Drug repositioning has become a hot topic that could provide an innovative solution in drug discovery by exploring the potential correlation between drugs and diseases. However, existing computational drug repositioning methods fail to effectively integrate heterogeneous data from multiple sources and neglect the multi-level and multi-scale interactions in biological systems. To address the above problems, we propose MAPTrans, which dynamically optimizes the representation of disease and drug with a multi-level meta-path aggregation strategy. In addition, a multi-view importance assessment mechanism is introduced to evaluate and filter the most discriminating views to optimize feature representation. A mutual attention mechanism Transformer architecture with a cross-view interaction that fuses the information of drugs and diseases in a multi-view space is designed. Experimental results of MAPTrans on multiple benchmark datasets show that it significantly outperforms existing baseline models.

Protein-protein interactions (PPIs) are fundamental to biological processes, yet experimental determination of PPIs remains costly and labor-intensive. While computational methods have emerged as promising alternatives, sequence-based approaches face critical challenges: (1) effectively capturing long-range dependencies and critical biochemical patterns in variable-length sequences, and (2) balancing computational efficiency with sensitivity to subtle residue-level interactions. Here, we present Dual Protein Embedding-based Graph Model (DPEG), which leverages dynamic graph attention networks to enable robust sequence-driven PPI prediction. Unlike structure-dependent methods, DPEG operates solely on sequence data, bypassing the need for structural or domain annotations. Specifically, we employ ESM-2 to transform sequences into residue-level graphs, preserving evolutionary and physicochemical context. To address variable sequence lengths, we design a module that can represent protein sequences of arbitrary lengths as graph networks at the amino acid level. Further, a gated attention mechanism is introduced to adaptively refining residue representations. Finally, a dynamic attention mechanism prioritizes functionally critical motifs within the graph. Evaluated on four diverse PPI datasets spanning different species and interaction types, DPEG achieves state-of-the-art performance and demonstrates strong cross-dataset generalizability. By integrating deep sequence semantics with graph-based interaction modeling, DPEG advances sequence-only PPI prediction, offering a scalable and biologically plausible framework for proteome-wide studies.

ABSTRACT With the growing application of Graph Convolutional Networks (GCNs) across various domains, particularly in the field of bioinformatics, the demand for their interpretability has become more urgent. In bioinformatics, many critical tasks, including drug discovery and protein–ligand interaction analysis, rely heavily on molecular activity prediction, where functional substructures play a decisive role in shaping molecular activities. However, existing explanation methods rely on the features of nodes and edges generated by the GCN model, while overlooking the critical structural information within the graph. On the other hand, how to ensure the compactness of the explanation methods also needs to be solved. To address these issues, we propose GTCExplainer in this paper. The Graph Transformer is introduced to capture more comprehensive graph data information, and a hierarchical edge selector is designed to select suitable edges for the explanation. In addition, a substructure reward mechanism is proposed to generate rewards that ensure the explanation's compactness while including key structures closely related to the molecular activity prediction of the GCN model. Experimental results on multiple biomolecular datasets show that our proposed GTCExplainer can effectively provide explanations for the GCN model on molecular activity prediction.

Molecular property prediction is an important task in drug discovery. However, experimental data for many drug molecules are limited, especially for novel molecular structures or rare diseases which affect the accuracy of many deep learning methods that rely on large training datasets. To this end, we propose PG-DERN, a novel few-shot learning model for molecular property prediction. A dual-view encoder is introduced to learn a meaningful molecular representation by integrating information from node and subgraph. Next, a relation graph learning module is proposed to construct a relation graph based on the similarity between molecules, which improves the efficiency of information propagation and the accuracy of property prediction. In addition, we use a MAML-based meta-learning strategy to learn well-initialized meta-parameters. In order to guide the tuning of meta-parameters, a property-guided feature augmentation module is designed to transfer information from similar properties to the novel property to improve the comprehensiveness of the feature representation of molecules with novel property. A series of comparative experiments on four benchmark datasets demonstrate that the proposed PG-DERN outperforms state-of-the-art methods.

Predicting drug-target affinity (DTA) is a crucial step in the process of drug discovery. Efficient and accurate prediction of DTA would greatly reduce the time and economic cost of new drug development, which has encouraged the emergence of a large number of deep learning-based DTA prediction methods. In terms of the representation of target proteins, current methods can be classified into 1D sequence- and 2D-protein graph-based methods. However, both two approaches focused only on the inherent properties of the target protein, but neglected the broad prior knowledge regarding protein interactions that have been clearly elucidated in past decades. Aiming at the above issue, this work presents an end-to-end DTA prediction method named MSF-DTA (Multi-Source Feature Fusion-based Drug-Target Affinity). The contributions can be summarized as follows. First, MSF-DTA adopts a novel "neighboring feature"-based protein representation. Instead of utilizing only the inherent features of a target protein, MSF-DTA gathers additional information for the target protein from its biologically related "neighboring" proteins in PPI (i.e., protein-protein interaction) and SSN (i.e., sequence similarity) networks to get prior knowledge. Second, the representation was learned using an advanced graph pre-training framework, VGAE, which could not only gather node features but also learn topological connections, therefore contributing to a richer protein representation and benefiting the downstream DTA prediction task. This study provides new perspective for the DTA prediction task, and evaluation results demonstrated that MSF-DTA obtained superior performances compared to current state-of-the-art methods.

