Graph-Based Machine Learning for Predicting Drug–Drug Interactions: A Systematic Review

Tail—aware heterogeneous graph neural networks for multi—class drug—drug interaction prediction

Among class imbalance strategies tested, Tail-Aware Focal Loss outperforms standard cross-entropy by 3.1% on Macro F1 and 7.4% on Recall, striking a better balance between class performance than either weighted cross-entropy or weighted random sampling.

[Prediction and verification of therapeutic drugs for triple-negative breast cancer using a knowledge graph-based drug repurposing model].

Graph representation and learning for drug administration prediction.

BACKGROUND The management of drug administration to patients promotes the customisation and accuracy of the treatments, reducing the risk of ineffective therapies and negative drug effects, and improving drug efficiency and management. METHODS We propose a graph representation learning model for predicting drug administration using the MIMIC-III database, which contains over 53K critical care admissions. We design a heterogeneous, weighted, directed, and multi-feature graph from patient demographics, diagnoses, and drug administration records. Our method uses a graph convolutional network to process node and edge features, predicting edge connections between patients and drugs. This choice enables us to analyse database properties, such as the relationship between drug administration and demographic classes, as well as the prediction accuracy with respect to drug occurrence. RESULTS We analyse the results in terms of correct, false positive, and false negative edge predictions of drug-patient administration. Our method has an accuracy of [Formula: see text] on the MIMIC-III database, with an F1-score of [Formula: see text]. We discuss the training results regarding convergence and execution time, analyse the accuracy on low-occurrence drugs, and compare our method with previous work. CONCLUSION Previous work has focused primarily on query and classification tasks for feature extraction and diagnosis prediction, achieving comparable accuracy but is often limited to specific pathologies (e.g., diabetes), patient groups (e.g., pregnant women), or drug types (e.g., antibiotics). Our approach processes heterogeneous data encompassing various patient characteristics, pathologies, and drug types, providing a more comprehensive and scalable solution.

Robust Prediction of Drug Interactions using Chemical Descriptors.

T-DDI pairs confidence-stratified predictions with LIME-based feature-level explanations and a web application for screening, supporting more reliable drug safety monitoring, and outperforming all evaluated baselines within the architectures and datasets considered here.

Comment on: “A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review” (Marzouk et al., 2025)

Marzouk et al. reviewed 147 studies on artificial intelligence (AI) applications for predicting drug–drug, drug–disease, and drug–nutrient interactions, providing a broad overview of current machine learning and deep-learning approaches. However, several methodological and conceptual limitations reduce the reproducibility and interpretability of the review. The search strategy appears largely restricted to PubMed with title- and abstract-level filtering, while manual record removal is reported without explicit criteria defining “irrelevant” studies, limiting transparency and reproducibility. Protocol registration, duplicate independent screening, standardized extraction procedures, and formal bias assessment using established frameworks such as ROBIS, PROBAST+AI, and TRIPOD+AI were not clearly reported. The review reports performance metrics such as area under the receiver operating characteristic curve (AUROC), but does not provide a structured framework for interpreting or comparing metrics across heterogeneous datasets, prediction tasks, and evaluation protocols. Because the interpretation of AUROC and precision–recall metrics depends on class prevalence, outcome definition, and the intended prediction task, future reviews should report complementary discrimination metrics, calibration, uncertainty estimates, and external validation rather than assuming that any single metric is universally preferable. Claims of superior model performance should be supported by confidence intervals and statistical comparisons appropriate to the evaluation design, such as paired DeLong testing when applicable. Claims of superior model performance should also be supported by appropriate statistical testing, including methods such as the nonparametric DeLong test. Several conceptual clarifications are also warranted. AI models may prioritize hypotheses but do not replace experimental or clinical validation under current regulatory standards. Furthermore, AUROC should not be conflated with pharmacokinetic area under the curve, and SciBERT should not be characterized as a three-dimensional molecular graph framework. Future reviews should adopt transparent multi-database searches, structured bias assessment, and reproducible reporting practices.

AMIUgraph: analysis and modeling of interactions for utility-driven benchmarking of graph-based models in healthcare.

BACKGROUND Graph-based machine learning approaches, including Knowledge Graph Embedding (KGE) methods and Graph Neural Networks (GNNs), have emerged as powerful tools for modeling complex biomedical data. However, a systematic and clinically grounded comparison of these approaches across heterogeneous healthcare graphs, accounting for both predictive performance and real-world deployment constraints, is still lacking. METHODS We introduce AMIUGraph, a comprehensive benchmarking framework for healthcare link prediction that integrates real-world clinical data with external biomedical knowledge bases. AMIUGraph evaluates eight state-of-the-art models, of which four are knowledge graph embedding (KGE) methods DistMult, CP, ComplEx, and ConvE and four are graph neural network (GNN) architectures GCN, GraphSAGE, GAT, and GIN. The models are evaluated across three heterogeneous bipartite graphs representing Patients-Diseases, Diseases-Drugs, and Drugs-Targets interactions. Models are assessed under both transductive and inductive learning settings using accuracy, AUC, precision, recall, F1-score, and training time as evaluation metrics. RESULTS Experimental results show that model performance is strongly influenced by graph structure and sparsity. GNNs consistently achieve superior predictive performance on sparse interaction graphs, particularly for Diseases-Drugs and Drugs-Targets prediction tasks. In contrast, KGE models demonstrate competitive accuracy with substantially lower computational costs in inductive clinical scenarios involving unseen patients. These trends are especially relevant in clinically realistic settings characterized by multimorbidity, such as gastrointestinal and liver diseases, where frequent patient updates and complex therapeutic interactions are common. CONCLUSION AMIUGraph provides a clinically grounded and utility-driven benchmarking framework that jointly evaluates KGE and GNN models across multiple healthcare graph types and learning settings. The findings offer practical guidance for selecting graph-based models in medical decision-support systems, including applications in gastrointestinal healthcare, while promoting transparency and reproducibility through the public release of all datasets, protocols, and code.