In the past few decades, chronic hepatitis B caused by hepatitis B virus (HBV) has been one of the most serious diseases to human health. The development of innovative systems is essential for preventing the complex pathogenesis of hepatitis B and reducing side effects caused by drugs. HBV inhibitory drugs have been developed through various compounds, and they are often limited by routine experimental screening and delay drug development. More recently, virtual screening of compounds has gradually been used in drug research with strong computational capability and is further applied in anti-HBV drug screening, thus facilitating a reliable drug screening process. However, the lack of structural information in traditional compound analysis is an important hurdle for unsatisfactory efficiency in drug screening. Here, a natural language processing technique was adopted to analyze compound simplified molecular input line entry system strings. By using the targeted optimized word2vec model for pretraining, we can accurately represent the relationship between the compound and its substructure. The machine learning model based on training results can effectively predict the inhibitory effect of compounds on HBV and liver toxicity. The reliability of the model is verified by the results of wet-lab experiments. In addition, a tool has been published to predict potential compounds. Hence, this article provides a new perspective on the prediction of compound properties for anti-HBV drugs that can help improve hepatitis B diagnosis and further develop human health in the future.
Sentiment analysis based on social media text is found to be essential for multiple applications such as project design, measuring customer satisfaction, and monitoring brand reputation. Deep learning models that automatically learn semantic and syntactic information have recently proved effective in sentiment analysis. Despite earlier studies’ good performance, these methods lack syntactic information to guide feature development for contextual semantic linkages in social media text. In this paper, we introduce an enhanced LSTM-based on dependency parsing and a graph convolutional network (DPG-LSTM) for sentiment analysis. Our research aims to investigate the importance of syntactic information in the task of social media emotional processing. To fully utilize the semantic information of social media, we adopt a hybrid attention mechanism that combines dependency parsing to capture semantic contextual information. The hybrid attention mechanism redistributes higher attention scores to words with higher dependencies generated by dependency parsing. To validate the performance of the DPG-LSTM from different perspectives, experiments have been conducted on three tweet sentiment classification datasets, sentiment140, airline reviews, and self-driving car reviews with 1,604,510 tweets. The experimental results show that the proposed DPG-LSTM model outperforms the state-of-the-art model by 2.1% recall scores, 1.4% precision scores, and 1.8% F1 scores on sentiment140.
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