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.

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: Nanopore direct RNA sequencing (DRS) enables the detection of RNA N6-methyladenosine (m6A) without extra laboratory techniques. A number of supervised or comparative approaches have been developed to identify m6A from Nanopore DRS reads. However, existing methods typically utilize either statistical features of the current signals or basecalling-error features, ignoring the richer information of the raw signals of DRS reads. RESULTS: Here, we propose RedNano, a deep-learning method designed to detect m6A from Nanopore DRS reads by utilizing both raw signals and basecalling errors. RedNano processes the raw-signal feature and basecalling-error feature through residual networks. We validated the effectiveness of RedNano using synthesized, Arabidopsis, and human DRS data. The results demonstrate that RedNano surpasses existing methods by achieving higher area under the ROC curve (AUC) and area under the precision-recall curve (AUPRs) in all three datasets. Furthermore, RedNano performs better in cross-species validation, demonstrating its robustness. Additionally, when detecting m6A from an independent dataset of Populus trichocarpa, RedNano achieves the highest AUC and AUPR, which are 3.8%-9.9% and 5.5%-13.8% higher than other methods, respectively. AVAILABILITY AND IMPLEMENTATION: The source code of RedNano is freely available at https://github.com/Derryxu/RedNano.