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E-GuARD:由专家指导的增强功能,用于稳定检测干扰生物测试的化合物

Vincenzo Palmacci^1,2, Yasmine Nahal^3,4, Matthias Welsch^1,2,5

¹Department of Pharmaceutical Sciences, Division of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna, 1090, Vienna, Austria.

Journal of cheminformatics

|April 29, 2025

概括

对药物发现的测试干扰是一个挑战. 一个新的框架E-GuARD使用自蒸和主动学习来改进预测模型,增强早期药物发现.

科学领域:

计算化学是一种计算化学.
药物发现药物发现
机器学习机器学习

背景情况:

来自小型有机化合物的测试干扰阻碍了早期的药物发现.
现有的用于识别干扰化合物的计算方法受到数据稀缺和不平衡的限制.

研究的目的:

介绍E-GuARD,这是一个解决数据稀缺和在预测测试验干扰方面的不平衡的新框架.
提高定量结构干扰关系 (QSIR) 模型的性能和适用性.

主要方法:

E-GuARD集成了自我蒸,主动学习和专家指导的分子生成.
该框架以代的方式将干扰相关分子添加到训练数据中.
应用于关于硫醇反应性,氧化还原反应性和化酶抑制的数据集.

主要成果:

E-GuARD实现了卓越的性能,模型达到MCC值高达0.47.
与基线模型相比,在丰富因子上有两倍或更高的改善.
在不同的干扰机制中始终优于标准方法.

结论:

E-GuARD提供了一个可扩展的解决方案,以减轻早期药物发现中的测试干扰.
该框架通过从新生成的信息化合物中学习来提高QSIR模型的预测性能.
建立了动态数据丰富和强大的分子建模的基础.

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