Aug 2026· Applied and Computational Engineering· 0 citations
TL;DR
Current studies remain constrained by data sparsity, negative-sample bias, insufficient model interpretability and limited prospective validation, so future work should strengthen standardized data integration, mechanism-constrained modeling and joint dose-timing optimization to improve interpretability, generalizability and clinical usability.
Abstract
Drug combination therapy can improve the treatment of complex diseases through multi-target intervention, but the number of candidate combinations expands rapidly with the size of the drug space, making purely high-throughput screening insufficient for research and clinical needs. Network medicine provides biological priors for assessing the rationale of combinations by integrating protein-protein interaction networks, disease modules and drug-target topology, whereas artificial intelligence can learn nonlinear features from heterogeneous data such as chemical structures, targets, omics profiles and cellular phenotypes. This review summarizes recent progress in drug combination synergy prediction based on network medicine and artificial intelligence. It compares network topology methods, conventional machine learning, graph neural networks and multimodal fusion models in terms of principles, applicable scenarios and limitations, and discusses their translational value in cancer, hypertension and other complex diseases. Current studies remain constrained by data sparsity, negative-sample bias, insufficient model interpretability and limited prospective validation. Future work should strengthen standardized data integration, mechanism-constrained modeling and joint dose-timing optimization to improve interpretability, generalizability and clinical usability.
This review systematically summarizes the latest developments in heterogeneous graph neural networks, protein language models, and generative artificial intelligence, pointing out the problems currently being addressed in research such as data sparsity and cold start as well as the manifestations of general machine learning challenges.
Qi-Zhong Yang· ITM Web of Conferences· 0 citations
Mental illnesses are a serious problem worldwide, and many aspects of their disease mechanisms and drug development remain unclear. Traditional drug development processes face several major challenges, including slow speed, high costs, and difficulty in achieving successful cures. However, drug-target interaction based on artificial intelligence is gradually becoming a research hotspot due to its advantages such as accessibility and low cost. This article reviews drug-target interaction research in the development of drugs for mental illnesses. It systematically summarizes and analyzes relevant research progress from three aspects: traditional biomedical methods, machine learning methods, and commonly used datasets, comparing the basic principles, application characteristics, advantages, and limitations of different methods. Based on this, it summarizes current research problems such as insufficient data quality, limited model generalization ability, insufficient interpretability, and insufficient depth in research targeting specific disease stages. Finally, it looks forward to the future development direction of artificial intelligence technology in intelligent drug discovery for mental illnesses, aiming to provide theoretical reference and practical guidance for subsequent related research.
The ability to efficiently analyze large-scale and complex biological data helps artificial intelligence to rapidly revolutionize biomedical research. This paper summarizes the value of artificial intelligence in three biomedical domains: multi-omics data integration for disease prediction, medical image analysis for clinical diagnose and drug discovery. In multi-omics research, artificial intelligence optimizes integration outcomes of heterogeneous biological datasets, deepening cognition of disease mechanisms and polishing predictive models. In medical imaging, deep learning performs excellently in various diagnosis tasks, yet the lack of interpretability brings out limitations. To address the problem, explainable artificial intelligence was generated to effectively increase model transparency and clinical validation. In drug discovery, artificial intelligence significantly accelerates main processes, including virtual screening, drug repurposing and protein structure prediction. In particular, AlphaFold is one of the most paramount breakthroughs. Despite these advancements, data heterogeneity, lack of clinical validation samples and model interpretability shortage restrain the practical usage of relevant techniques. This paper sorts out the potential and limitations of artificial intelligence in biomedical research, pointing out that future research should build more stable and interpretable models.
Shuyue Yang· Applied and Computational En...· 0 citations
This review highlights the synergy between AI and HTS, emphasizing DL techniques such as convolutional neural networks for bioactivity prediction, recurrent neural networks for de novo design, and reinforcement learning for property optimization.
K. Herbetko, Katarzyna Herbetko, Magdalena Mikołajek et al.· Future Medicinal Chemistry· 0 citations
PredictRx shows how AI-driven predictive modeling which can speed up molecular screening and early-stage breast cancer medication discovery shows how AI-driven predictive modeling can speed up molecular screening and early-stage breast cancer medication discovery.
Ritu Chauhan, Neha Pandey, M. Zuhairi· Frontiers in Artificial Inte...· 0 citations
MKASynergy, an adaptive drug synergy prediction method based on a mixture-of-experts kernel mechanism, achieves competitive predictive performance and visualization analysis confirms the model’s effectiveness in feature decoupling and helps interpret latent drug synergistic mechanisms.
Cundong Lin, Jiancheng Ni, Ying Yang et al.· Network Modeling Analysis in...· 0 citations
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