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Dongsheng Cao

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4 papers

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AAAI Conference 2026 Conference Paper

TRACE: Transformation-Aware Graph Refinement for Reaction Condition Prediction

Yujie Chen
Tengfei Ma
Yuansheng Liu
Leyi Wei
Shu Wu
Dongsheng Cao
Yiping Liu
Xiangxiang Zeng

Identifying suitable reaction conditions is critical for chemical synthesis, as they directly affect yield, selectivity, and transformation feasibility. While recent methods have shown promising results, most approaches either encode reactants and products independently or rely on rule-based reaction graphs, both of which constrain the ability of the model to capture condition-relevant structural transformations. In this work, we propose TRACE, a transformation-aware graph refinement framework for reaction condition prediction. TRACE constructs atom-level joint graphs that integrate both reactant and product structures to represent condition-relevant transformations. A structure-aware encoder enriches atom features with local chemical context, followed by a dynamic interaction refinement module that adaptively infers task-specific edges. To further guide the model toward condition-relevant patterns, a mechanism regularized graph encoder incorporates reaction center information, enabling more accurate modeling of transformation mechanisms. Experiments on benchmark datasets show that TRACE achieves state-of-the-art performance across multiple condition types. The integration of transformation-aware refinement leads to improvements in prediction accuracy and generalization, while maintaining robust performance in challenging and realistic synthesis planning scenarios.

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JBHI Journal 2025 Journal Article

CardiOT: Towards Interpretable Drug Cardiotoxicity Prediction Using Optimal Transport and Kolmogorov--Arnold Networks

Xinyu Zhang
Hao Wang
Zhenya Du
Linlin Zhuo
Xiangzheng Fu
Dongsheng Cao
Boqia Xie
Keqin Li

Investigating the inhibitory effects of compounds on cardiac ion channels is essential for assessing cardiac drug safety. Consequently, researchers have developed computational models to evaluate combined cardiotoxicity (CCT) on cardiac ion channels. However, limitations in experimental data often cause issues like uneven data distribution and scarcity. Additionally, existing models primarily emphasize atomic information flow within graph neural networks (GNNs) while overlooking chemical bonds, leading to inadequate recognition of key structures. Therefore, this study integrates optimal transport (OT), structure remapping (SR), and Kolmogorov-Arnold networks (KANs) into a GNN-based CCT prediction model, CardiOT. First, the proposed CardiOT model employs OT pooling to optimize sample-feature joint distribution using expectation maximization, identifying “important” sample-feature pairs. Additionally, SR technology is used to emphasize the role of chemical bond information in message propagation. KAN technology is integrated to greatly enhance model interpretability. In summary, the model mitigates challenges related to uneven data distribution and scarcity. Multiple experiments on public datasets confirm the model's robust performance. We anticipate that this model will provide deeper insights into compound inhibition mechanisms on cardiac ion channels and reduce toxicity risks.

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JBHI Journal 2025 Journal Article

DrugKANs: A Paradigm to Enhance Drug-Target Interaction Prediction With KANs

Xiangzheng Fu
Zhenya Du
Yifan Chen
Haiting Chen
Linlin Zhuo
Aiping Lu
Dongsheng Cao
Xiaojun Yao

Identifyingpotential drug-target interactions (DTIs) is crucial for understanding drug mechanisms, and recent computational methods have yielded promising results in this area. However, these methods face several challenges, including limited model generalization due to heavy reliance on multiple similarity datasets and complex feature extraction, as well as a lack of interpretability by ignoring intrinsic information about drugs and targets. To address these challenges, we propose DrugKANs, a novel DTI prediction model that enhances both the quality and interpretability of DTI representations by integrating a dual-tower architecture with Kolmogorov-Arnold Network (KAN) technology. Our model involves utilizing a pre-trained model to derive initial representations of drugs and targets, and employing a lightweight attention mechanism to capture key features, thereby improving representation quality. We leverage the dual-tower architecture and a lightweight feature interaction mechanism to extract high-level representations separately for drugs and targets, aiming to reduce complex feature interactions and mitigate overfitting. Additionally, we incorporate a contrastive learning strategy within the drug-target bipartite graph to address sparse neighborhood effects and enhance topological information. The inclusion of KAN technology further improves the interpretability of the DTI prediction model. Experimental results on public datasets demonstrate that our model predicts DTIs effectively, underscoring its potential as a valuable tool in drug discovery. This comprehensive methodology presents a balanced approach to overcoming the identified challenges in DTI prediction. Our data and code are available at: https://github.com/Excelsior511/DrugKANs.

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IJCAI Conference 2023 Conference Paper

GPMO: Gradient Perturbation-Based Contrastive Learning for Molecule Optimization

Xixi Yang
Li Fu
Yafeng Deng
Yuansheng Liu
Dongsheng Cao
Xiangxiang Zeng

Optimizing molecules with desired properties is a crucial step in de novo drug design. While translation-based methods have achieved initial success, they continue to face the challenge of the “exposure bias” problem. The challenge of preventing the “exposure bias” problem of molecule optimization lies in the need for both positive and negative molecules of contrastive learning. That is because generating positive molecules through data augmentation requires domain-specific knowledge, and randomly sampled negative molecules are easily distinguished from the real molecules. Hence, in this work, we propose a molecule optimization method called GPMO, which leverages a gradient perturbation-based contrastive learning method to prevent the “exposure bias” problem in translation-based molecule optimization. With the assistance of positive and negative molecules, GPMO is able to effectively handle both real and artificial molecules. GPMO is a molecule optimization method that is conditioned on matched molecule pairs for drug discovery. Our empirical studies show that GPMO outperforms the state-of-the- art molecule optimization methods. Furthermore, the negative and positive perturbations improve the robustness of GPMO.

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