Property-constrained molecular generation and editing are crucial in AI-driven drug discovery but remain hindered by two factors: (i) capturing the complex relationships between molecular structures and multiple properties remains challenging, and (ii) the narrow coverage and incomplete annotations of molecular properties weaken the effectiveness of property-based models. To tackle these limitations, we propose HSPAG, a data-efficient framework featuring hierarchical structure–property alignment. By treating SMILES and molecular properties as complementary modalities, the model learns their relationships at atom, substructure, and whole-molecule levels. Moreover, we select representative samples through scaffold clustering and hard samples via an auxiliary variational auto-encoder (VAE), substantially reducing the required pre-training data. In addition, we incorporate a property relevance-aware masking mechanism and diversified perturbation strategies to enhance generation quality under sparse annotations. Experiments demonstrate that HSPAG captures fine-grained structure–property relationships and supports controllable generation under multiple property constraints. Two real-world case studies further validate the editing capabilities of HSPAG.

In recent years, there has been an increased recognition of the importance of professional development (PD) programs that prepare grade K-5 teachers to teach computer science (CS) and computational thinking (CT), primarily through integrating these topics into core curriculum. Prior studies using quantitative and qualitative methods have examined outcomes of such PD programs, revealing important aspects to measure when evaluating teacher readiness and concerns about integrating CS and CT into their instruction, including individual capacity, network and resources, and barrier and concerns. The current study aims to fill in the gap of validated instruments to measure teacher readiness and concerns to teach or integrate CS and CT in the K-5 educational context. An instrument was developed based on existing validated measures and adapted for the K-5 teacher population. Pre-survey data from 641 K-5 teachers trained to integrate CS and CT into their instruction was used to validate the instrument. Internal reliability and exploratory factor analysis (EFA) were conducted on two subscales: readiness and concerns to integrate CS and CT. Results suggested satisfactory psychometric properties for both subscales overall, but suggested the removal of one item on each subscale to further improve internal reliability and factor structure. Pearson correlation results suggested the readiness and concerns subscales were moderately correlated (r=-0.38), indicating readiness and concerns to be two correlated but separate aspects in teachers' preparedness to integrate CS and CT in K-5 classrooms. The development and validation of this instrument provide a reliable and valuable tool to assess future PD program outcomes in K-5 CS education.

Accurate medical image segmentation is a pivotal task in medical image analysis, serving as the foundation for extracting critical clinical information and directly influencing the precision of disease identification, diagnosis, and treatment decision-making. Although Convolutional Neural Networks (CNNs) and Transformers have demonstrated remarkable performance in medical image segmentation, their sensitivity to variations in target size and morphology remains insufficient, resulting in limited capability of single-scale features to comprehensively capture multi-target details. Current approaches also face bottlenecks, including restricted global context modeling capabilities, high computational complexity, and inefficient multilevel feature fusion. To address these challenges in medical image segmentation, this study proposes an innovative architecture named GroupTransUNet. The model implements dual enhancements based on the UNet framework: (i) A novel Next-Generation Transformer Block (NGTB) is designed for the bottleneck layer, which organically integrates Efficient Multi-Head Self-Attention (E-MHSA) with Convolution-Enhanced Multi-Layer Perceptron (MLP) through a complementary mechanism to simultaneously enhance global semantic understanding and local detail characterization. (ii) The Grouped Feature Fusion Module (GFFM) is introduced in skip connections, employing grouped convolutions and multi-dilation rate strategies to construct multi-scale contextual receptive fields while preserving detailed integrity, thereby significantly improving feature fusion efficiency. Experimental results on the Synapse and ACDC datasets validate the effectiveness of our proposed GroupTransUNet. The source code can be obtained at https://anonymous.4open.science/r/GroupTransUNetA3D6.

Drug sensitivity is essential for identifying effective treatments. Meanwhile, circular RNA (circRNA) has potential in disease research and therapy. Uncovering the associations between circRNAs and cellular drug sensitivity is crucial for understanding drug response and resistance mechanisms. In this study, we proposed DeepHeteroCDA, a novel circRNA-drug sensitivity association prediction method based on multi-scale heterogeneous network and graph attention mechanism. We first constructed a heterogeneous graph based on drug-drug similarity, circRNA-circRNA similarity, and known circRNA-drug sensitivity associations. Then, we embedded the 2D structure of drugs into the circRNA-drug sensitivity heterogeneous graph and use graph convolutional networks (GCN) to extract fine-grained embeddings of drug. Finally, by simultaneously updating graph attention network for processing heterogeneous networks and GCN for processing drug structures, we constructed a multi-scale heterogeneous network and use a fully connected layer to predict the circRNA-drug sensitivity associations. Extensive experimental results highlight the superior of DeepHeteroCDA. The visualization experiment shows that DeepHeteroCDA can effectively extract the association information. The case studies demonstrated the effectiveness of our model in identifying potential circRNA-drug sensitivity associations. The source code and dataset are available at https://github.com/Hhhzj-7/DeepHeteroCDA.

