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产生深度学习的混合框架用于抗病毒的发现

Huynh Anh Duy^1,2, Tarapong Srisongkram³

¹Graduate School in the Program of Research and Development in Pharmaceuticals, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen, 40002, Thailand.

Scientific reports

|July 15, 2025

概括

研究人员开发了一种混合AI模型,将WGAN-GP和BiLSTM结合起来,以发现新的抗病毒 (AVP). 这种强大的工具成功识别了815个AVP,加速了针对病毒感染的新型基疗法的开发.

科学领域:

计算化学和生物信息学
药物的发现和开发.
传染病研究传染病研究.

背景情况:

抗病毒 (AVP) 显示出治疗病毒感染的希望,但它们的发现很困难.
目前用于识别AVP的现有方法有限,阻碍了治疗的发展.

研究的目的:

开发和验证一种混合计算模型,以有效地发现新型抗病毒.
为了利用先进的机器学习技术来增强生成和分类.

主要方法:

一个混合模型,将瓦斯斯坦生成对抗网络与梯度惩罚 (WGAN-GP) 集成为生成和双向长短期记忆 (BiLSTM) 网络进行分类.
严格验证BiLSTM模型,包括应用领域和特征重要性分析.
对生成的序列的多样性和功能性进行WGAN-GP模型的全面评估.

主要成果:

BiLSTM模型在识别AVPs方面表现出可靠的性能.
该WGAN-GP模型成功生成了多样化和功能性序列.
混合模型确定了815个新型AVP,在不同的病毒标中丰富度有显著的变化,特别是流感A病毒的高.

结论:

混合WGAN-GP和BiLSTM方法是加速抗病毒发现的强大工具.
该研究提供了对AVP特征的宝贵见解,并确定了有前途的候选人进行进一步研究.
开发了一个公开可访问的平台 (https://avp-predictor.streamlit.app),以帮助研究人员优先考虑候选AVP.

关键词:

抗病毒的发现.生物信息学是一种生物信息学.深度学习是一种深度学习.

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