TY  - JOUR
T1  - Kimyasal Bileşiklerin Tat Özelliklerinin GNN Tabanlı Modellerle Sınıflandırılması
TT  - Classification of Taste Properties of Chemical Compounds with GNN-Based Models
AU  - İsgandarli, Ulvi
AU  - Gülbandılar, Eyyüp
PY  - 2025
DA  - June
Y2  - 2025
DO  - 10.53608/estudambilisim.1700516
JF  - Eskişehir Türk Dünyası Uygulama ve Araştırma Merkezi Bilişim Dergisi
JO  - Journal of ESTUDAM Information
PB  - Eskişehir Osmangazi Üniversitesi
WT  - DergiPark
SN  - 2687-606X
SP  - 28
EP  - 34
VL  - 6
IS  - 1
LA  - tr
AB  - Özet: Bu çalışma, kimyasal bileşiklerin tat özelliklerinin (acı veya tatlı) moleküler yapılarından tahmin edilmesini ele almaktadır. Bu amaçla, bileşiklerin kanonik SMILES gösterimlerinden moleküler grafikler oluşturulmuş ve bu grafikler üzerinde GraphSAGE, Graph Convolutional Network (GCN) ve Graph Attention Network (GAT) olmak üzere üç farklı Graph Neural Network (GNN) mimarisi uygulanmıştır. Modeller, Kaggle platformundan elde edilen bir veri seti üzerinde eğitilmiş ve doğruluk, kesinlik, duyarlılık, özgüllük ve F1 skoru gibi metrikler kullanılarak değerlendirilmiştir. Sonuçlar, GNN tabanlı yaklaşımların tat sınıflandırma görevinde etkili olduğunu ve özellikle GraphSAGE modelinin %90.09 doğruluk ve 0.8772 F1 skoru ile en iyi performansı sergilediğini ortaya koymuştur. Bu bulgular, GNN tabanlı modellerin özellikle tat ile ilişkili bileşiklerin hızlı taranması ve değerlendirilmesi gibi görevlerde ilaç keşfi ve gıda bilimi gibi alanlara katkı sağlayabileceğini göstermektedir.
KW  - Kimyasal Bileşikler
KW  - Tat Sınıflandırması
KW  - Grafik Sinir Ağları
KW  - Grafik Evrişimli Ağlar
KW  - Grafik Dikkat Ağları
N2  - This study focuses on predicting the taste properties (bitter or sweet) of chemical compounds from their molecular structures. To achieve this, molecular graphs were generated from the canonical SMILES representations of the compounds, and three different Graph Neural Network (GNN) architectures GraphSAGE, Graph Convolutional Network (GCN), and Graph Attention Network (GAT) were applied to these graphs. The models were trained on a dataset obtained from the Kaggle platform and evaluated using metrics such as accuracy, precision, sensitivity, specifity and F1 score. The results demonstrated that GNN-based approaches are effective in taste classification tasks, with the GraphSAGE model showing the best performance, achieving an accuracy of 90.09% and an F1 score of 0.8772. These findings suggest that GNN-based models can contribute to fields such as drug discovery and food science, especially in tasks like the rapid screening and evaluation of compounds related to taste.
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UR  - https://doi.org/10.53608/estudambilisim.1700516
L1  - https://dergipark.org.tr/tr/download/article-file/4875622
ER  -