## 4 Methodologies in AMP Generation
Through a unique structural design, the sequence features and secondary structure information are mapped to a shared latent space, thereby generating antimicrobial peptides with ideal sequence properties and secondary structures. [PERSON] et al. trained a binary classifier network to predict antimicrobial peptides and evaluate the quality of the generated peptides [152]. They used a variational autoencoder combined with a variational attention mechanism to learn the latent space of antimicrobial peptides and generate new antimicrobial peptides. The physicochemical properties of the generated antimicrobial peptides were subsequently analysed and compared with those of real antimicrobial peptides. [PERSON] et al. proposed a conditional-based VAE model employing a denoising concept for generating novel AMPs [51]. They introduced a novel conditional denoising variational autoencoder framework tailored for AMP generation. The model incorporates explicit physicochemical property constraints (e.g., charge, hydrophobicity) as conditional inputs to guide both the encoder and decoder during training, ensuring that the generated peptides align with predefined biological requirements. To address data scarcity and noise robustness, denoising layers are integrated into the architecture, enabling the stable generation of high-fidelity AMPs even under noisy or limited training data conditions. A multiobjective loss function is designed to harmonise three critical components. The first is reconstruction loss for sequence fidelity. The second is KL divergence for latent space regularisation. The third is property preservation (PP) loss to enforce adherence to target physicochemical profiles. This unified optimisation strategy enhances the model's ability to produce functionally validated AMP candidates while maintaining structural diversity. In 2022, [PERSON] et al. proposed the feedback-AVPGAN to address the scarcity of experimentally validated antiviral peptides (AVPs), a GAN enhanced with a feedback loop for in silico AVP discovery [153]. Traditional GANs struggle with limited training data, as the discriminator relies solely on known AVPs, which are rare. Their framework innovatively integrates a transformer-based classifier into the feedback loop to iteratively refine the discriminator's training using both real and synthetic peptide sequences. They utilised ten features as inputs, including the molecular volume, molecular weight, disulphide bonds, isoelectric point (pI), gravity, aromaticity, instability index, and secondary structure fraction (\(a\)-helix, \(\beta\)-sheet, and random coil). In recent years, generative adversarial neural (GAN) networks have become a very popular architecture for generating highly realistic content [154; 155]. The GAN has two components, which are a generator and a discriminator, that compete with each other during training. The generator produces data, and the discriminator tries to distinguish the generated data from the real data. The GAN uses a competitive path and no longer requires an assumed data distribution. [PERSON] et al. proposed the PepGan method, which uses a GAN to generate active peptides and avoid overlap with inactive peptides [47]. [PERSON] et al. proposed an antimicrobial peptide generation method based on GAN v2, which uses a bidirectional conditional GAN to learn data-driven priors and control antimicrobial peptide generation via conditional variables [49; 156]. Moreover, CAMP R3 was used to verify the generated antimicrobial peptide candidate sequences, and a large proportion (89%) of the generated samples were predicted to have antimicrobial properties [81]. [PERSON] et al. proposed an AMP-GAN generation model that is based on the conditional generative adversarial network method [156]. AMP-GAN consists of three modules as follows: an encoder, a generator, and a discriminator. Among them, the encoder is a structure based on a bidirectional GAN (BiGAN), which maps the real AMP sequence to the representation of the latent space.