CogDL: A Comprehensive Library for Graph Deep Learning

1 code implementation • 1 Mar 2021 • Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang

In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.

Graph Classification Graph Embedding +5

MolFM: A Multimodal Molecular Foundation Model

2 code implementations • 6 Jun 2023 • Yizhen Luo, Kai Yang, Massimo Hong, Xing Yi Liu, Zaiqing Nie

In this study, we introduce MolFM, a multimodal molecular foundation model designed to facilitate joint representation learning from molecular structures, biomedical texts, and knowledge graphs.

Cross-Modal Retrieval Knowledge Graphs +4

DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection

1 code implementation • CVPR 2022 • Haibao Yu, Yizhen Luo, Mao Shu, Yiyi Huo, Zebang Yang, Yifeng Shi, Zhenglong Guo, Hanyu Li, Xing Hu, Jirui Yuan, Zaiqing Nie

Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities.

3D Object Detection Autonomous Driving +1

Towards Unified AI Drug Discovery with Multiple Knowledge Modalities

no code implementations • 17 Apr 2023 • Yizhen Luo, Xing Yi Liu, Kai Yang, Kui Huang, Massimo Hong, Jiahuan Zhang, Yushuai Wu, Zaiqing Nie

In recent years, AI models that mine intrinsic patterns from molecular structures and protein sequences have shown promise in accelerating drug discovery.

Drug Discovery Knowledge Graphs +2

DeepCRE: Transforming Drug R&D via AI-Driven Cross-drug Response Evaluation

no code implementations • 6 Mar 2024 • Ting Zhang, Hao Zhou, Hainan Wu, Hanwen Sunchu, Lei Hu, Xiaofang Chen, Suyuan Zhao, Gaochao liu, Chao Sun, Jiahuan Zhang, Yizhen Luo, Peng Liu, Zaiqing Nie, Yushuai Wu

The fields of therapeutic application and drug research and development (R&D) both face substantial challenges, i. e., the therapeutic domain calls for more treatment alternatives, while numerous promising pre-clinical drugs have failed in clinical trials.