https://scholars.lib.ntu.edu.tw/handle/123456789/638638
標題: | LE-DTA: Local Extrema Convolution for Drug Target Affinity Prediction | 作者: | Langore, Tanoj Hsu, Te Cheng Hsieh, Yi Hsien CHE LIN |
關鍵字: | Deep network architecture | drug-target interaction | graph neural network | local extrema | 公開日期: | 1-一月-2023 | 來源出版物: | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | 摘要: | One of the essential parts of drug discovery and design is the prediction of drug-target affinity (DTA). Researchers have proposed computational approaches for predicting DTA to circumvent the more expensive in vivo and in vitro tests. More recent approaches employed deep network architectures to obtain the features from the drug molecules and protein sequences. The drug compounds are represented as graphs and the target protein as a sequence to extract this information. In this work, we develop a new graph-based prediction model, termed LE-DTA, that utilizes local extrema convolutions for effective feature extraction. It focuses on the local and global extrema of graphs for node embedding. We investigated the performances of both the proposed models on three different benchmark datasets. Our proposed model showed improvement in CI by 1.12% and 0.35% and a reduction in MSE by 7.7% and 3.33% on the KIBA and BindingDB datasets, respectively. we also showed that despite using various pooling operations on our proposed model, we achieved an average reduction in MSE by 7% on the KIBA dataset and 3% improvement on the BindingDB dataset. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/638638 | ISBN: | 978-1-7281-6327-7 | ISSN: | 15206149 | DOI: | 10.1109/ICASSP49357.2023.10096347 |
顯示於: | 電機工程學系 |
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