Bio-inspired algorithm-based hyperparameter tuning for drug-target binding affinity prediction in healthcare

被引:0
|
作者
Sharma, Moolchand [1 ]
Deswal, Suman [1 ]
机构
[1] Deenbandhu Chhotu Ram Univ Sci & Technol, Murthal, Haryana, India
来源
关键词
Drug-target; healthcare; drug-target interaction; convolution neural network; attention mechanism; bidirectional LSTM; memetic particle swarm optimization algorithm; DAVIS and KIBA dataset; MODEL; LSTM;
D O I
10.3233/IDT-230145
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The greatest challenge for healthcare in drug repositioning and discovery is identifying interactions between known drugs and targets. Experimental methods can reveal some drug-target interactions (DTI) but identifying all of them is an expensive and time-consuming endeavor. Machine learning-based algorithms currently cover the DTI prediction problem as a binary classification problem. However, the performance of the DTI prediction is negatively impacted by the lack of experimentally validated negative samples due to an imbalanced class distribution. Hence recasting the DTI prediction task as a regression problem may be one way to solve this problem. This paper proposes a novel convolutional neural network with an attention-based bidirectional long short-term memory (CNN-AttBiLSTM), a new deep-learning hybrid model for predicting drug-target binding affinities. Secondly, it can be arduous and time-intensive to tune the hyperparameters of a CNN-AttBiLSTM hybrid model to augment its performance. To tackle this issue, we suggested a Memetic Particle Swarm Optimization (MPSOA) algorithm, for ascertaining the best settings for the proposed model. According to experimental results, the suggested MPSOA-based CNN-Att-BiLSTM model outperforms baseline techniques with a 0.90 concordance index and 0.228 mean square error in DAVIS dataset, and 0.97 concordance index and 0.010 mean square error in the KIBA dataset.
引用
收藏
页码:1455 / 1474
页数:20
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