MOTIVATION: Predicting protein-ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and better highlighting active sites are also significant challenges. RESULTS: We propose an innovative neural network model called DEAttentionDTA, based on dynamic word embeddings and a self-attention mechanism, for predicting protein-ligand binding affinity. DEAttentionDTA takes the 1D sequence information of proteins as input, including the global sequence features of amino acids, local features of the active pocket site, and linear representation information of the ligand molecule in the SMILE format. These three linear sequences are fed into a dynamic word-embedding layer based on a 1D convolutional neural network for embedding encoding and are correlated through a self-attention mechanism. The output affinity prediction values are generated using a linear layer. We compared DEAttentionDTA with various mainstream tools and achieved significantly superior results on the same dataset. We then assessed the performance of this model in the p38 protein family. AVAILABILITY AND IMPLEMENTATION: The resource codes are available at https://github.com/whatamazing1/DEAttentionDTA.

Backbone network is the core part of the Internet. Due to the high transmission speed of traffic in the backbone network, Quality of Service (QoS) monitoring of services in the backbone network becomes a highly important and challenging issue. Traffic classification is the basis of QoS monitoring. The existing traffic classification is based on full traffic, which is impractical in high-speed backbone network traffic. This paper presents a method to classify the sampled traffic and gives an example of its application in QoS monitoring. Specifically, we design the Multiple Counter Sketch (MC Sketch) to quickly extract features from the sampled data stream in a backbone, propose the Batch Classifier based on Agglomerative Clustering (BCAC) for unsupervised clustering of traffic, and combine with the supervised machine learning method to train the labeled data in the clustering results to get the classification model. The experimental results of sampled traffic collected on a 10Gbps link show that even when the sampling ratio is 1:1024, the accuracy of our classification model reaches 96.3%. When different block sizes are set, the average clustering time of BCAC is only about one-third of the traditional agglomerative classifier. Moreover, we give an example of applying our traffic classification method to monitor the QoS, and the results show that our method can efficiently and accurately monitor the QoS dynamics of backbone network traffic.