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基于机器学习的生物活性预测和描述器指导的合理设计的粉样β聚合抑制剂的粉样β聚合抑制剂

Avantika Bansal¹, Akshat Raj Sharma¹, Arya Chakraborty²

¹Advanced BioComputing Lab, Department of Bioengineering and Biotechnology, Birla Institute of Technology Mesra, Ranchi, Jharkhand 835215, India.

ACS chemical neuroscience

|November 5, 2025

概括

研究人员开发了Amylo-IC50Pred,这是一个机器学习平台,通过预测针对粉样β (Aβ) 聚合的小分子的有效性,加速发现阿尔茨海默病 (AD) 抑制剂.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
神经科学是一个神经科学.

背景情况:

阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,其特征是粉样β (Aβ) 聚合.
抑制Aβ聚合是一种关键的治疗策略,但其复杂性使抑制剂设计复杂化.
目前开发Aβ抑制剂的实验方法缓慢且资源密集.

研究的目的:

开发基于机器学习的工具,用于快速虚拟选针对Aβ聚合的小分子.
克服Aβ.常规和计算抑制剂设计的局限性.
为了加速发现新的阿尔茨海默病治疗方法.

主要方法:

开发了Amylo-IC50Pred,这是一个用户友好的网络平台,集成机器学习模型.
在584种生物验证化合物上训练了两个分类模型和一个回归模型.
利用基于Histogram的随机森林和渐变增强算法进行预测.

主要成果:

随机森林模型在区分抑制剂和诱方面实现了100%的准确性.
基于历史图的渐变增强精确地分类了抑制剂的强度,准确度为81%.
随机森林回归模型实现了0.93的高R2来预测IC50值.

结论:

艾米洛-IC50Pred为Aβ聚合抑制剂的虚拟查提供了一个快速而准确的方法.
关键的分子特性,如疏水性和形状,对于有效的Aβ抑制至关重要.
该平台是加速阿尔茨海默病药物发现的宝贵资源.

关键词:

这是阿尔茨海默氏症.一个Aβ聚合的Aβ聚合.IC50 IC50 IC50 IC50 IC50 IC50 IC50 IC50 IC50 IC50 IC50 IC50 IC50 机器学习是机器学习.分子描述器分子描述器

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