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Cheng Liang

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7 papers

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AAAI Conference 2026 Conference Paper

Geometry-Aware Variational Information Maximization for Deep Incomplete Multi-view Clustering

Wenlan Chen
Lu Gao
Daoyuan Wang
Fei Guo
Cheng Liang

Incomplete multi-view clustering (IMVC) aims to group data into meaningful clusters when each sample is only partially observed across multiple views. Most existing methods either rely on imputation strategies that may introduce noise and distort the underlying data distribution, or adopt cross-view alignment techniques that focus on pairwise relationships, often resulting in suboptimal representations and unstable clustering performance. In this paper, we propose Geometry-Aware Variational Information Maximization for Deep Incomplete Multi-view Clustering (GAVIM), a novel imputation-free variational framework that enables robust and coherent incomplete multi-view clustering. Specifically, GAVIM leverages mutual information maximization to preserve the high mutual information between the available multi-view data and the shared embedding. Moreover, we explicitly retain local geometric consistency within each view-specific latent space under the guidance of an adaptive global supervision signal. Lastly, GAVIM aligns all views simultaneously using a Gramian representation alignment measure, ensuring coherent structure across modalities and promoting unified, semantically meaningful representations. Extensive experiments on five benchmark IMVC datasets with varying levels of view incompleteness demonstrate that GAVIM consistently outperforms state-of-the-art methods in clustering accuracy and representation quality.

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AAAI Conference 2026 Conference Paper

PharmaQA: Prompt-Based Molecular Representation Learning via Pharmacophore-Oriented Question Answering

Chengwei Ai
Qiaozhen Meng
Mengwei Sun
Ruihan Dong
Hongpeng Yang
Shiqiang Ma
Xiaoyi Liu
Cheng Liang

Molecular representation plays a central role in computational drug discovery. Pharmacophores, functional groups responsible for molecular bioactivity, have been widely studied in cheminformatics. However, their incorporation into molecular representation learning, particularly in a context reasoning or generalization, remains relatively limited. To address this gap, we propose PharmaQA, a pharmacophore oriented question answering framework that formulates tailored prompts to extract context-aware molecular semantics. Rather than encoding pharmacophore features, PharmaQA learns to answer pharmacophore related queries. This design enables flexible reasoning across diverse tasks, including molecular property prediction, compound-target interaction prediction, and binding affinity estimation. Experimental results on benchmark datasets demonstrate that PharmaQA achieves competitive performance. In a ligand discovery case study using FDA-approved compounds, the framework identified potential inhibitors for three therapeutic targets, with strong docking performance. As a generalizable and modular solution, PharmaQA incorporates pharmacophoric knowledge into molecular embeddings, enhancing both predictive accuracy and interpretability in drug discovery applications.

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NeurIPS Conference 2025 Conference Paper

Disentangled Cross-Modal Representation Learning with Enhanced Mutual Supervision

Lu Gao
Wenlan Chen
Daoyuan Wang
Fei Guo
Cheng Liang

Cross-modal representation learning aims to extract semantically aligned representations from heterogeneous modalities such as images and text. Existing multimodal VAE-based models often suffer from limited capability to align heterogeneous modalities or lack sufficient structural constraints to clearly separate the modality-specific and shared factors. In this work, we propose a novel framework, termed D isentangled C ross- M odal Representation Learning with E nhanced M utual Supervision (DCMEM). Specifically, our model disentangles the common and distinct information across modalities and regularizes the shared representation learned from each modality in a mutually supervised manner. Moreover, we incorporate the information bottleneck principle into our model to ensure that the shared and modality-specific factors encode exclusive yet complementary information. Notably, our model is designed to be trainable on both complete and partial multimodal datasets with a valid Evidence Lower Bound. Extensive experimental results demonstrate significant improvements of our model over existing methods on various tasks including cross-modal generation, clustering, and classification.

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NeurIPS Conference 2025 Conference Paper

DynaPhArM: Adaptive and Physics-Constrained Modeling for Target-Drug Complexes with Drug-Specific Adaptations

Diya Zhang
Mengwei Sun
Xingdan Wang
Cheng Liang
Qiaozhen Meng
Shiqiang Ma
Fei Guo

Accurately modeling the target-drug complex at atom level presents a significant challenge in the computer-aided drug design. Traditional methods that rely solely on rigid transformations often fail to capture the adaptive interactions between targets and drugs, particularly during substantial conformational changes in targets upon ligand binding, which becomes especially critical when learning target-drug interactions in drug design. Accurately modeling these changes is crucial for understanding target-drug interactions and improving drug efficacy. To address these challenges, we introduce DynaPhArM, an SE(3)-Equivariant Transformer model specifically designed to capture adaptive alterations occurring within target-drug interactions. DynaPhArM utilizes the cooperative scalar-vector representation, drug-specific embeddings, and a diffusion process to effectively model the evolving dynamics of interactions between targets and drugs. Furthermore, we integrate physical information and energetic principles that maintain essential geometric constraints, such as bond lengths, bond angles, van der Waals forces (vdW), within a multi-task learning (MTL) framework to enhance accuracy. Experimental results demonstrate that DynaPhArM achieves state-of-the-art performance with an overall root mean square deviation (RMSD) of 2. 01 Å and a sc-RMSD of 0. 29 Å while exhibiting higher success rates compared to existing methodologies. Additionally, DynaPhArM shows promise in enhancing drug specificity, thereby simulating how targets adapt to various drugs through precise modeling of atomic-level interactions and conformational flexibility.

