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EFA-DTI: Edge Feature Attention을 활용한 약물-표적 상호작용 예측
에르햄바야르 자담바(Erkhembayar Jadamba),김수헌(Sooheon Kim),이현수(Hyeonsu Lee),김화종(Hwajong Kim) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.7
Drug discovery is a high-level field of research requiring the coordination of disciplines ranging from medicinal chemistry, systems biology, structural biology, and increasingly, artificial intelligence. In particular, drug-target interaction (DTI) prediction is central to the process of screening for and optimizing candidate substances to treat disease from a nearly infinite set of compounds. Recently, as computer performance has developed dramatically, studies using artificial intelligence neural networks have been actively conducted to reduce the cost and increase the efficiency of DTI prediction. This paper proposes a model that predicts an interaction value between a given molecule and protein using a learned molecule representation via Edge Feature Attention-applied Graph Net Embedding with Fixed Fingerprints and a protein representation using pre-trained protein embeddings. The paper describes architectures, experimental methods, and findings. The model demonstrated higher performance than DeepDTA and GraphDTA, which had previously demonstrated the best performance in DTI studies.