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病毒性传染病的深度药物预测框架使用基于优化器的卷积神经网络组合:COVID-19作为一个案例研究

A S Aruna^1,2, K R Remesh Babu³, K Deepthi⁴

¹Dept. of Information Technology, Government Engineering College Palakkad, APJ Abdul Kalam Technological University, Palakkad, Kerala, 678633, India. aruna.arathi@gmail.com.

Molecular diversity

|October 8, 2024

概括

这项研究介绍了CGDVDA,这是一种用于预测针对COVID-19等新病毒威胁的现有药物的AI方法. 它通过分析药物结构和病毒基因组,有效地识别潜在的治疗方法,加速药物的重新用途.

科学领域:

计算生物学是一种计算生物学.
传染病研究传染病研究.
医学中的人工智能

背景情况:

像SARS-CoV-2这样的新兴病毒威胁暴露了人类对流行病的脆弱性.
开发针对疾病的新型治疗方法是耗时且资源密集的.
药物重新定位通过重新定位现有疗法提供了更快的替代方案.

研究的目的:

开发一种有效的计算方法来预测病毒与药物之间的关联.
加快对包括COVID-19在内的病毒性传染病的潜在治疗方法的识别.
在面对新出现的微生物威胁时,利用人工智能进行药物重新用途.

主要方法:

开发了一个集体方法,CGDVDA,集成卷积神经网络 (CNN),遗传算法 (GA) 和深森林分类器.
特性载体是通过结合药物化学结构和病毒基因组序列相似性来生成的.
用CNN提取深层特征,通过GA进行优化,并通过深层森林分类器进行分类.

主要成果:

CGDVDA模型在五倍交叉验证时实现了高性能,ROC-AUC得分为0.9159.
该方法在预测COVID-19和其他病毒性疾病的药物中表现出有效性.
对比分析显示,CGDVDA在预测病毒与药物相关性方面表现优于最先进的方法.

结论:

CGDVDA是一种有效的计算工具,用于预测新型病毒与药物之间的关联.
该研究强调了人工智能驱动的药物重新定位对抗病毒传染病的潜力.
开发的方法可以显著加快对新兴病毒流行病的治疗方法的发现.

关键词:

在 COVID-19 疫情中,卷积神经网络是一种卷积神经网络.森林深处的森林深处的森林.药物重新定位是药物重新定位.遗传算法遗传算法遗传算法

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