Accurate prediction of molecular properties is a central task in drug discovery and materials science, yet it is often constrained by the scarcity of labeled samples, leading to the challenging problem of few-shot molecular property prediction (FS-MPP). To address this issue, existing meta-learning approaches have made notable progress but still suffer from two critical limitations: (i) the reliance on homogeneous graph representations, which neglect higher-level chemical semantics such as pharmacophores; and (ii) the lack of adaptive relational reasoning tailored to different molecules and tasks. In this work, we propose a novel framework termed Heterogeneous Property-Cross-Aware Relation Network (HPCA). HPCA constructs a unified heterogeneous graph strictly limited to the training set, in which atoms, globally shared pharmacophores, and molecular properties are modeled as distinct types of nodes to prevent information leakage. Building upon this representation, HPCA incorporates an adaptive relational reasoning module and a cross-layer attention mechanism, enabling dynamic learning of critical interactions that determine molecular properties. Extensive experiments conducted on four public benchmarks—TOX21, SIDER, MUV, and ToxCast—under the standard episodic evaluation protocol of the few-shot learning community demonstrate that HPCA achieves statistically significant improvements over strong meta-learning baselines such as PAR and Meta-MGNN across diverse few-shot settings.

MOTIVATION: Proteins play a crucial role in biological systems, and accurate protein sequence prediction is essential for applications such as drug discovery. Existing inverse folding models primarily rely on protein backbone structure information, overlooking the biochemical properties embedded in protein surface data that constrain its functionality, leading to limited prediction accuracy. RESULTS: We propose a novel inverse folding framework, SurfFold, which integrates both protein backbone structure and surface information for sequence prediction. Additionally, it incorporates side-chain structural information and its interaction with surface information. Then, we introduce a Representation alignment module to better fuze structure and surface Representations. Experimental results demonstrate that SurfFold achieves state-of-the-art performance on the CATH4.2 dataset, and additional experiments validate the effectiveness of the proposed modules. Moreover, the homologous structure inverse folding experiment also demonstrates that SurfFold possesses excellent capability in homologous protein design. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/jiudizhengf/SurfFold.

Spectral Graph Neural Networks effectively handle graphs with different homophily levels, with low-pass filter mining feature smoothness and high-pass filter capturing differences. When these distinct filters could naturally form two opposite views for self-supervised learning, the commonalities between these counterparts for the same node remain unexplored, leading to suboptimal performance. In this paper, a simple yet effective self-supervised contrastive framework, LOHA, is proposed to address this gap. LOHA optimally leverages low-pass and high-pass views by embracing "harmony in diversity". Rather than solely maximizing the difference between these distinct views, which may lead to feature separation, LOHA harmonizes the diversity by treating the propagation of graph signals from both views as a composite feature. Specifically, a novel high-dimensional feature named spectral signal trend is proposed to serve as the basis for the composite feature, which remains relatively unaffected by changing filters and focuses solely on original feature differences. LOHA achieves an average performance improvement of 2.8% over runner-up models on 9 real-world datasets with varying homophily levels. Notably, LOHA even surpasses fully-supervised models on several datasets, which underscores the potential of LOHA in advancing the efficacy of spectral GNNs for diverse graph structures.

Current graph topology augmentation methods are mostly static and heavily rely on the assumption of homophily, where connected nodes are presumed to share the same labels by default. Due to the complexity of real-world graphs, the underlying assumption is often disrupted, thus performance declines, demonstrating their limited adaptability. Although learnable methods flexibly change augmentation strategies based on data, ignorance of balancing consistency and diversity leads to suboptimal performance. This gap highlights the need for universally applicable graph augmentation strategies that ensure these two aspects, thereby enhancing model robustness. To address these challenges, we propose the RETAIN framework as an adaptive data augmentation method for graphs with different homophily levels. This method can dynamically adjust the graph structure based on a learned conditional distribution with the aid of a graph explainer, thus balancing the consistency and diversity of the augmented data. By framing graph topology modification and model refinement as a joint optimization problem, RETAIN facilitates concurrent learning from augmented data and model optimization. Empirical evaluations across diverse benchmarks on node classification tasks reveal that RETAIN can be effectively combined with other methods in a plug-and-play manner and consistently yields performance improvement across a diverse set of benchmarks for both homophilic and heterophilic graphs.

MOTIVATION: In the field of drug discovery, accurately and effectively predicting the binding affinity between proteins and ligands is crucial for drug screening and optimization. However, current research primarily utilizes representations based on sequence or structure to predict protein-ligand binding affinity, with relatively less study on protein surface information, which is crucial for protein-ligand interactions. Moreover, when dealing with multimodal information of proteins, traditional approaches typically concatenate features from different modalities in a straightforward manner without considering the heterogeneity among them, which results in an inability to effectively exploit the complementary between modalities. RESULTS: We introduce a novel multimodal feature extraction (MFE) framework that, for the first time, incorporates information from protein surfaces, 3D structures, and sequences, and uses cross-attention mechanism for feature alignment between different modalities. Experimental results show that our method achieves state-of-the-art performance in predicting protein-ligand binding affinity. Furthermore, we conduct ablation studies that demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within the framework. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/Sultans0fSwing/MFE.

MOTIVATION: Molecular representation learning plays an indispensable role in crucial tasks such as property prediction and drug design. Despite the notable achievements of molecular pre-training models, current methods often fail to capture both the structural and feature semantics of molecular graphs. Moreover, while graph contrastive learning has unveiled new prospects, existing augmentation techniques often struggle to retain their core semantics. To overcome these limitations, we propose a gradient-compensated encoder parameter perturbation approach, ensuring efficient and stable feature augmentation. By merging enhancement strategies grounded in attribute masking and parameter perturbation, we introduce MoleMCL, a new MOLEcular pre-training model based on multi-level contrastive learning. RESULTS: Experimental results demonstrate that MoleMCL adeptly dissects the structure and feature semantics of molecular graphs, surpassing current state-of-the-art models in molecular prediction tasks, paving a novel avenue for molecular modeling. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this work are available in GitHub at https://github.com/BioSequenceAnalysis/MoleMCL.

Augmented Reality (AR) has the potential to help drivers become aware of invisible hazards through an Augmented Reality Head-Up Display (AR-HUD). However, this issue is still underexplored. To address it, a novel warning system for invisible dangers in AR-HUD user interfaces has been designed as a carrier for agents' cognitive information to enhance driver perception. This design was created by using a team cooperation perception model that combined the perception cycle of a human driver with a computational agent. Furthermore, user experiments were conducted to investigate the impact of this design on safe driving in two typical scenarios. The experimental results showed that this design can significantly improve drivers’ situation awareness and reaction time in both human-driving and auto-pilot modes, and enhance human drivers' trust in the auto-pilot system. The model and design can be generalized to more AR-HUD scenarios requiring human-machine perception and cognitive cooperation.

In this paper, we propose a novel proxy caching scheme for Video on Demand (VoD) services. Our approach is based on an observation we have made during subjective VoD performance evaluation tests. We have found that when users are seeking for some specific content, they pay most attention to the initial delay, while a small shift of the starting point is acceptable. Based on this observation as well as the dynamic popularity of video segments, we propose an efficient segment based caching algorithm. The approach is applied on a proxy and minimizes the average initial playout delay at the clients. Our experimental results show a significant reduction of the average initial waiting time compared to conventional caching approaches.