## 6 Discussion and Conclusions  
This paper systematically examines the components of video-driven AI-based AMP generation frameworks, encompassing the following three critical dimensions: datasets, feature encoding methods, methodologies in AMP generation, and challenges and limitations. For the dataset part, we introduced AMP, non-AMP, and real peptide databases. AMP databases are generally curated repositories (e.g., APD3, DRAMP) and provide experimentally validated AMP sequences for model training. Non-AMP generation method synthetic non-AMP datasets, created via rule-based sampling or adversarial training, enhance discrimination capability by contrasting functional/nonfunctional sequences. Real peptide databases can augment training with non-AMP natural peptides (e.g., UniProt), improving model generalisability and evaluation robustness. In the Feature Encoding Methods section, we introduce the following three categories: mapping-based methods, physicochemical-based methods, and secondary structure-based methods. Bidirectional sequence-feature translation (e.g., one-hot encoding, k-mer tokenisation) ensures interpretability and reversibility, linking abstract vectors to biophysically meaningful sequences. Physicochemical-based methods involve the quantitative profiling of hydrophobicity, charge, and amphipticity via tools such as Peptides (R package) or PyBioMed to determine structure-activity relationships. The secondary structure-based methods involve residue-level conformation analysis (\(\alpha\)-helix, \(\beta\)-sheet, and coil) via algorithms to capture structural determinants of antimicrobial function. The methodologies in the AMP generation part introduced AMP generative models, including architectures of RNNs, VAEs, GANs, and diffusions. RNNs can autoregress sequences with long short-term memory (LSTM) networks. VAEs explore the latent space with encoder-decoder frameworks. GANs train adversarial information for high-fidelity AMP synthesis. Diffusion model iterative denoising for diverse, property-constrained generation. In the challenges and limitations section, data scarcity, the lack of unified evaluation standards, translational gaps, and scalability are discussed. The synergy of AI and experimental biology holds transformative potential for addressing antimicrobial resistance. Emerging paradigms, such as diffusion models with structural conditioning and hybrid neurosymbolic frameworks, offer avenues to overcome current limitations. Success will require interdisciplinary collaboration, merging computational innovation with rigorous biochemical validation to advance next-generation peptide therapeutics. Furthermore, we discuss the strengths, weaknesses, and future directions of the video-driven AI methods that were reviewed. Video-driven AI methods, such as GANs, VAEs, and diffusion models, have demonstrated remarkable performance in capturing complex patterns and generating high-quality outputs. These methods can leverage large-scale video data to learn robust feature representations, which can be beneficial for AMP predictive modelling. Despite their strengths, these methods face several challenges when applied to AMP predictive modelling. The limitations identified in Section 5, such as data scarcity, model interpretability, and computational efficiency, highlight the need for tailored solutions. To address the identified gaps and challenges, we suggest several future research directions. These include developing data augmentation techniques to expand AMP datasets, exploring interpretable AI methods to enhance model transparency, and optimising model architectures for computational efficiency. [PERSON] conceived the research framework, designed the content structure, and wrote the original draft of the review. [PERSON] conducted the literature collection and analysis. [PERSON] contributed to the material collection and grammar checking. [PERSON] focused on comparative studies and case analysis during the writing and revision processes.