Accurate drug-drug interaction (DDI) prediction is crucial for optimizing the efficacy of combination therapies and minimizing adverse effects. Most existing methods rely on single features and struggle to integrate structural and sequential drug information. Additionally, prediction bias caused by class imbalance remains a significant challenge. To address these issues, this study proposes a multi-source example-driven learning framework for DDI (MEDL-DDI) that jointly models structural and sequential drug representations to achieve robust multimodal fusion and mitigate class imbalance. MEDL-DDI enriches SMILES with chemical knowledge, extracts global semantic features via a Transformer, and identifies key substructures through a graph information bottleneck. Moreover, an example-driven mechanism guided by example centers enhances the model's ability to recognize minority classes. Experimental results on three benchmark datasets validate that MEDL-DDI outperforms state-of-the-art methods. The case study on cardiovascular drug interactions further highlights MEDL-DDI's practical value and applicability.

Synergistic drug combinations represent a promising strategy for enhancing cancer treatment efficacy. However, existing models struggle to effectively integrate heterogeneous data and capture complex interactions across system and molecular levels, limiting their predictive performance. To address this, we propose a multi-level information capture model (MMSSyn) capable of understanding drug synergy mechanisms through hierarchical information. First, we utilize a system relationship awareness module (SRAM) to capture system-level interaction patterns via modeling high-order associations between drugs and cell lines. Subsequently, a molecular substructure learning module (MSLM) refines molecular-level representations by learning informative substructure features, and a multi-level information fusion module (MIFM) is proposed to adaptively integrate features from both the system and molecular levels. MMSSyn achieves state-of-the-art Area Under Curve (AUC) scores of 97.9% on the Merck and 96.3% on the DrugComb benchmarks, demonstrating superior performance and strong generalization ability across datasets.

RGB images provide a practical and cost-effective method of caries detection. However, the ambiguity of RGB caries images may lead to labeling errors during annotation, which can reduce the robustness of caries classification models. To address this, we propose Core Inter-Category Contrastive Learning (CICC) to improve the robustness of caries classification models. Rather than relying on traditional network fine-tuning, CICC focuses on improving the robustness of the model to label errors from a novel perspective by identifying core data that are highly relevant to the caries category. CICC utilizes the Jensen-Shannon Divergence to select core data, mitigating the impact of label errors on model performance. Inter-Category Contrastive Learning enhances feature representations of samples from different categories to improve the model's discrimination between caries categories. We validated the effectiveness of CICC in improving model robustness from model optimization and experimental results. Extensive experiments demonstrate that CICC significantly outperforms other comparative methods in caries classification performance and robustness. Our code is available at: https://github.com/papercode-for-cheung/CICC.

The prediction of binding free energy changes ($\Delta \Delta G$) caused by mutations in protein complexes is crucial for understanding disease mechanisms and designing antibodies. Approximately 60% of pathogenic missense mutations lead to functional abnormalities by disrupting molecular interactions. However, although existing $\Delta \Delta G$ predictors exhibit strong performance in benchmarks, they suffer from inadequate generalization, a misalignment between evaluation metrics and practical needs, and poor adaptability to complex mutation scenarios. This study systematically assessed eight mainstream predictors, covering both physical energy function-based and machine learning-based methods, and constructed an independent evaluation set. This study employed multi-dimensional metrics, including regression accuracy and classification capability, while also analyzing the performance variations of predictors across different mutation types, stability categories, and microenvironments of protein mutation sites. The results indicate that >60% of predictors (5 out of 8) predictors exhibit a systematic bias toward overestimating mutational instability. In the three-class classification task, predictors demonstrate a limited ability to identify stabilizing mutations ($\Delta \Delta G< -0.5$ kcal/mol), with recall rates <0.1 for this class, and overall predictive efficacy depends on the protein local structure. In summary, this study reveals the limitations of current $\Delta \Delta G$ predictors in terms of generalization and adaptability to complex scenarios, thus providing a reference for the optimization and practical application of $\Delta \Delta G$ prediction methods. It suggests that future breakthroughs can be achieved by constructing balanced and standardized datasets alongside developing local-global fusion algorithms.