## 4 Methodologies in AMP Generation
The generator is responsible for generating new AMP sequences via a conditional generative adversarial network (CGAN), which means that the generator takes into account the given conditional vector when generating sequences, such as the sequence length, microbial target, target mechanism, and MIC50 value. The goal of the generator is to produce sequences that can deceive the discriminator. This helps the generator optimise its output through adversarial training so that it can generate more realistic peptide sequences. In 2019, [PERSON] et al. proposed a feedback generative adversarial network (FBGAN) for optimising the DNA coding sequence of antimicrobial peptides [157]. They proposed a new method to optimise the gene sequences encoding antimicrobial peptides by combining feedback generative adversarial networks (FBGANs) with DNA sequence optimisation technology. This method focuses on improving the functional and structural quality of antimicrobial peptides and further improving their antimicrobial activity. The performance of AMP-encoding genes was evaluated, and the AMP analyser was trained on 2600 AMPs and 2600 random peptides from the APD3 database. Next, the edit distance between the generated AMPs and known AMPs was calculated to evaluate sequence similarity and diversity. Moreover, the physicochemical properties (such as length, molecular weight, charge, etc.) of the generated protein and known AMPs were compared. Finally, the generated peptides were folded, and the edit distances between the generated sequence after feedback and the natural sequence were compared. Diffusion is a popular and powerful framework that is widely utilised in generating areas [158; 159]. Diffusion models are a family of generative algorithms that learn to synthesise data by gradually denoising random noise through an iterative process. Inspiredby thermodynamics, they consist of a forward process, which incrementally adds noise to the data until it becomes Gaussian, and a reverse process, where a neural network is trained to reconstruct the original data by predicting and removing noise stepwise. Unlike GANs or VAEs, diffusion models excel in generating high-fidelity, diverse outputs while avoiding mode collapse, leveraging gradient-based learning for stable training. They have revolutionised domains such as image generation (e.g., DALL-E 2 [160], stable diffusion [161]), audio synthesis, and molecular design, with extensions such as guided diffusion enabling precise control over generated properties (e.g., antimicrobial activity and structural motifs [162]). The ability of these peptides to balance quality and diversity makes them particularly powerful for biomedical applications, such as the design of novel peptides with tailored functional characteristics. [PERSON] et al. addressed AMP diversity via a text-guided conditional denoising diffusion (TG-CDDPM) model for generating novel AMPs [163]. The TG-CDDPM model is a three-stage framework leveraging denoising diffusion principles to generate novel, homologous AMPs with enhanced diversity to overcome the limitations in cost, scalability, and diversity of traditional AMP generation models, including LSTMs, VAEs, and GANs. [PERSON] et al. proposed an integrated deep learning framework, Diff-AMP, which can automatically complete the generation, recognition, attribute prediction, and iterative optimisation of AMPs [54]. They innovatively incorporate dynamic diffusion and attention mechanisms into the reinforcement learning framework for efficient processing.