## 4 Methodologies in AMP Generation  
In the early research stage, deep learning methods were usually used to design new antimicrobial peptides. Typical methods include recurrent memory networks (RNNs) [140], long short-term memory (LSTM) [141], etc. RNNs use memory units to process the entire sequence and have achieved some success in the early stage, but this method only processes one input at a time [142; 143]. Moreover, owing to the vanishing and exploding gradient characteristics of RNNs, the effect in the reference process is usually not ideal. Some researchers subsequently proposed the use of LSTM to generate antimicrobial peptides. LSTM uses a gate mechanism and a backwards loop to ensure that the error signal in the form of a gradient is not lost after processing. It is often used to process long sequences. Following a typical method, [PERSON] et al. used LSTM units to train RNNs and proposed a unique network that focuses on generating linear cationic peptides with amphipathic helices, obtaining the characteristics most relevant to antimicrobial activity [44]. Moreover, the network can be trained to predict the next amino acid at each position in the input. This method successfully designs a new antimicrobial peptide. They used the CAMP prediction tool to evaluate the antimicrobial activity of the generated sequences and compared them with the training data, random sequences, and manually designed helical sequences. The Euclidean distance between the generated sequences and the training data in the global peptide descriptor space was calculated to analyse the similarity. Finally, a helical wheel plot was generated to visualise the amphipathic helical structure of the top-ranked sequences. [PERSON] et al. used the LSTM model proposed by et al. in 2018 to generate a wider range of antimicrobial peptide sequences by modifying the input dataset [144]. They first obtained 3100 antimicrobial peptide sequences with lengths of more than seven amino acid residues from the APD3 database as a dataset and performed model training and sequence generation. The generated sequences were then screened via the CAMP AMP prediction tool, and sequences predicted to have a high probability of being antimicrobial peptides (greater than 0.950) via three algorithms, namely, support vector machine, random forest, and discriminant analysis, were selected [68]. Furthermore, 35 sequences with activity against specific microbial species were screened via the algorithm proposed by [PERSON] et al. [145]. Finally, five peptide sequences were selected for synthesis and experimental verification. The AlphaFold algorithm was used to model the spatial structure of the synthetic peptides, which were subsequently compared with known antimicrobial peptides [146]. The HAPPENN classifier was used to predict their hemolytic potential and perform molecular dynamics simulations to study their interactions with bacterial membranes [147]. Another typical method is to use the variational autoencoder (VAE) method [148]. The VAE consists of an encoder and a decoder. The encoder converts molecules into latent vector representations. The latent representation in the decoder attempts to recreate the input molecule. Owing to its good performance, VAE has been widely used in new antimicrobial peptides [148]. Following the typical method, [PERSON] et al. proposed two generative architectures based on the VAE method [52; 53]. They used the VAE to design antimicrobial peptides from scratch. The model was trained on a large number of antimicrobial peptide sequences, and they experimentally verified that the peptides they generated had antimicrobial activity. [PERSON] et al. proposed an improved VAE method to guide the trained classifier [149]. Finally, the method tested 20 generated sequences and achieved excellent performance. [PERSON] et al. proposed the LSSAMP model, which is composed of an encoder, a decoder, a multiscale VQ-VAE, and a prior model [150; 151].