MOTIVATION: Protein-RNA interactions play a pivotal role in biological processes and disease mechanisms, with hotspot residues being critical for targeted drug design. Traditional experimental methods for identifying hotspot residues are often inefficient and expensive. Moreover, many existing prediction methods rely heavily on high-resolution structural data, which may not always be available. Consequently, there is an urgent need for an accurate and efficient sequence-based computational approach for predicting hotspot residues in protein-RNA complexes. RESULTS: In this study, we introduce DeepHotResi, a sequence-based computational method designed to predict hotspot residues in protein-RNA complexes. DeepHotResi leverages a pretrained protein language model to predict protein structure and generate an amino acid contact map. To enhance feature representation, DeepHotResi integrates the Squeeze-and-Excitation (SE) module, which processes diverse amino acid-level features. Next, it constructs an amino acid feature network from the contact map and SE-module-derived features. Finally, DeepHotResi employs a graph attention network to model hotspot residue prediction as a graph node classification task. Experimental results demonstrate that DeepHotResi outperforms state-of-the-art methods, effectively identifying hotspot residues in protein-RNA complexes with superior accuracy on the test set. AVAILABILITY AND IMPLEMENTATION: The source code and dataset are available at https://github.com/Q1DT/DeepHotResi.

Solubility is not only a significant physical property of molecules but also a vital factor in small-molecule drug development. Determining drug solubility demands stringent equipment, controlled environments, and substantial human and material resources. The accurate prediction of drug solubility using computational methods has long been a goal for researchers. In this study, we introduce MSCSol, a solubility prediction model that integrates multidimensional molecular structure information. We incorporate a graph neural network with geometric vector perceptrons (GVP-GNN) to encode 3D molecular structures, representing spatial arrangement and orientation of atoms, as well as atomic sequences and interactions. We also employ Selective Kernel Convolution combined with Global and Local attention mechanisms to capture molecular features context at different scales. Additionally, various descriptors are calculated to enrich the molecular representation. For the 2D and 3D structural data of molecules, we design different data augmentation strategies to enhance generalization ability and prevent the model from learning irrelevant information. Extensive experiments on benchmark and independent datasets demonstrate MSCSol's superior performance. Ablation studies further confirm the effectiveness of different modules. Interpretability analysis highlights the importance of various atomic groups and substructures for solubility and verifies that our model effectively captures functional molecular structures and higher-order knowledge.

The subcellular localization of long non-coding RNAs (lncRNAs) is fundamental to understanding their functional roles in gene regulation and disease mechanisms. Existing prediction models typically rely on single-source features, which often fail to capture the full complexity of lncRNA localization. To address this limitation, we propose MFF-LncLoc, a Transformer-based model designed to integrate multiple feature types for improved prediction accuracy. MFF-LncLoc processes embedding matrices derived from a non-overlapping trinucleotide approach, leveraging the Transformer’s capacity to capture both contextual and positional information to extract global features. Unlike conventional models that primarily focus on k-mer frequency features, MFF-LncLoc incorporates a diverse set of features, including statistical properties, sequence characteristics, and secondary structure information. These multi-dimensional features are then processed by a Convolutional Neural Network (CNN) to extract local sequence patterns, followed by a fully connected layer for subcellular localization prediction. Ablation studies confirm that the inclusion of multi-feature data significantly enhances model performance. MFF-LncLoc outperforms existing models across several metrics, including accuracy (ACC), macro-recall, macro-F1, AUC, and AUPR, demonstrating that the integration of diverse features offers substantial improvements over traditional single-feature approaches. The source code and dataset are available at https://github.com/ZiyuFanCSU/MFFLncLoc.

This study investigates the role of circular RNAs (circRNAs) in drug sensitivity, with a focus on their potential to inform personalized medicine. While current methods for identifying circRNA-drug sensitivity associations are resource-intensive, we propose LSNSCDA, a novel prediction algorithm that integrates Local Smoothing Graph Neural Networks (LS-GNN) and Credible Negative Sampling (CNS) to improve prediction accuracy. Our approach overcomes the challenges of fixed-length propagation in graph neural networks and the unreliability of randomly sampled negative instances. Experimental results show that LSNSCDA outperforms existing models, providing more reliable predictions and valuable insights into cancer treatment. Extensive evaluation confirms the effectiveness of each component of our model, while case studies further demonstrate its practical applicability. The source code and dataset are available at https://github.com/ZiyuFanCSU/LSNSCDA.

MOTIVATION: Effective molecular representation is critical in drug development. The complex nature of molecules demands comprehensive multi-view representations, considering 1D, 2D, and 3D aspects, to capture diverse perspectives. Obtaining representations that encompass these varied structures is crucial for a holistic understanding of molecules in drug-related contexts. RESULTS: In this study, we introduce an innovative multi-view contrastive learning framework for molecular representation, denoted as MolMVC. Initially, we use a Transformer encoder to capture 1D sequence information and a Graph Transformer to encode the intricate 2D and 3D structural details of molecules. Our approach incorporates a novel attention-guided augmentation scheme, leveraging prior knowledge to create positive samples tailored to different molecular data views. To align multi-view molecular positive samples effectively in latent space, we introduce an adaptive multi-view contrastive loss (AMCLoss). In particular, we calculate AMCLoss at various levels within the model to effectively capture the hierarchical nature of the molecular information. Eventually, we pre-train the encoders via minimizing AMCLoss to obtain the molecular representation, which can be used for various down-stream tasks. In our experiments, we evaluate the performance of our MolMVC on multiple tasks, including molecular property prediction (MPP), drug-target binding affinity (DTA) prediction and cancer drug response (CDR) prediction. The results demonstrate that the molecular representation learned by our MolMVC can enhance the predictive accuracy on these tasks and also reduce the computational costs. Furthermore, we showcase MolMVC's efficacy in drug repositioning across a spectrum of drug-related applications. AVAILABILITY AND IMPLEMENTATION: The code and pre-trained model are publicly available at https://github.com/Hhhzj-7/MolMVC.