Cell clustering is crucial for uncovering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data by identifying cell types and marker genes. Despite its importance, existing benchmarks for scRNA-seq clustering remain fragmented, lacking standardized protocols and often omitting recent advances in artificial intelligence.To fill these gaps, we present scCluBench, a comprehensive benchmark of clustering algorithms for scRNA-seq data. scCluBench provides 36 scRNA-seq datasets collected from diverse public sources, covering multiple tissues, which are uniformly processed to ensure consistency for systematic evaluation and downstream analyses. To assess performance, we collect and reproduce a range of scRNA-seq clustering methods, including traditional, deep learning-based, graph-based, and biological foundation models. We comprehensively evaluate each method both quantitatively and qualitatively, using core performance metrics and visualization analyses. Furthermore, we construct representative downstream biological tasks, such as marker gene identification and cell type annotation, to further assess the practical utility. scCluBench then investigates the performance differences and applicability boundaries of various clustering models across diverse analytical tasks, systematically assessing their robustness and scalability in real-world scenarios. Overall, scCluBench offers a standardized and user-friendly benchmark for scRNA-seq clustering, with standardized datasets, unified evaluation protocols, and transparent analyses, facilitating informed method selection and providing valuable insights into model generalizability and application scope.

Drug-Drug Interaction (DDI) prediction has attracted considerable attention in designing multi-drug combination strategies and avoiding adverse reactions. Notably, Artificial Intelligence (AI)-driven DDI prediction methods have emerged as a pivotal research paradigm. However, most AI-driven DDI prediction methods fall short in exploring intra-molecular motifs, and heavily rely on the overly idealized assumption of the complete inter-molecular topology, limiting their expressive capacities. To this end, we propose a Motif-Oriented representation learning with TOpology Refinement for DDI prediction, namely MOTOR, to exploit both the multi-granularity motif information and the topological structure of DDI networks. Specifically, MOTOR effectively captures motif internal structures, motif local contexts, and motif global semantics. Furthermore, MOTOR employs an iterative learning strategy to continuously refine the DDI topology and optimize the corresponding drug representations. Extensive experimental results demonstrate that MOTOR exhibits superior performance with interpretable insights in DDI prediction tasks across three real-world datasets, thereby opening up new avenues in AI-driven DDI prediction.

Foundation models, which exploit self-supervised learning on vast amounts of data, have demonstrated superior performance in various biomedical domains. However, the differences in existing foundation models, such as pre-training data sources, model architectures, and pre-training tasks, lead to noticeable variations in their performance across diverse biomedical tasks. Therefore, it remains unclear how to select the most appropriate foundation model for a new task. Here, we propose an alternative solution scMoE to integrate multiple foundation models within the same domain through a mixture-of-expert framework. The key idea of scMoE is to selectively activate certain foundation models according to the specific task and input using a multi-head sparse router. scMoE can be further extended to integrate foundation models across modalities, such as integrating multiple single-cell foundation models and multiple molecular foundation models. Our experiments across three biomedical tasks, including cancer drug response prediction, drug combination perturbation prediction, and synergistic drug combination prediction, demonstrate that scMoE significantly outperforms individual foundation models, highlighting its effectiveness and robustness in complex biomedical applications. Overall, scMoE provides a novel integrative framework for utilizing heterogeneous foundation models, paving the path for applying multiple diverse foundation models to new tasks.

Zero-shot hashing (ZSH) has shown excellent success owing to its efficiency and generalization in large-scale retrieval scenarios. However, existing works ignore the locality relationships of representations and attributes, which have effective transferability between seeable classes and unseeable classes. Also, the continuous value attributes are not fully harnessed. In response, we conduct a COMprehensive Attribute Exploration for ZSH, named COMAE, which depicts the relationships from seen classes to unseen ones through three meticulously designed explorations, i.e., point-wise, pair-wise and class-wise consistency constraints. By regressing attributes from the proposed attribute prototype network, COMAE learns the local features that are relevant to the visual attributes. Then COMAE utilizes contrastive learning to comprehensively depict the context of attributes, rather than instance-independent optimization. Finally, the class-wise constraint is designed to cohesively learn the hash code, image representation, and visual attributes more effectively. Furthermore, theoretical analysis is provided to show the effectiveness of COMAE. Experimental results demonstrate that COMAE outperforms state-of-the-art hashing models, especially in scenarios with a larger number of unseen label classes.

