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The simultaneous use of two or more drugs in clinical treatment may raise the risk of a drug–drug interaction (DDI). DDI prediction is very important to avoid adverse drug events in combination therapy. Recently, deep learning methods have been applied successfully to DDI prediction and improved prediction performance. However, there are still some problems with the present models, such as low accuracy due to information loss during molecular representation or incomplete drug feature mining during the training process. Aiming at these problems, this study proposes an accurate and simple framework named PTB-DDI for drug–drug interaction prediction. The PTB-DDI framework consists of four key modules: (1) ChemBerta tokenizer for molecular representation, (2) Bidirectional Long Short-Term Memory (BiLSTM) to capture the bidirectional context-aware features of drugs, (3) Multilayer Perceptron (MLP) for mining the nonlinear relationship of drug features, and (4) interaction predictor to perform an affine transformation and final prediction. In addition, we investigate the effect of dual-mode on parameter-sharing and parameter-independent within the PTB-DDI framework. Furthermore, we conducted comprehensive experiments on the two real-world datasets (i.e., BIOSNAP and DrugBank) to evaluate PTB-DDI framework performance. The results show that our proposed framework has significant improvements over the baselines based on both datasets. Based on the BIOSNAP dataset, the AUC-ROC, PR-AUC, and F1 scores are 0.997, 0.995, and 0.984, respectively. These metrics are 0.896, 0.873, and 0.826 based on the DrugBank dataset. Then, we conduct the case studies on the three newly approved drugs by the Food and Drug Administration (FDA) in 2024 using the PTB-DDI framework in dual modes. The obtained results indicate that our proposed framework has advantages for predicting drug–drug interactions and that the dual modes of the framework complement each other. Furthermore, a free website is developed to enhance accessibility and user experience. [ABSTRACT FROM AUTHOR]

Circadian rhythms regulate the body's homeostasis through the temporal control of tissue-specific circadian rhythm control genes. Circadian rhythm disorders (CRD) affect the expression levels of circadian rhythms-associated genes in brain and muscle aryl hydrocarbon receptor nuclear translocator-like-1(BMAL1), which is thought to contribute to metabolic disorders and an altered immune system. However, the relationship between CRD and the development of periodontitis was poorly reported. Therefore, this study aimed to investigate the role played by BMAL1 in periodontitis. We used a modified multi-platform approach (MMPM) to induce circadian rhythm disturbances in rats to investigate the role of BMAL1 in periodontitis. Our results showed significant downregulation of BMAL1 in the CRD with periodontitis group, significant resorption of alveolar bone, increased osteoclast differentiation, and upregulation of the inflammatory signaling molecule NF-κB. In addition, apoptosis and oxidative stress levels were increased in periodontal tissues. Collectively, our study suggests that BMAL1 is a key regulator in periodontitis exacerbated by CRD and that CRD may lead to the downregulation of BMAL1, thereby exacerbating oxidative stress and apoptosis in periodontal tissues. Our study found that BMAL1 may be associated with the progression of periodontitis and provides a new perspective on the treatment of periodontitis. [ABSTRACT FROM AUTHOR]