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ACVPICPred:基于人工神经网络的抗冠状病毒的抑制活性预测

Min Li¹, Yifei Wu¹, Bowen Li¹

¹Medical College, Guizhou University, Huaxi District, Guiyang 550025, Guizhou, China.

Computational and structural biotechnology journal

|October 29, 2024

概括

本研究介绍了ACVPICPred,这是一个计算模型,使用序列和结构数据预测抗冠状病毒 (ACoVP) 活性. 整合结构信息显著提高了开发新型冠状病毒治疗的预测准确性.

科学领域:

计算生物学是一种计算生物学.
药物发现药物发现
病毒学病毒学

背景情况:

提供安全有效的冠状病毒抑制,对于开发新的抗病毒药物至关重要.
加快识别活性抗冠状病毒 (ACoVPs) 对于对抗冠状病毒疾病至关重要.
目前用于ACoVPs活动预测的方法需要提高准确性和稳定性.

研究的目的:

开发ACVPICPred,这是一个用于预测ACoVPs抑制活性的计算模型.
整合序列和结构信息,以提高预测准确度.
使用数据增强技术来增强模型的稳定性.

主要方法:

使用AlphaFold3进行结构预测.
采用生物信息学工具从序列和结构中提取特征.
应用数据增强 (噪声注入,SMOGN) 以提高数据集的稳定性.
进行了五次交叉验证以进行绩效评估.

主要成果:

该模型在培训数据集上实现了0.7668的皮尔森相关系数和0.5880的R2.
结合结构特征的模型表现出高于仅序列模型的性能.
数据增强技术提高了整体模型的稳定性.

结论:

通过整合序列和结构数据,ACVPICPred有效地预测了ACoVPs的抑制活性.
组合的序列和结构特征产生更强大的预测结果.
开发的模型及其发现有助于加速抗冠状病毒药物开发.

关键词:

抗冠状病毒是抗冠状病毒.人工神经网络的人工神经网络抑制度的抑制度是一种抑制.回归是一种回归.

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