Search Results for author: Chengqiang Lu

Found 10 papers, 8 papers with code

Learning to Reweight for Graph Neural Network

no code implementations19 Dec 2023 Zhengyu Chen, Teng Xiao, Kun Kuang, Zheqi Lv, Min Zhang, Jinluan Yang, Chengqiang Lu, Hongxia Yang, Fei Wu

In this paper, we study the problem of the generalization ability of GNNs in Out-Of-Distribution (OOD) settings.

Out-of-Distribution Generalization

Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction

1 code implementation8 Jun 2023 Jiaxian Yan, Zhaofeng Ye, ZiYi Yang, Chengqiang Lu, Shengyu Zhang, Qi Liu, Jiezhong Qiu

By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels.

Drug Discovery

Knowledge Distillation of Transformer-based Language Models Revisited

no code implementations29 Jun 2022 Chengqiang Lu, Jianwei Zhang, Yunfei Chu, Zhengyu Chen, Jingren Zhou, Fei Wu, Haiqing Chen, Hongxia Yang

In the past few years, transformer-based pre-trained language models have achieved astounding success in both industry and academia.

Knowledge Distillation Language Modelling

ProtGNN: Towards Self-Explaining Graph Neural Networks

1 code implementation2 Dec 2021 Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Cheekong Lee

In this work, we propose Prototype Graph Neural Network (ProtGNN), which combines prototype learning with GNNs and provides a new perspective on the explanations of GNNs.

Motif-based Graph Self-Supervised Learning for Molecular Property Prediction

1 code implementation NeurIPS 2021 Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, Chee-Kong Lee

To bridge this gap, we propose Motif-based Graph Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif generation framework for GNNs.

Molecular Property Prediction Property Prediction +2

GraphMI: Extracting Private Graph Data from Graph Neural Networks

1 code implementation5 Jun 2021 Zaixi Zhang, Qi Liu, Zhenya Huang, Hao Wang, Chengqiang Lu, Chuanren Liu, Enhong Chen

Then we design a graph auto-encoder module to efficiently exploit graph topology, node attributes, and target model parameters for edge inference.

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction

1 code implementation7 Jul 2020 Zhongkai Hao, Chengqiang Lu, Zheyuan Hu, Hao Wang, Zhenya Huang, Qi Liu, Enhong Chen, Cheekong Lee

Here we propose a novel framework called Active Semi-supervised Graph Neural Network (ASGN) by incorporating both labeled and unlabeled molecules.

Active Learning Molecular Property Prediction +1

Molecular Property Prediction: A Multilevel Quantum Interactions Modeling Perspective

2 code implementations25 Jun 2019 Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, Lixin He

In this paper, we propose a generalizable and transferable Multilevel Graph Convolutional neural Network (MGCN) for molecular property prediction.

Graph Regression Molecular Property Prediction +1

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