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EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction

机译：基于EA的HyperBard Deave学习模型的高级参数优化，用于有效药物目标相互作用预测

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摘要

The identification of drug-target interactions (DTIs) is an important process in drug repositioning and drug discovery. However, it is very expensive and time-consuming to determine all possible DTIs with experimental approaches. Most existing machine learning-based methods formulate the DTIs prediction problem as a binary classification problem. Nevertheless, the lack of experimentally validated negative samples results in imbalanced class distribution within the datasets, which may have a negative influence on the DTI prediction performance. Casting DTI prediction task as a regression problem seems an interesting alternative to avoid this issue especially with the recent increase in protein structural data and DTI datasets. Within this context, a twofold contribution is described in this paper. First, we propose a novel deep learning model for predicting drug-target binding affinities called "Convolution Neural Network with Attention-based bidirectional Long Short-Term Memory network" (CNN-AbiLSTM), which combines a CNN with an attention-based biLSTM. Second, building a powerful hybrid CNN-AbiLSTM model can be highly complicated and requires a suitable setting of the model's hyper parameters. To handle this problem, we propose an evolutionary algorithm-based framework more specifically a Differential Evolution (DE) algorithm to find the optimal configuration of the proposed model. Experimental results show that the proposed DE-based CNN-AbiLSTM model achieves better performance compared with baseline methods.
机译：药物 - 靶靶相互作用（DTI）是药物排雷和药物发现中的重要过程。然而，以实验方法确定所有可能的DTI是非常昂贵和耗时的。大多数现有的基于机器学习的方法将DTI预测问题标志为二进制分类问题。然而，缺乏实验验证的负样本导致数据集内的不平衡的类分布，这可能对DTI预测性能产生负面影响。铸造DTI预测任务作为回归问题似乎是一个有趣的替代方案，避免了近期蛋白质结构数据和DTI数据集的增加。在这种情况下，本文描述了双重贡献。首先，我们提出了一种新的深度学习模型，用于预测被称为“卷积神经网络与基于注意的双向长期短期内存网络”（CNN-ABILSTM）的药物目标结合亲和力，其将CNN与基于注意的BILSTM相结合。其次，构建强大的混合CNN-ABILSTM模型可以高度复杂，需要适当的模型的超参数设置。为了处理这个问题，我们提出了一种基于进化算法的框架，更具体地说是一种差分演进（de）算法来查找所提出的模型的最佳配置。实验结果表明，与基线方法相比，所提出的基于DNN-ABILSTM模型实现了更好的性能。

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