ArticleBMC bioinformatics2024
CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Prioritising search for virtual screening via preliminary interpretable low-feature likelihood-based rankings of drug-target activity measures.BMC bioinformatics · 2026Article
- Article
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Informative relational learning for adverse reaction prediction with enhanced generalization to novel drugs.Bioinformatics (Oxford, England) · 2026Article
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.Bioinformatics (Oxford, England) · 2026Article
- Contextual Representation Learning With ResNet Refinement and Deep Classification for Enhanced Drug Target Interaction Prediction.Chemical biology & drug design · 2026Article
- ExplainBind: Explainable Physicochemical Determinants of Protein-Ligand Binding via Non-Covalent Interactions.bioRxiv : the preprint server for biology · 2026Article
- Protein and ligand novelty in drug-target interaction prediction: a dual-encoder fusion strategy for more interpretable and generalizable modeling.BMC bioinformatics · 2026Article
- LiBRe: A Ligand-Aware Sequence-Based Binding Residue Prediction Model for Virtual Screening.Journal of chemical information and modeling · 2026Article
- A comprehensive review of neural network-based approaches for drug-target interaction prediction.Molecular diversity · 2026Review
- C2M-Mamba: drug-drug interaction prediction based on cross-modal cross-Mamba.BMC bioinformatics · 2026Article
- Hybrid Dual-Context Prompted Cross-Attention Framework with Language Model Guidance for Multi-Label Prediction of Human Off-Target Ligand-Protein Interactions.International journal of molecular sciences · 2026Article
- Article
- LNMGAT: a laplacian regularized pseudo-negative mining graph attention network for robust drug-target interaction prediction under multi-scenario cold-start settings.Frontiers in bioinformatics · 2026Article
- KG-DFI: A Prediction of Drug-Food Interactions Based on Knowledge Graph Embedding.Computational and structural biotechnology journal · 2026Article
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- DCGAT-DTI: dynamic cross-graph attention network for drug-target interaction prediction.Bioinformatics advances · 2026Article
- Repurposing AI for protein interactions and dynamics: opportunities, limitations, and lessons.Frontiers in bioinformatics · 2026Review
- A Systematic Review of Drug-Related InteractionsUtilizing Deep Learning and LLMs for Prediction and Mitigation.ACS omega · 2025Review
- Host-Microbe Interactions: Prospects of Machine Learning and Deep Learning Technologies in Animal Viral Disease Management.Veterinary sciences · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Accurate and efficient prediction of drug-target interaction (DTI) is critical to advance drug development and reduce the cost of drug discovery. Recently, the employment of deep learning methods has enhanced DTI prediction precision and efficacy, but it still encounters several challenges. The first challenge lies in the efficient learning of drug and protein feature representations alongside their interaction features to enhance DTI prediction. Another important challenge is to improve the generalization capability of the DTI model within real-world scenarios. To address these challenges, we propose CAT-DTI, a model based on cross-attention and Transformer, possessing domain adaptation capability. CAT-DTI effectively captures the drug-target interactions while adapting to out-of-distribution data. Specifically, we use a convolution neural network combined with a Transformer to encode the distance relationship between amino acids within protein sequences and employ a cross-attention module to capture the drug-target interaction features. Generalization to new DTI prediction scenarios is achieved by leveraging a conditional domain adversarial network, aligning DTI representations under diverse distributions. Experimental results within in-domain and cross-domain scenarios demonstrate that CAT-DTI model overall improves DTI prediction performance compared with previous methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.