EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction
作者：
Highlights：
• A contribution to drug repurposing using deep learning and optimization is proposed.
• A novel deep learning model is developed to predict Drug-Target binding affinities.
• The proposed model integrates a Convolutional Neural Network on the top of a BiLSTM.
• The Hyperparameters of the model are set using a Differential Evolution algorithm.
• The Differential Evolution-based learning framework outperformed baseline methods.
摘要
•A contribution to drug repurposing using deep learning and optimization is proposed.•A novel deep learning model is developed to predict Drug-Target binding affinities.•The proposed model integrates a Convolutional Neural Network on the top of a BiLSTM.•The Hyperparameters of the model are set using a Differential Evolution algorithm.•The Differential Evolution-based learning framework outperformed baseline methods.
论文关键词：Drug-Target binding affinity prediction,Convolutional Neural Network,Bidirectional LSTM,Attention mechanism,Differential Evolution algorithm
论文评审过程：Received 13 October 2020, Revised 29 June 2021, Accepted 29 June 2021, Available online 8 July 2021, Version of Record 23 July 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115525