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AIIM Journal 2025 Journal Article

EDDINet: Enhancing drug–drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning

Hong Wang
Luhe Zhuang
Yijie Ding
Prayag Tiwari
Cheng Liang

Predicting drug–drug interactions (DDIs) is crucial for understanding and preventing adverse drug reactions (ADRs). However, most existing methods inadequately explore the interactive information between drugs in a self-supervised manner, limiting our comprehension of drug–drug associations. This paper introduces EDDINet: Enhancing Drug-Drug Interaction Prediction via Information Flow and Consensus-Constrained Multi-Graph Contrastive Learning for precise DDI prediction. We first present a cross-modal information-flow mechanism to integrate diverse drug features, enriching the structural insights conveyed by the drug feature vector. Next, we employ contrastive learning to filter various biological networks, enhancing the model’s robustness. Additionally, we propose a consensus regularization framework that collaboratively trains multi-view models, producing high-quality drug representations. To unify drug representations derived from different biological information, we utilize an attention mechanism for DDI prediction. Extensive experiments demonstrate that EDDINet surpasses state-of-the-art unsupervised models and outperforms some supervised baseline models in DDI prediction tasks. Our approach shows significant advantages and holds promising potential for advancing DDI research and improving drug safety assessments. Our codes are available at: https: //github. com/95LY/EDDINet_code.

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IJCAI Conference 2025 Conference Paper

Image-Enhanced Hybrid Encoding with Reinforced Contrastive Learning for Spatial Domain Identification in Spatial Transcriptomics

Daoyuan Wang
Lu Gao
Wenlan Chen
Cheng Liang
Fei Guo

Spatial transcriptomics integrates spatial, gene expression, and multichannel immunohistochemistry image data, enabling advanced insights into cellular organization. However, existing methods often struggle to effectively fuse these multimodal data, limiting their potential for accurate spatial domain identification. Here, we propose IE-HERCL (Image-Enhanced Hybrid Encoding with Reinforced Contrastive Learning), a novel framework designed to address this challenge. Specifically, IE-HERCL employs hybrid encoding to capture both the non-spatial features and spatial dependencies for both gene and image modalities via autoencoders and GraphSAGE, respectively. These features are then fused using cross-view attention mechanisms to generate the unified informative embedding. To enhance the representation learning capability, we introduce a reinforced contrastive learning strategy to mitigate the influences of false negative samples, where we detect potential positive counterparts with high-order random walks. In addition, the cluster alignment is dynamically refined through optimal transport, which ensures that the fused consensus representation is coherent and robust, enabling accurate spatial domain identification. Our approach achieves state-of-the-art performance on five image-enhanced spatial transcriptomics datasets, demonstrating its robustness and effectiveness in multimodal integration and spatial domain identification. IE-HERCL offers a powerful and innovative solution for advancing spatial transcriptomics analysis. The code is released on https: //github. com/wdyi701/IE-HERCL.

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EAAI Journal 2025 Journal Article

Unsupervised multi-view feature selection based on weighted low-rank tensor learning and its application in multi-omics datasets

Daoyuan Wang
Lianzhi Wang
Wenlan Chen
Hong Wang
Cheng Liang

With the explosive growth of unlabeled multi-view data with high dimensionality in various fields such as bioinformatics, unsupervised multi-view feature selection has become a key technique to simultaneously handle the curse of dimensionality in multiple datasets. Most existing methods heavily rely on the pseudo labels obtained from respective views, where they perform feature selections without comprehensively exploiting the high-order connections among views. In this paper, we propose a weighted low-rank tensor-based unsupervised multi-view feature selection framework (WLTL), which integrates multi-view spectral clustering and weighted low-rank tensor to generate high-quality pseudo labels for feature selection. Specifically, we stack the clustering indicator matrices into a three-dimensional tensor and impose the weighted tensor nuclear norm constraint, which captures high-order correlations and consistent pseudo label information while allowing for more refined consideration of the importance of each view. Our method also features an adaptive strategy to automatically assign view weights, optimizing the feature selection process by considering both individual view characteristics and inter-view relationships. We present an efficient optimization algorithm for iteratively refining the proposed framework. Extensive experiments implemented on six machine learning and three multi-omics datasets confirm the superiority of WLTL. Additionally, the case study performed on the cancer dataset serves to affirm the practicality of our model.

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