefficient (r) indicates a positive correlation between Phy and the variables PE (r = 0.35), PVC (r = 0.69), PP (r = 0.27), PS (r = 0.29), and LI (r = 0.22). However, variables such as WW (r = -0.05) have a weak correlation, and while PVC and PE showed the most significant effect on Phy concentration, WW had the lowest effect. Furthermore, LIME (local interpretable model-agnostic explanations) and SHAP (shapley additive explanations) provided us with important results for interpreting the random forest regression (RF) and DL models' predictions, respectively. The LIME and SHAP analyses suggest that the system with more PVC has a higher predicted Phy value. For WW, the reverse is true; higher WW values result in lower Phy predictions. Researchers were given the model explainability decision tree (DT) structure to study reactants' effects on output (Phy). In conclusion, the dye industry can use microalgae to treat WW contaminated with MPs while also producing high amounts of Phy using a DL model.
Modeling of Biomass Productivity and Lipid Percentage From Chlorella Saccharophila Via Response Surface Method Using Matlab
(2021) Beyter, Tuba; Ayas, Zehra Şapcı; Onay, Aytun
Yenilenemeyen enerji kaynaklarının kontrolsüz tüketilmesi çevresel sorunların hızla artmasına neden olur. Mikroalglerin karbon, azot, fosfor gibi kirletici parametreleri substrat olarak kullanmaları çevresel sorunların giderilmesi için önem taşımaktadır. Çalışmamızda Chlorella Saccharophila'dan maksimum biyokütle ve yağ verimliliğini elde etmek için farklı fosfor, karbon, azot, sülfür ve demir konsantrasyonlarıyla ilgili biyokütle ve yağ verimliliği üzerindeki değişiklikleri belirlemek amacıyla EDTA (10-200 mg/L), KH2PO4 (15-300 mg/L), NaNO3 (60-1000 mg/L), ZnSO4.7H2O (2-36 mg/L) ve FeSO4.7H2O (1-20 mg/L) konsantrasyonları incelendi. Proses değişkenlerinin birbiri arasındaki etkileşim etkisi cevap yüzeyi yöntemi ile belirlendi. Minimum biyokütle 1.019 g/L, maksimum biyokütle ise 2.187 olarak bulundu. Maksimum biyokütle EDTA(X1) 100 mg/L, KH2PO4(X2) 175 mg/L, NaNO3(X3) 500 mg/L, ZnSO4.7H2O(X4) 4 mg/L ve FeSO4.7H2O(X5) 2 mg/L parametre değerlerinde bulundu. Deneysel ve teorik hesaplamalarda elde edilen maksimum biyokütle değeri aynı parametre değerlerinde elde edildi. Lipit yüzdesinin hesaplamalarında da minimum %29.76, maksimum % 37.61 değeri elde edildi bu değer deneysel olarak bulunan maksimum lipit yüzdesi (38.9%) değeriyle uyum içindedir. Maksimum lipit yüzdesi EDTA(X1) 25 mg/L, KH2PO4(X2) 175 mg/L, NaNO3 (X3) 120 mg/L, ZnSO4.7H2O(X4) 4 mg/L ve FeSO4.7H2O(X5) 2 mg/L parametre değerlerinde elde edildi. Bu değer teorik olarak elde edilen maksimum lipit yüzdesi girdi parametreleri ile aynıdır.