Deep learning shortens the cycle of the drug discovery for its success in extracting features of molecules and proteins. Generating new molecules with deep learning methods could enlarge the molecule space and obtain molecules with specific properties. However, it is also a challenging task considering that the connections between atoms are constrained by chemical rules. Aiming at generating and optimizing new valid molecules, this article proposed Molecular Substructure Tree Generative Model, in which the molecule is generated by adding substructure gradually. The proposed model is based on the Variational Auto-Encoder architecture, which uses the encoder to map molecules to the latent vector space, and then builds an autoregressive generative model as a decoder to generate new molecules from Gaussian distribution. At the same time, for the molecular optimization task, a molecular optimization model based on CycleGAN was constructed. Experiments showed that the model could generate valid and novel molecules, and the optimized model effectively improves the molecular properties.

Air pollution has been long recognized as a hazardous factor for the human cardiovascular system. Sulfur dioxide (SO2) is a common ambient air pollutant that is able to cause detrimental effects on hearts. Though the cardiotoxicity effects by sulfur dioxide were well documented in epidemiological reports, however, the underlying mechanisms remain unclear owing to the technical limitations that exist in traditional experimental measures. In this article, we developed a multi-scale virtual ventricular tissue, which incorporated electrophysiological activities from subcellular to tissue levels and could provide comprehensive records and insightful mechanisms of the SO2induced ventricular arrhythmias. Based on the available cellular and molecular experimental data, our findings provide a rationale at tissue level in support of epidemiologic studies pointing to the deleterious effects of SO2pollution on cardiac function.

BACKGROUND: Binding sites are the pockets of proteins that can bind drugs; the discovery of these pockets is a critical step in drug design. With the help of computers, protein pockets prediction can save manpower and financial resources. RESULTS: In this paper, a novel protein descriptor for the prediction of binding sites is proposed. Information on non-bonded interactions in the three-dimensional structure of a protein is captured by a combination of geometry-based and energy-based methods. Moreover, due to the rapid development of deep learning, all binding features are extracted to generate three-dimensional grids that are fed into a convolution neural network. Two datasets were introduced into the experiment. The sc-PDB dataset was used for descriptor extraction and binding site prediction, and the PDBbind dataset was used only for testing and verification of the generalization of the method. The comparison with previous methods shows that the proposed descriptor is effective in predicting the binding sites. CONCLUSIONS: A new protein descriptor is proposed for the prediction of the drug binding sites of proteins. This method combines the three-dimensional structure of a protein and non-bonded interactions with small molecules to involve important factors influencing the formation of binding site. Analysis of the experiments indicates that the descriptor is robust for site prediction.

Exercise is considered as an effective mean against overweight and obesity-related diseases. In this paper, a real-time activity recognition and counting approach is proposed to evaluate amount of exercise only using a wearable smart watch. First, accelerometer and gyroscope data are collected to extract efficient features. Then Support Vector Machine classifiers are trained to recognize nine common exercise activities in real time. In order to measure the frequency of repetitive activity, a general activity counting algorithm based on gyroscope is proposed which is applicable for different types of activity. Various activities can be counted uninterruptedly using the proposed general method without frequently changing algorithms. Through experiments, it is demonstrated that the extracted features are efficient for real time exercise activity recognition. Moreover, our comparative experiments have shown that our counting approach is more accurate than other products on the market.

Whenever looking at a stranger's portrait, besides observable appearance, we always build a personality impression implicitly in our subconscious. It is quite interesting to ask how a portrait impresses people. This paper presents a novel method to infer personality impression from portrait. Firstly, a questionnaire is applied to demonstrate the consistence of people's impression. And then personality-related features are explored through the statistical analysis method. Finally, features are trained using Support Vector Machine. Experimental results demonstrate our method could achieve a precision of 52.14% and a recall of 52.78% on inferring 4 personalities from 2,463 randomly selected portraits of people downloaded from "Google images". Improvements of 44.04% and 37.91% are reported compared to a baseline method. And features contribution analysis deeply unveils the correspondence between portrait contents and personality impressions. Demonstrations with respect to visual patterns in portrait collages of different personalities further prove the effectiveness of the proposed method. Furthermore, we apply our method to analyze portraits of Hillary Clinton and obtain an interesting multifaceted figure of this famous politics, which is another proof of both our concept and method.

A new color image watermarking algorithm with resistance to JPEG lossy compression based on quantization index modulation (QIM) is proposed in this paper. As it is known, QIM method can achieve a good balance between the embedding bit rate, robustness and distortion between the original image and the composited image by modulating the source signal into different clusters. The corresponding DCT coefficients margins of any two color channels selected from the three as the source signal is substantiated could achieve a high level embedding robustness and a low level distortion in this paper. However, JPEG lossy compression could bring a destructive influence to the watermark since it discards pretty much image information. In this paper, a compensation function is designed to correct the errors caused by JPEG compression. Experiments show the algorithm proposed has a good performance to resist the high compression ratio JPEG lossy compression and other attacks.