Duy Huynh Anh, Srisongkram Tarapong
Graduate School in the Program of Research and Development in Pharmaceuticals, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
Department of Health Sciences, College of Natural Sciences, Can Tho University, Can Tho 900000, Vietnam.
Int J Mol Sci. 2025 Jun 26;26(13):6159. doi: 10.3390/ijms26136159.
Dengue virus (DENV) remains a critical global health challenge, with no approved antiviral treatments currently available. The growing prevalence of DENV infections highlights the urgent need for effective therapeutics. Antiviral peptides (AVPs) have gained significant attention due to their potential to inhibit viral replication. However, traditional drug discovery methods are often time-consuming and resource-intensive. Advances in artificial intelligence, particularly deep generative models (DGMs), offer a promising approach to accelerating AVP discovery. This report provides a comprehensive assessment of the role of DGMs in identifying novel AVPs for DENV. It presents an extensive survey of existing antimicrobial and AVP datasets, peptide sequence feature representations, and the integration of DGMs into computational peptide design. Additionally, in vitro and in silico screening data from previous studies highlight the therapeutic potential of AVPs against DENV. Variational autoencoders and generative adversarial networks have been extensively documented in the literature for their applications in AVP generation. These models have demonstrated a remarkable capacity to generate diverse and structurally viable compounds, significantly expanding the repertoire of potential antiviral candidates. Additionally, this report assesses both the strengths and limitations of DGMs, providing valuable insights for guiding future research directions. As a data-driven and scalable framework, DGMs offer a promising avenue for the rational design of potent AVPs targeting DENV and other emerging viral pathogens, contributing to the advancement of next-generation therapeutic strategies.
登革病毒（DENV）仍然是一项严峻的全球健康挑战，目前尚无获批的抗病毒治疗方法。DENV感染的日益流行凸显了对有效治疗方法的迫切需求。抗病毒肽（AVP）因其抑制病毒复制的潜力而备受关注。然而，传统的药物发现方法通常既耗时又耗费资源。人工智能的进步，特别是深度生成模型（DGM），为加速AVP的发现提供了一种很有前景的方法。本报告全面评估了DGM在识别针对DENV的新型AVP中的作用。它对现有的抗菌和AVP数据集、肽序列特征表示以及将DGM整合到计算肽设计中进行了广泛的调查。此外，先前研究的体外和计算机模拟筛选数据突出了AVP对DENV的治疗潜力。变分自编码器和生成对抗网络在文献中已被广泛记录其在AVP生成中的应用。这些模型已证明具有生成多样且结构可行的化合物的显著能力，极大地扩展了潜在抗病毒候选物的范围。此外，本报告评估了DGM的优势和局限性，为指导未来的研究方向提供了有价值的见解。作为一个数据驱动且可扩展的框架，DGM为合理设计针对DENV和其他新兴病毒病原体的强效AVP提供了一条有前景的途径，有助于推动下一代治疗策略的发展。
