###### Abstract
In recent years, with the rapid development of artificial intelligence and bioinformatics technology, research on antimicrobial peptide generation models has made significant progress [43]. For example, deep learning-based recurrent neural networks (RNNs) [44; 45; 46], generative adversarial networks (GANs) [47; 48; 49], variational autoencoders (VAEs) [50; 51; 52; 53], and diffusion models [54; 55] have been widely used in the generation and optimisation of antimicrobial peptide sequences. These models are able to learn features from many known antimicrobial peptide sequences and generate new sequences with potential antimicrobial activity. In addition, researchers have used many existing machine learning- or deep learning-based methods to evaluate the antimicrobial activity and toxicity of generated sequences [56]. For example, classifiers are used to distinguish whether a given peptide chain is an antimicrobial peptide [57; 58; 59; 60]; regression models are used to predict the minimum inhibitory concentration of an antimicrobial peptide against a certain target (the higher the minimum inhibitory concentration, the weaker the antimicrobial peptide is against the corresponding target, and vice versa) [61; 62; 63]; and machine learning- or deep learning-based classifiers are used to predict the toxicity of candidate peptides by integrating amino acid sequence and structural information [64; 65; 66] to quickly screen out candidate sequences with high potential. Although significant progress has been made in the research of antimicrobial peptide generation models, several challenges remain, including the scarcity of high-quality real data of antimicrobial peptides, the integration of multilabel features, and the verification of the effectiveness of the generation model. Among them, the scarcity of high-quality real data of antimicrobial peptides refers to the limited high-quality experimental data of antimicrobial peptides, which restricts the training and verification of the model. The selection and integration of multilabel features specifically refers to how to select specific features (such as length, charge, and hydrophobicity) and how to integrate the above multiple features of antimicrobial peptides to optimise the input data of the generation model and improve the diversity and specificity of the generated sequence. The validation of the generative model can be used not only to verify the validity of the generative model but also to select and optimise the generative model, which includes three factors. The first is experimental validation and optimisation, that is, to verify the activity and specificity of the generated antimicrobial peptides through various computer simulation experiments--the appropriate model architecture is selected, and the model parameters are further optimised. The second is enhancing the generalisation ability of the model. The existing model may have overfitting problems when new antimicrobial peptides are generated, resulting in poor results in practical applications. The third is multiobjective optimisation. The design of antimicrobial peptides needs to consider multiple factors, such as antimicrobial activity, toxicity, and stability. A way to achieve multiobjective optimisation is an important challenge. In addition, we elaborate on how the general principles of AI methodologies, such as GANs, VAEs, and diffusion models, are specifically tailored and applied to the context of AMP generation. This includes detailed discussions on how these architectures can capture the unique features and patterns in biological data, thereby increasing the accuracy and efficiency of AMP prediction and design. AMPs are emerging as promising alternatives to conventional antibiotics because of their broad-spectrum activity, low resistance risk, and unique membrane-disrupting mechanisms. However, traditional experimental methods for AMP discovery face challenges such as high costs, time-consuming synthesis, and limited exploration of the vast chemical space.