Guikun Xu (徐桂昆)

M.S. Candidate in Computer Science and Technology,
School of Computer and Artificial Intelligence, Southwest Jiaotong University (SWJTU).

Biograph: Being mentored by Professor Yan Yang and Assistant Researcher Yongquan Jiang, I am currently pursuing my Master's degree in “Computer Science and Technology” at the School of Computer and Artificial Intelligence, Southwest Jiaotong University (SWJTU). Prior to this, I earned my Bachelor of Science degree from the School of Science at Southwest Petroleum University (SWPU), majoring in “Electronic Information Science and Technology”.

Research Topics: I am primarily engaged in the exploration of AI for Science, with a particular concentration on Molecular Machine Learning. In addition to this, I possess a substantial interest in the study and application of Deep Generative Algorithms.

news

Jan 16, 2024	New!!! Our paper has been accepted as a spotlight paper by The Twelfth International Conference on Learning Representations (ICLR 2024)!

selected publications [full list]

(*) denotes equal contribution

ICLRSpotlight

GTMGC: Using Graph Transformer to Predict Molecule’s Ground-State Conformation

Guikun Xu, Quanyong Jiang, Pengchuan Lei, Yan Yang, and Jim.X Chen

In the 12th International Conference on Learning Representations, 2024.
Spotlight Presentation [Top 5%]
Abstract PDF Code

The ground-state conformation of a molecule is often decisive for its properties. However, experimental or computational methods, such as density functional theory (DFT), are time-consuming and labor-intensive for obtaining this conformation. Deep learning (DL) based molecular representation learning (MRL) has made significant advancements in molecular modeling and has achieved remarkable results in various tasks. Consequently, it has emerged as a promising approach for directly predicting the ground-state conformation of molecules. In this regard, we introduce GTMGC, a novel network based on Graph-Transformer (GT) that seamlessly predicts the spatial configuration of molecules in a 3D space from their 2D topological architecture in an end-to-end manner. Moreover, we propose a novel self-attention mechanism called Molecule Structural Residual Self-Attention (MSRSA) for molecular structure modeling. This mechanism not only guarantees high model performance and easy implementation but also lends itself well to other molecular modeling tasks. Our method has been evaluated on the Molecule3D benchmark dataset and the QM9 dataset. Experimental results demonstrate that our approach achieves remarkable performance and outperforms current state-of-the-art methods as well as the widely used open-source software RDkit.