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

🎉 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

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.