可解释的人工智能驱动的预测流感神经氨基酶抑制剂使用堆叠集体学习框架
Ittipat Meewan1, Nalini Schaduangrat2, Lawankorn Mookdarsanit3
1Center for Advanced Therapeutics, Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, 73170, Thailand.
Computers in biology and medicine
|November 22, 2025
概括
一种新的计算方法,XAI-NAI,准确预测神经氨基酶抑制剂 (NAI) 活性,加速发现有效的流感治疗方法. 这种方法有助于识别用于打击耐药性的新型NAI.
科学领域:
- 计算化学和化学信息学
- 抗病毒药物发现的发现.
- 在药理学中的机器学习.
背景情况:
- 流感仍然是一个重要的全球健康问题,神经氨基酶抑制剂 (NAI) 是关键的抗病毒药物.
- 新兴的耐药菌株和病毒变异降低了当前NAI的有效性,需要开发新型化合物.
- 传统的药物发现是缓慢而昂贵的,这凸显了对高效的计算方法的需求.
研究的目的:
- 开发一个精确的,可解释的计算方法 (XAI-NAI) 以快速准确地预测NAI活动.
- 通过高效和成本效益的计算方法,加快新一代国家银行机构的发现.
- 在FDA批准的药物中通过in silico药物重新定位来识别潜在的NAI.
主要方法:
- 利用了21个分子描述符和嵌入式来表示NAI.
- 采用了六种机器学习 (ML) 方法来构建126个基础回归器,生成126维特征向量.
- 应用了两步特征选择和堆叠策略,使用元回归器进行优化预测.
主要成果:
- 与传统的ML模型和已发表的预测模型相比,XAI-NAI表现出卓越的预测性能.
- 在一个独立的测试组中,获得了0.750的R2,0.831的RMSE和0.576的MAE.
- 在FDA批准的药物中使用XAI-NAI结合分子对接和动力学模拟成功确定了潜在的NAI.
结论:
- XAI-NAI是一个有效的计算工具,用于预测NAI活动和加速药物发现.
- 该方法为查和识别新型国家药物感染因子以解决耐药性提供了一个有希望的途径.
- XAI-NAI可以支持寻找流感和其他潜在疾病的新抗病毒药物的努力.
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