Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions and 320 unique food items, composed of 563 unique compounds. The potential number of interactions is 87,192 and 92,143 for disjoint and joint versions of the graph. We defined several similarity subnetworks comprising food-drug similarity, drug-drug similarity, and food-food similarity networks. A unique part of the graph involves encoding the food composition as a set of nodes and calculating a content contribution score. To predict new FDIs, we considered several link prediction algorithms and various performance metrics, including the precision@top (top 1%, 2%, and 5%) of the newly predicted links. The shortest path-based method has achieved a precision of 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability, and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. FDMine is publicly available to support clinicians and researchers.

Radiation’s harmful effects on biological organisms have long been studied through mainly evaluating pathological changes in cells, tissues, or organs. Recently, there have been more accessible gene expression datasets relating to radiation exposure studies. This provides an opportunity to analyze responses at the molecular level toward revealing phenotypic differences. Biomarkers in toxicogenomics have been suggested as indicators of radiation exposure and seem to react differently to various dosages of radiation. This study proposes a predictive gene signature specific to radiation exposure and can be used in automatically diagnosing the exposure dose. In searching for a reliable gene set that will correctly identify the exposure dose, consideration needs to be given to the size of the set. For this reason, we experimented with the number of genes used for training and testing. Gene set sizes of 28, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1,000 were tested to find the size that provided the best accuracy across three datasets. Models were then trained and tested using multiple datasets in various ways, including an external validation. The dissimilarities between these datasets provide an analogy to real-world conditions where data from multiple sources are likely to have variances in format, settings, time parameters, participants, processes, and machine tolerances, so a robust training dataset from many heterogeneous samples should provide better predictability. All three datasets showed positive results with the correct classification of the radiation exposure dose. The average accuracy of all three models was 88% for gene sets of both 400 and 1,000 genes. R400 provided the best results when testing the three datasets used in this study. A literature validation of top selected genes shows high relevance of perturbations to adverse effects reported during cancer radiotherapy.

Predicting drug–protein interactions (DPIs) is a critical challenge in bioinformatics and drug discovery, as each computational approach provides only a partial view of these complex molecular relationships. Deep learning techniques such as graph neural networks (GNNs) capture local structural patterns from molecular graphs, whereas classical algorithms like Random Forest (RF) leverage global molecular descriptors. We introduce Structure-Guided Curriculum Learning forDrug-Protein Interaction Prediction (SGCL-DPI), a structure-guided curriculum learning framework that initially leverages the global molecular insights captured by a RF model to guide and progressively refine the structural pattern learning of a GNN, enhancing drug–protein interaction prediction accuracy. SGCL-DPI employs a curriculum learning strategy in which an RF teacher model provides initial high-level predictive guidance to a GNN student model, and the focus of training gradually shifts from the RF to the GNN. The training objective integrates three components: binary cross-entropy for correct interaction classification, knowledge distillation to align the GNN’s outputs with the RF’s predictions, and a structural consistency term to maintain similarity-based relational patterns in the learned representations. We evaluated SGCL-DPI on two benchmark datasets: BindingDB and a challenging STITCH-derived dataset. On the BindingDB dataset, a standalone RF baseline using classical molecular descriptors achieved an area under the receiver operating characteristic curve (AUC-ROC) of 99.18% and an area under the precision–recall curve (AUPR) of 99.14%, outperforming many deep learning models. This result highlights the strong predictive power of traditional descriptors on this dataset. On the more difficult STITCH-derived hard split, SGCL-DPI attained a balanced performance, with an F1-score of 67.06%, an AUC-ROC of 82.33%, and an AUPR of 71.69%. Notably, the model outperformed both a purely GNN-based deep model and the traditional RF baseline in terms of F1-score, demonstrating superior ability to predict interactions for entirely unseen drug–protein pairs. These findings demonstrate that SGCL-DPI effectively bridges classical machine learning and deep learning approaches for DPI prediction. By integrating global descriptor-based knowledge with graph-based structural learning, the proposed framework significantly improves predictive accuracy and generalization on challenging interaction prediction tasks, highlighting a promising direction for future DPI prediction research. The full implementation of the proposed framework is publicly available at https://github.com/soufanom/SGCL-DPI to ensure transparency and reproducibility.

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.