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LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach.

Wenjing QiuQianle LiangLiyi YuXuan XiaoWangren QiuWeizhong Lin
Published in: Current pharmaceutical design (2024)
Moreover, LSTM-SAGDTA obtained superior accuracy over current state-of-the-art methods only by using less training time. The results of experiments suggest that this method represents a highprecision solution for the DTA predictor.
Keyphrases
  • neural network
  • working memory
  • emergency department
  • mass spectrometry
  • capillary electrophoresis
  • solid state