Accurately predicting Drug-Drug Interactions (DDIs) is critical to designing effective drug combination therapies. Recently, Artificial Intelligence (AI)-powered DDI prediction approaches have emerged as a new paradigm. However, most existing methods oversimplify the complex hierarchical structure within molecules and overlook the multi-source heterogeneous information external to molecules, limiting their modeling and predictive capabilities. To address this, we propose a H ierarchical H eterogeneous graph learning framework for D DI prediction, namely H2D. H2D employs an internal-to-external, local-to-global hierarchical perspective, exploiting intra-molecular multi-granularity structures and inter-molecular biomedical interactions to mutually enhance across hierarchical levels. Extensive experimental results demonstrate H2D's effectiveness on three real-world DDI prediction tasks (binary-class, multi-class, and multi-label). In sum, H2D achieves state-of-the-art performance in DDI prediction by leveraging the multi-scale graph structures, opening up new avenues in AI-powered DDI prediction.

Drug-target interaction (DTI) prediction can identify novel ligands for specific protein targets, and facilitate the rapid screening of effective new drug candidates to speed up the drug discovery process. However, the current methods are not sensitive enough to complex topological structures, and complicated relations between multiple node types are not fully captured yet. To address the above challenges, we construct a metapath-based heterogeneous bioinformatics network, and then propose a DTI prediction method with metapath-based hierarchical transformer and attention network for drug-target interaction prediction (MHTAN-DTI), applying metapath instance-level transformer, single-semantic attention and multi-semantic attention to generate low-dimensional vector representations of drugs and proteins. Metapath instance-level transformer performs internal aggregation on the metapath instances, and models global context information to capture long-range dependencies. Single-semantic attention learns the semantics of a certain metapath type, introduces the central node weight and assigns different weights to different metapath instances to obtain the semantic-specific node embedding. Multi-semantic attention captures the importance of different metapath types and performs weighted fusion to attain the final node embedding. The hierarchical transformer and attention network weakens the influence of noise data on the DTI prediction results, and enhances the robustness and generalization ability of MHTAN-DTI. Compared with the state-of-the-art DTI prediction methods, MHTAN-DTI achieves significant performance improvements. In addition, we also conduct sufficient ablation studies and visualize the experimental results. All the results demonstrate that MHTAN-DTI can offer a powerful and interpretable tool for integrating heterogeneous information to predict DTIs and provide new insights into drug discovery.

Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. However, existing computational DDI methods mostly rely on the single-view paradigm, failing to handle the complex features and intricate patterns of DDIs due to the limited expressiveness of the single view. To this end, we propose a Hierarchical Triple-view Contrastive Learning framework for Drug-Drug Interaction prediction (HTCL-DDI), leveraging the molecular, structural and semantic views to model the complicated information involved in DDI prediction. To aggregate the intra-molecular compositional and structural information, we present a dual attention-aware network in the molecular view. Based on the molecular view, to further capture inter-molecular information, we utilize the one-hop neighboring information and high-order semantic relations in the structural view and semantic view, respectively. Then, we introduce contrastive learning to enhance drug representation learning from multifaceted aspects and improve the robustness of HTCL-DDI. Finally, we conduct extensive experiments on three real-world datasets. All the experimental results show the significant improvement of HTCL-DDI over the state-of-the-art methods, which also demonstrates that HTCL-DDI opens new avenues for ensuring medication safety and identifying synergistic drug combinations.