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ReGen-DTI:一种新的生成药物向相互作用模型,用于预测针对SARS-COV2的潜在候选药物

Kaushik Bhargav Sivangi¹, Santhosh Amilpur¹, Chandra Mohan Dasari¹

¹Indian Institute of Information Technology, Sri City, Chittoor, 517646, Andhra Pradesh, India.

Computational biology and chemistry

|July 27, 2023

概括

这项研究引入了一种新的深度学习方法,用于发现新的COVID-19药物. 该方法产生具有高结合亲和力与SARS-CoV-2主蛋白酶的独特分子,性能优于现有的治疗方法.

科学领域:

计算化学是一种计算化学.
人工智能在药物发现中的作用
分子建模分子建模

背景情况:

由于COVID-19的流行,需要快速开发有效的抗病毒疗法.
深度学习和强化学习为探索药物发现中的广化学空间提供了强大的工具.
鉴定SARS-CoV-2主要蛋白酶 (3CLPro) 的新型抑制剂对于治疗干预至关重要.

研究的目的:

开发一种生成的深度学习模型,用于针对SARS-CoV-2主蛋白酶的新药候选物生成.
评估产生的分子的有效性,独特性和新性.
使用基于深度学习的药物向相互作用模型,预测候选分子与3CLPro的结合亲和力.

主要方法:

使用强化学习的生成方法,以采样针对SARS-CoV-2的新分子 3CLPro.
在针对特定目标活跃的现有重定向分子上微调生成模型.
开发和应用基于深度学习的药物向相互作用 (DTI) 模型来预测结合亲和力.

主要成果:

在微调后,生成模型产生了具有高有效性 (92.71%),独特性 (93.55%) 和新性 (100%) 的分子.
拟议的DTI模型准确地预测了结合亲缘关系,大多数生成的分子显示得分<100nM.
与Remdesivir等商业药物相比,生成的分子显示出明显更高的预测结合亲和力.

结论:

提出的生成深度学习框架有效地设计了针对SARS-CoV-2具有高潜力的新型药物候选者.
综合DTI模型为评估药物向相互作用提供了一种有效的替代传统对接方法.
这种方法加速了对SARS-CoV-2主要蛋白酶的强效抑制剂的发现,提供了有前途的治疗线索.

关键词:

这就是Covid-19的原因.深度神经网络是一个神经网络.药物发现药物发现药物向相互作用药物向相互作用生成型模型是一种生成型模型.强化学习是一种强化学习.

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