MOTIVATION: The accurate and robust representation of drug molecule features, the prediction of drug-target biomacromolecule interactions, and the determination of physicochemical properties are crucial in drug development. However, these tasks remain challenging due to issues such as the limited generalizability of single-modal representations, the absence of multitask prediction frameworks, and weak adaptability in cold-start scenarios. RESULTS: In this study, we present DrugDL, a framework for comprehensive drug molecule representation and the prediction of multiple downstream tasks, including drug-target interactions, binding affinities, binding sites, physicochemical properties, toxicity, and drug-drug interactions. DrugDL jointly learns representations of the drug chemical space and the target protein biological space, while capturing multiscale interaction mechanisms between drug molecules and target proteins through the integration of cross-modal contrastive learning and single-modal feature enhancement algorithms. Specifically, DrugDL employs a multitask prediction framework to predict multiple properties of drug molecules. In practical applications, it consistently outperforms state-of-the-art methods, particularly in cold-start tasks. The framework has been successfully applied to high-throughput screening, the identification of inhibitors of SARS-CoV-2 and metabolic enzymes, and the prediction of cancer-targeted drugs. Experimental validations on EGFR and ALK targets further demonstrate its effectiveness as a precise drug discovery tool. By enabling accurate molecular representation and multi-property prediction, DrugDL provides end-to-end technical support for drug development, thereby significantly accelerating the drug discovery process. AVAILABILITY AND IMPLEMENTATION: The datasets and code are available at https://github.com/ZhangQi9910/DrugDL. The version of record is archived in Zenodo with the DOI: 10.5281/zenodo.20579718.

Noncoding RNA refers to RNA that does not encode proteins. The lncRNA and miRNA it contains play crucial regulatory roles in organisms, and their aberrant expression is closely related to various diseases. Traditional experimental methods for validating the interactions of these RNAs have limitations, and existing prediction models exhibit relatively limited functionality, relying on isolated feature extraction and performing poorly in handling various types of small sample tasks. This paper proposes an improved de Bruijn graph that can inject RNA structural information into the graph while preserving sequence information. Furthermore, the improved de Bruijn graph enables graph neural networks to learn broader dependencies and correlations among data by introducing richer edge relationships. Meanwhile, the multitask learning model, DVMnet, proposed in this paper can handle multiple related tasks, and we optimize model parameters by integrating the total loss of three tasks. This enables multitask prediction of RNA interactions, disease associations, and subcellular localization. Compared with the best existing models in this field, DVMnet has achieved the best performance with a 3% improvement in the area under the curve value and demonstrates robust results in predicting diseases and subcellular localization. The improved de Bruijn graph is also applicable to various scenarios and can unify the sequence and structural information of various nucleic acids into a single graph.

The human ether-a-go-go-related gene (hERG) cardiac toxicity of a compound refers to its inhibitory effect on the hERG potassium channel. The hERG channel is crucial for cardiac depolarization, and its blockage can lead to prolongation of the QT interval, triggering arrhythmias and posing life-threatening risks. Therefore, assessing hERG cardiac toxicity is a vital consideration in drug development. Traditional assessment methods are complex and have low throughput, making the development of deep learning models to predict this toxicity essential for enhancing drug development efficiency, reducing risks, and promoting personalized treatment. In this paper, we propose a novel multi-type feature fusion framework, MTF-hERG, for accurately predicting the cardiac toxicity of hERG compounds. This framework integrates various molecular features such as molecular fingerprints, 2D molecular images, and 3D molecular graphs to comprehensively capture the intrinsic structures and properties of compounds. By utilizing fully connected neural networks, DenseNet, and Equivariant Graph Neural Networks for feature extraction, we ensure that the model can precisely identify molecular characteristics associated with hERG blocking activity. Through deep fusion of extracted features and the construction of fully connected layers with different activation functions, we achieve classification predictions of whether a compound is an hERG blocker and regression predictions of its hERG inhibitory capacity. When comparing MTF-hERG with other state-of-the-art methods using benchmark datasets, we found that the average ACC, AUC, AUPR, RMSE, and R² values of MTF-hERG were 0.926, 0.943, 0.913, 0.453, and 0.681, respectively. The results demonstrate that MTF-hERG exhibits excellent predictive performance in various scenarios, significantly outperforming the existing baseline models. Furthermore, the visualization results of MTF-hERG not only reveal the key features and decision mechanisms of the model but also provide valuable support for further optimization of molecular structures. Therefore, the MTF-hERG framework is poised to become a powerful tool for predicting the hERG cardiac toxicity of compounds, offering robust support for drug development and exerting a profound impact on human health.

The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. In this study, we propose a multi-modal deep learning framework called MMD-DTA for predicting drug-target binding affinity and binding regions. The model can predict DTA while simultaneously learning the binding regions of drug-target interactions through unsupervised learning. To achieve this, MMD-DTA first uses graph neural networks and target structural feature extraction network to extract multi-modal information from the sequences and structures of drugs and targets. It then utilizes the feature interaction and fusion modules to generate interaction descriptors for predicting DTA and interaction strength for binding region prediction. Our experimental results demonstrate that MMD-DTA outperforms existing models based on key evaluation metrics. Furthermore, external validation results indicate that MMD-DTA enhances the generalization capability of the model by integrating sequence and structural information of drugs and targets. The model trained on the benchmark dataset can effectively generalize to independent virtual screening tasks. The visualization of drug-target binding region prediction showcases the interpretability of MMD-DTA, providing valuable insights into the functional regions of drug molecules that interact with proteins.

The prediction of interaction sites between circular RNA (circRNA) and RNA binding proteins (RBPs) is crucial for regulating diseases and discovering new treatment approaches. Computational models have been widely used to predict circRNA-RBP interaction sites due to the availability of genome-wide circRNA binding event data. However, efficiently obtaining multi-scale circRNA features to improve prediction accuracy remains a challenging problem. In this study, we propose SSCRB, a lightweight model for predicting circRNA-RBP interaction sites. Our model extracts both sequence and structural features of circRNA and incorporates multi-scale features through the attention mechanism. Furthermore, we develop an ensemble model by combining multiple submodels to enhance predictive performance and generalizability. We evaluate SSCRB on 37 circRNA datasets and compare it with other state-of-the-art methods. The average AUC of SSCRB is 97.66%, demonstrating its efficiency and robustness. SSCRB outperforms other methods in terms of prediction accuracy while requiring significantly fewer computational resources.