Improving Multi-Task GNNs for Molecular Property Prediction via Missing Label Imputation | |
Fenyu Hu![]() ![]() | |
发表期刊 | Machine Intelligence Research
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2023-02 | |
页码 | 1-31 |
摘要 | The prediction of molecular properties is a fundamental task in the field of drug discovery. Recently, Graph Neural Networks (GNNs) have been gaining prominence in this area. Since a molecule tends to have multiple correlated properties, there is a great need to develop the multi-task learning ability of GNNs. However, limited by expensive and time-consuming human annotations, collecting complete labels for each task is difficult. As a result, most existing benchmarks involve a lot of missing labels in training data, and the performance of GNNs is impaired for lacking enough supervision information. To overcome this obstacle, we propose to improve multi-task molecular property prediction via missing label imputation. Specifically, a bipartite graph is firstly introduced to model the molecule-task co-occurrence relationships. Then, the imputation of missing labels is transformed into predicting missing edges on this bipartite graph. To predict the missing edges, a graph neural network is devised, which can learn the complex molecule-task co-occurrence relationships. After that, we select reliable pseudo-labels according to the uncertainty of the prediction results. Boosting with enough and reliable supervision information, our approach achieves the state-of-the-art performance on a variety of real-world datasets. |
语种 | 英语 |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 智能计算与学习 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57488 |
专题 | 模式识别实验室 |
通讯作者 | Shu Wu |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Fenyu Hu,Dingshuo Chen,Qiang Liu,et al. Improving Multi-Task GNNs for Molecular Property Prediction via Missing Label Imputation[J]. Machine Intelligence Research,2023:1-31. |
APA | Fenyu Hu,Dingshuo Chen,Qiang Liu,&Shu Wu.(2023).Improving Multi-Task GNNs for Molecular Property Prediction via Missing Label Imputation.Machine Intelligence Research,1-31. |
MLA | Fenyu Hu,et al."Improving Multi-Task GNNs for Molecular Property Prediction via Missing Label Imputation".Machine Intelligence Research (2023):1-31. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
MIR-2023-02-016.R1-P(5659KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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