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Updated: Jun 7, 2025

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对分子ADMET属性预测和可靠的不确定性量化进行形式化图形学习

Peiyao Li^1,2, Lan Hua², Zhechao Ma³

¹Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China.

Journal of chemical information and modeling

|November 21, 2024

概括

这项研究引入了合规融合回归 (CFR),这是一个新的图形神经网络 (GNN) 模型,用于预测药物吸收,分布,新陈代谢,分泌和毒性 (ADMET) 特性. CFR提高了预测可靠性,并量化了候选药物的不确定性.

科学领域:

计算化学是一种计算化学.
药理学药理学是指药理学的学科.
机器学习是机器学习.

背景情况:

药物发现是昂贵的,在描述ADMET属性的过程中,成本很大.
图形神经网络 (GNN) 在 silico ADMET 预测中得到了改进,但在不确定性量化方面遇到了困难.
数据量和质量影响GNN性能,使不确定性估计至关重要,特别是对域外化合物.

研究的目的:

引入一种新的GNN模型,即合规融合回归 (CFR),用于强大的ADMET属性预测和不确定性量化.
解决基于GNN的ADMET模型中可靠量化预测不确定性的关键挑战.

主要方法:

开发了合规化融合回归 (CFR),将一个GNN与联合平均量位回归损失集成在一起.
在不确定性量化中采用基于集合的合规预测 (CP) 方法.
在各种ADMET预测任务中评估了框架.

主要成果:

CFR实现了准确的ADMET预测.
该框架证明了可靠的概率校准.
产生了高质量的预测间隔,优于现有的不确定性量化方法.

结论:

CFR为可靠的in silico ADMET预测和不确定性量化提供了一个有希望的方法.
该模型提高了预测的可靠性,特别是在新药或域外候选药物方面.
这项工作通过提供可靠的不确定性估计,推动了GNN在药物发现中的应用.

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