计算机科学
人工智能
机器学习
人工神经网络
可靠性(半导体)
过程(计算)
领域(数学分析)
图形
限制
数据挖掘
理论计算机科学
工程类
数学
机械工程
功率(物理)
数学分析
物理
操作系统
量子力学
作者
Fan Fan,Gang Wu,Yining Yang,Liu Fu,Qian Yi,Qingmiao Yu,Hongqiang Ren,Jinju Geng
标识
DOI:10.1021/acs.est.3c04571
摘要
The application of deep learning (DL) models for screening environmental estrogens (EEs) for the sound management of chemicals has garnered significant attention. However, the currently available DL model for screening EEs lacks both a transparent decision-making process and effective applicability domain (AD) characterization, making the reliability of its prediction results uncertain and limiting its practical applications. To address this issue, a graph neural network (GNN) model was developed to screen EEs, achieving accuracy rates of 88.9% and 92.5% on the internal and external test sets, respectively. The decision-making process of the GNN model was explored through the network-like similarity graphs (NSGs) based on the model features (FT). We discovered that the accuracy of the predictions is dependent on the feature distribution of compounds in NSGs. An AD characterization method called ADFT was proposed, which excludes predictions falling outside of the model’s prediction range, leading to a 15% improvement in the F1 score of the GNN model. The GNN model with the AD method may serve as an efficient tool for screening EEs, identifying 800 potential EEs in the Inventory of Existing Chemical Substances of China. Additionally, this study offers new insights into comprehending the decision-making process of DL models.
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