2026· ITM Web of Conferences· 0 citations· 7 references
TL;DR
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.
Abstract
Drug-target interaction (DTI) prediction is an efficient pre-screening method that uses algorithmic models to assess the binding potential of drug molecules to protein targets. Current research is accelerating towards the integration of heterogeneous graph neural networks, protein language models, and generative artificial intelligence. This review systematically summarizes the latest developments in these technologies, 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 such as recommendation systems and noise learning in the biomedical field; Interpret representative models such as Dual Heterogeneous Graph Transformer for Drug-Target Interaction (DHGT-DTI) and Graph Positional encoding and Sequence features for Drug-Target Interaction (GPS-DTI). The combination of dual perspective learning, equivariant graph convolution, and attention mechanism enhances the understanding and reasoning ability of these models in complex biological networks. The generative artificial intelligence diffusion model has opened up a path for developing new drugs through structured and data enhanced approaches. Research has shown that important issues related to computational performance, interpretability, and data compatibility still need to be addressed in existing models. Building a high-performance, multifunctional pre trained model for large-scale biomolecules should be a key direction for development.
A systematic reference for future algorithm design, mechanism exploration, and real-world drug discovery applications for AI-driven DTI prediction methodologies, covering binding theories, task formulations, data representation, model design, translational applications, and unresolved challenges.
Jiaxuan Hu, Lianlian Wu, Song He et al.· Journal of Chemical Informat...· 0 citations
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.
Haoyang Su· Applied and Computational En...· 0 citations
A DTI prediction method based on the global self-attentive pooled graph neural network and protein pretraining model, called T-pGNN4DTI, which uses a global self-attention pooled graph neural network to learn more meaningful features of the drug molecule.
By integrating disease-specific context into molecular generation, DrugGen-2 advances AI-assisted drug discovery, offering a powerful tool for de novo design and drug repurposing that accounts for the complex interplay between diseases and molecular targets.
Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami et al.· 0 citations
Drug-drug interactions (DDIs) represent a substantial challenge in contemporary
pharmacotherapy, especially given polypharmacy, the effects of foods, and the modification
of host-microbiota systems on drugs. Although useful, existing DDI identification techniques have
many constraints related to cost, time, and scalability.
A literature review was conducted using PubMed, Scopus, Web of Science, and IEEE
Xplore, focusing on machine learning techniques, deep neural architectures, and network-based
models that integrate multi-omic, pharmacological, and clinical data.
By combining chemical, biological, and clinical data into scalable computer platforms,
demonstrated that artificial intelligence techniques, such as machine learning (ML) and deep learning
(DL), are changing the prediction of DDI. Some notable studies, such as DeepDDI, TP-DDI, and
Decagon, use approaches that successfully capture the intricate PK-PD interactions of pharmaceuticals.
On the other hand, food-drug interactions and microbiome-mediated drug interactions were also
successfully predicted using multimodal and graph-based models, respectively.
Critical issues, such as insufficient data, class imbalance, and model interpretability,
must be addressed through explainable AI and multimodal fusion techniques.
The purpose of this article is to present an overview of how artificial intelligence might
serve not only as a tool but also as a strategic solution for safe prescribing and tailored pharmacotherapy,
hence opening up new avenues for the field of drug safety science.
D. Tripathi, Ankita Wal, Vivek Kumar Gupta et al.· Current Bioinformatics· 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.
Hou-Bo Fang· ITM Web of Conferences· 0 citations
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