张旭东

张旭东

Tongji University

Hey there! I’m currently a second-year Ph.D. student in Computer Science and Technology at Tongji University, supervised by Prof. Guang Chen, the head of the Robotics & Embodied AI Lab. My research focuses on AI for drug discovery, with specific interests in (1) structure-based molecular generation, (2) generative models, and (3) reinforcement learning.

📣 News

🎉 2025.02: Our work (RCP-Bench) on collaborative perception is accepted by CVPR2025!

🎉 2024.12: We won the championship in The Second Global AI Drug Development Algorithm Competition!

🎉 2024.11: Our work (AMG) on 3D structure-based molecular generation is accepted by Briefings in Bioinformatics!

🎉 2022.09: Our work on medical image segmentation is accepted by IEEE Journal of Biomedical and Health Informatics (IF: 7.7)!

🎉 2022.07: Our work (Molormer) on drug-drug interaction prediction is accepted by Briefings in Bioinformatics (IF: 13.994)!

🎉 2021.12: Our work on drug-drug interaction prediction is accepted by Briefings in Bioinformatics (IF: 13.994)!

📝 Publications

Deep reinforcement learning as an interaction agent to steer fragment-based 3D molecular generation for protein pockets
Xudong Zhang, Jing Hou, Sanqing Qu, Fan Lu, Zhixin Tian, Yanping Zhang, Guang Chen, Alois Knoll, Shaorong Gao
Briefings in Bioinformatics (BIB), 2024


Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction
Xudong Zhang, Gan Wang, Xiangyu Meng, Shuang Wang, Ying Zhang, Alfonso Rodriguez-Paton, Jianmin Wang*, Xun Wang*
Briefings in Bioinformatics (BIB), 2022

In this paper, we propose Molormer, a method based on a lightweight attention mechanism for DDIs prediction. Molormer takes the two-dimension (2D) structures of drugs as input and encodes the molecular graph with spatial information.


DeepFusion: A Deep Learning Based Multi-Scale Feature Fusion Method for Predicting Drug-Target Interactions
Tao Song*, Xudong Zhang, Mao Ding*, Alfonso Rodriguez-Paton, Shudong Wang, Gan Wang
Methods, 2022

Predicting drug-target interactions (DTIs) is essential for both drug discovery and drug repositioning. We generate global structural similarity feature based on similarity theory and generate local chemical sub-structure semantic feature using transformer for both drug and protein.


TransFusionNet: Semantic and Spatial Features Fusion Framework for Liver Tumor and Vessel Segmentation Under JetsonTX2
Xun Wang, Xudong Zhang, Gan Wang, Ying Zhang, Xin Shi, Huanhuan Dai, Min Liu, Zixuan Wang, Xiangyu Meng
IEEE Journal of Biomedical and Health Informatics (JBHI), 2022

We introduce TransFusionNet, which consists of a semantic feature extraction module, a local spatial feature extraction module, an edge feature extraction module, and a multi-scale feature fusion module to achieve fine-grained segmentation of liver tumors and vessels.


AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug-drug interaction prediction
Shanchen Pang, Ying Zhang, Tao Song*, Xudong Zhang, Xun Wang, Alfonso Rodriguez-Patón
Briefings in Bioinformatics (BIB), 2021

The properties of the drug may be altered by the combination,which may cause unexpected drug–drug interactions (DDIs). In this work, we propose a novel attention-mechanism-based multidimensional feature encoder for DDIs prediction, namely attention-based multidimensional feature encoder.


🏆 Honors and Awards

  • 2023.04: Outstanding Graduate of Shandong Province
  • 2022.12: National scholarship for Postgraduates

🌏 Academic Service

Reviewer: Briefings in Bioinformatics, Journal of Cheminformatics, BioData Mining, BMC Bioinformatics.