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一个强大的集机器学习方法用于抑制剂发现:HIV-1 NNRTI的案例研究和使用MD模拟的验证

Anvesha Shree¹, Pratyush Pani^1,2, Malay Kumar Rana¹

¹Physical and Biomolecular Research Lab, Department of Chemical Sciences, Indian Institute of Science Education and Research (IISER) Berhampur, Ganjam, Odisha, 760010, India.

Chemistry, an Asian journal

|September 29, 2025

概括

我们开发了一个AI框架,以加快针对HIV-1逆转录酶 (RT) 等点的药物发现速度. 这种人工智能模型确定了一种新型抑制剂NP1,显示出新的HIV治疗方法的前景.

科学领域:

计算化学和药物发现
药理学中的人工智能
病毒学和传染病学.

背景情况:

对新型治疗药物的日益增长的需求需要有效的药物发现方法.
人工智能 (AI) 为具有成本效益和可扩展的药物识别提供了潜力.
HIV-1逆转录酶 (RT) 是抗病毒药物开发的关键目标.

研究的目的:

提出基于人工智能的整体框架,以加速小分子抑制剂的识别.
应用人工智能框架来发现HIV-1RT的抑制剂.
用计算和模拟方法验证AI识别的化合物.

主要方法:

在ChEMBL数据上训练的堆叠组合AI模型的开发.
使用人工智能模型对自然产品图谱 (NPA) 数据库进行选.
通过物理化学和ADMET过器,分子对接和1μs分子动力学 (MD) 模拟来评估潜在的抑制剂.
网络分析用于识别潜在的全性调节部位.

主要成果:

人工智能模型实现了高预测性能 (准确率为90.3%,ROC-AUC为89.4%).
化合物NP1对HIV-1 RT NNRTI结合口袋进行了稳定的结合.
在MD后的模拟中,NP1的表现优于FDA批准的药物多拉维林.
网络分析表明,涉及N136和E138.8残留物的潜在全性调节.

结论:

人工智能-MD管道为发现和重新利用抑制剂提供了一个有效的策略.
该框架具有广泛的适用性,用于识别针对各种目标的治疗剂.
化合物NP1代表了新型HIV-1RT抑制剂开发的有希望的头.

关键词:

合唱团组合在一起.艾滋病毒-1病毒.模拟MD模拟模拟MD模拟机器学习机器学习没有NNRTI,没有NRTI.

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