针对登革热病毒的抗病毒的发现的深度生成模型:系统审查
Huynh Anh Duy1,2, Tarapong Srisongkram3
1Graduate School in the Program of Research and Development in Pharmaceuticals, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
International journal of molecular sciences
|July 12, 2025
概括
深度生成模型 (DGMs) 通过利用人工智能加速对登革热病毒 (DENV) 新型抗病毒 (AVPs) 的发现. 这种方法为开发新的DENV疗法提供了一个可扩展的解决方案.
科学领域:
- 生物技术是生物技术.
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 登革热病毒 (DENV) 构成了严重的全球健康威胁,目前没有抗病毒治疗.
- 抗病毒 (AVP) 显示出抑制病毒复制的潜力,但传统的发现进展缓慢.
- 人工智能,特别是深度生成模型 (DGM),为AVP发现提供了更快的途径.
研究的目的:
- 评估DGM在识别针对DENV的新型AVP中的实用性.
- 为了调查现有的AVP数据集,特征表示和设计中的DGM集成.
- 评估AVP与DENV之间的潜力,使用体外和体内数据.
主要方法:
- 审查现有的抗微生物和AVP数据集.
- 对序特征表示的分析.
- 集成深度生成模型 (DGM),包括变异自动编码器和生成对抗网络,用于AVP生成.
- 对AVP疗效的体外和内查数据的评估.
主要成果:
- DGMs,如VAE和GAN,可以产生多样化和可行的化合物.
- 这些模型显著扩大了DENV.的潜在抗病毒候选者池.
- 现有的数据支持AVPs对DENV的治疗潜力.
结论:
- DGM提供了一个数据驱动和可扩展的框架,用于针对DENV的合理AVP设计.
- 这种人工智能驱动的方法加速了下一代抗病毒疗法的发展.
- DGM对发现针对DENV和其他新兴病毒病原体的AVP具有前景.
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