Antimicrobial resistance has become a critical global health problem due to the abuse of conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides (AMPs) are a group of natural peptides that show promise as next-generation antibiotics due to their low toxicity to the host, broad spectrum of biological activity, including antibacterial, antifungal, antiviral, and anti-parasitic activities, and great therapeutic potential, such as anticancer, anti-inflammatory, etc. Most importantly, AMPs kill bacteria by damaging cell membranes using multiple mechanisms of action rather than targeting a single molecule or pathway, making it difficult for bacterial drug resistance to develop. However, experimental approaches used to discover and design new AMPs are very expensive and time-consuming. In recent years, there has been considerable interest in using in silico methods, including traditional machine learning (ML) and deep learning (DL) approaches, to drug discovery. While there are a few papers summarizing computational AMP prediction methods, none of them focused on DL methods. In this review, we aim to survey the latest AMP prediction methods achieved by DL approaches. First, the biology background of AMP is introduced, then various feature encoding methods used to represent the features of peptide sequences are presented. We explain the most popular DL techniques and highlight the recent works based on them to classify AMPs and design novel peptide sequences. Finally, we discuss the limitations and challenges of AMP prediction.

As an emerging distributed energy system, microgrid power flow prediction plays a crucial role in optimizing energy dispatch and power grid operation. Traditional methods of power flow prediction mainly rely on statistics and time series models, neglecting the spatial relationships among different nodes within the microgrid. To overcome this limitation, a Physical-Informed Graph Attention Network (PI-GAT) is proposed to capture the spatial structure of the microgrid, while an attention mechanism is introduced to measure the importance weights between nodes. In this study, we constructed a representative 14-node microgrid power flow dataset. After collecting the data, we preprocessed and transformed it into a suitable format for graph neural networks. Next, an autoencoder was employed for pre-training, enabling unsupervised learning-based dimensionality reduction to enhance the expressive power of the data. Subsequently, the extended data is fed into a graph convolution module with attention mechanism, allowing adaptive weight learning and capturing relationships between nodes. And integrate the physical state equation into the loss function to achieve high-precision power flow prediction. Finally, simulation verification was conducted, comparing the PI-GAT method with traditional approaches. The results indicate that the proposed model outperforms the other latest model across various evaluation indicators. Specifically, it has 46.9% improvement in MSE and 14.08% improvement in MAE.