This article systematically reviews the development history of antiviral drug discovery, with a focus on target-based strategies, the application of artificial intelligence and nanotechnology, and potential future directions, providing a comprehensive reference for research in the field.
Literature Overview
The article 'Antiviral drug discovery and development: challenges and future directions,' published in the journal Signal Transduction and Targeted Therapy, reviews and summarizes the evolution of antiviral drugs from early viral inhibitors to modern broad-spectrum antiviral agents. The article systematically outlines the latest advances in target-based drug discovery strategies, mechanism-driven innovative approaches, and pharmacokinetic optimization. It deeply explores the application of artificial intelligence, medicinal chemistry tools, and nanotechnology in antiviral drug development, and provides perspectives on emerging directions such as targeting membraneless organelles (e.g., liquid-liquid phase separation). Additionally, the article analyzes challenges in antiviral drug development, including high viral mutation rates, drug resistance, and difficulties in clinical translation, emphasizing the importance of multidisciplinary collaboration. The entire passage is coherent and logical, ending with a Chinese period.Background Knowledge
Antiviral drug development is a core strategy for combating viral infectious diseases. In recent years, although vaccines have played a crucial role in preventing infections, the importance of antiviral drugs in treating and controlling outbreaks has become increasingly prominent, especially in the face of SARS-CoV-2 variants that reduce vaccine efficacy. Currently, antiviral drugs primarily act by targeting key steps in the viral life cycle (such as attachment, entry, replication, assembly, and release) or by modulating host factors to inhibit viral replication. Typical drug targets include viral polymerases, proteases, integrases, and neuraminidases. Representative drugs such as remdesivir, nirmatrelvir, and molnupiravir have been widely used in the treatment of COVID-19. However, the high mutation rate of viruses easily leads to drug resistance, and the lack of common targets across different virus types makes the development of broad-spectrum antiviral drugs challenging. Moreover, although host targets may reduce the risk of resistance, they could result in higher toxicity. In recent years, artificial intelligence (AI) has shown great potential in protein structure prediction, target identification, and bioactivity prediction, while nanotechnology (such as lipid nanoparticles in mRNA vaccines) plays a key role in drug delivery and vaccine development. Although over a hundred antiviral drugs have been approved, drug development still faces challenges such as high failure rates, high costs, and inconsistencies between animal models and human disease phenotypes. How to integrate multi-omics data, leverage AI to accelerate molecular design, and develop more precise disease models are current research priorities. This article, set against this backdrop, systematically summarizes the progress and challenges in antiviral drug development, providing strategic guidance for future innovation.
Research Methods and Experiments
The article adopts a review-based research methodology, systematically revisiting milestone events since the approval of the first antiviral drug, idoxuridine, in 1963, and outlining key developmental stages in antiviral drug discovery, including early nucleoside analogs, selective prodrugs, antiretroviral drugs, and the application of computer-aided drug design (CADD). The article details the principles, advantages, and limitations of two major strategies in drug discovery—phenotypic drug discovery and target-based drug discovery (TBDD)—and illustrates their applications in antiviral drug development with examples (e.g., RYL-634, INS018_055). The authors also analyze virus-host interaction networks, covering viral targets, host targets, and mechanisms by which viruses antagonize host innate immunity, discussing the roles of key signaling pathways such as IFN, RIG-I, cGAS-STING, and TLR in antiviral immunity. Furthermore, the article systematically describes drug screening technologies, including virtual screening, high-throughput screening, and AI-driven screening, emphasizing multidimensional approaches for target identification and validation, such as gene knockout, protein inhibitor experiments, and evaluation in cellular and animal models. Finally, drawing on lessons from the COVID-19 pandemic, the authors summarize the challenges and future directions in antiviral drug development, including emerging strategies such as AI, nanotechnology, and targeting liquid-liquid phase separation.Key Conclusions and Perspectives
Research Significance and Prospects
This article provides a comprehensive summary of the history, current status, and future directions of antiviral drug development, offering researchers a strategic reference. By reviewing the strengths and weaknesses of different drug discovery strategies, it emphasizes the importance of integrating phenotypic and target-driven approaches, promoting a deeper understanding from 'phenomenon' to 'essence.' The article highlights the empowering roles of AI and nanotechnology, suggesting that future drug development will increasingly rely on data-driven approaches and precise delivery.
Looking ahead, targeting host factors and virus-host interaction networks will be key to overcoming drug resistance. In particular, exploring emerging mechanisms such as liquid-liquid phase separation may reveal entirely new dimensions of viral replication regulation. Additionally, building animal models that more closely mimic human pathology (e.g., humanized mice) and leveraging real-world data to optimize clinical trial design will improve the success rate of drug translation. Integration of multi-omics analysis and enhanced interpretability of AI models will also advance the development of personalized antiviral therapies. Ultimately, interdisciplinary collaboration and technological innovation will be the core drivers of breakthroughs in antiviral drug development.
Conclusion
This article comprehensively reviews the development history of antiviral drug research, systematically elaborating the evolutionary path from early nucleoside analogs to modern broad-spectrum antiviral drugs, with a focus on analyzing phenotypic and target-based drug discovery strategies and their applications in the development of anti-COVID-19 drugs. The article points out that although over a hundred antiviral drugs have been approved, high viral mutation rates, drug resistance, and clinical translation difficulties continue to severely constrain research efficiency. The integration of artificial intelligence and nanotechnology brings revolutionary opportunities to drug design and delivery, while targeting virus-host interaction networks and emerging mechanisms such as liquid-liquid phase separation offers entirely new strategies for overcoming drug resistance. In the future, antiviral drug development must place greater emphasis on interdisciplinary integration, combining AI prediction, high-throughput screening, and humanized model validation to enhance the druggability and translational success of candidate molecules. Simultaneously, strengthening global collaboration, sharing data resources, and optimizing clinical trial design will help accelerate the transition of innovative drugs from the laboratory to the clinic, providing a solid foundation for应对 future pandemic threats. This review offers systematic strategic guidance and cutting-edge perspectives for researchers in the antiviral field, making it a valuable reference.
