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Zhixiang Cheng

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JBHI Journal 2026 Journal Article

ReMol: A Chemical Reaction Knowledge-guided Self-supervised Molecular Image Representation Learning Framework

Zhixiang Cheng
Hongxin Xiang
Mingquan Liu
Li Zeng
Xiangxiang Zeng
Bosheng Song

Molecular representation learning (MRL) is critical in computational chemistry and drug discovery, paving the way for efficient molecular properties and biological activity prediction. However, existing sequence-based or graph-based MRL methods emphasize static and intrinsic molecular topological features while ignoring dynamic and interactive chemical knowledge, resulting in insufficient generalization ability. MolR meets this challenge by leveraging the equivalence of molecules participating in chemical reactions in embedding space to assist in learning molecular representations. However, it can suffer from unsatisfactory performance because it lacks reaction center information and the complex relationship between reactions, which provides a deeper understanding of the chemical processes. We propose ReMol, an elaborate chemical reaction knowledge-guided self-supervised molecular image representation learning framework to address this issue. The ReMol framework integrates comprehensive reaction inductive biases, including reaction templates and consistency, and diversity in chemical reactions. Experimental results demonstrate that our framework achieves state-of-the-art results compared with cutting-edge methods on various challenging downstream tasks, such as chemical reaction and molecular property prediction tasks. Overall, our work offers a robust tool for advancing chemistry research, with the potential to make significant contributions to both molecular representation learning and drug discovery.

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NeurIPS Conference 2025 Conference Paper

EDBench: Large-Scale Electron Density Data for Molecular Modeling

Hongxin Xiang
Ke Li
Mingquan Liu
Zhixiang Cheng
Bin Yao
Wenjie Du
Jun Xia
Li Zeng

Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but ignore the importance of electron density (ED) $\rho(r)$ in accurately understanding molecular force fields (MFFs). ED describes the probability of finding electrons at specific locations around atoms or molecules, which uniquely determines all ground state properties (such as energy, molecular structure, etc. ) of interactive multi-particle systems according to the Hohenberg-Kohn theorem. However, the calculation of ED relies on the time-consuming first-principles density functional theory (DFT), which leads to the lack of large-scale ED data and limits its application in MLFFs. In this paper, we introduce EDBench, a large-scale, high-quality dataset of ED designed to advance learning-based research at the electronic scale. Built upon the PCQM4Mv2, EDBench provides accurate ED data, covering 3. 3 million molecules. To comprehensively evaluate the ability of models to understand and utilize electronic information, we design a suite of ED-centric benchmark tasks spanning prediction, retrieval, and generation. Our evaluation of several state-of-the-art methods demonstrates that learning from EDBench is not only feasible but also achieves high accuracy. Moreover, we show that learning-based methods can efficiently calculate ED with comparable precision while significantly reducing the computational cost relative to traditional DFT calculations. All data and benchmarks from EDBench will be freely available, laying a robust foundation for ED-driven drug discovery and materials science.

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