ArticleBiomolecules2022
Multi-TransDTI: Transformer for Drug-Target Interaction Prediction Based on Simple Universal Dictionaries with Multi-View Strategy.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Bidirectional cross-modal fusion with tensor interaction for drug-target binding prediction.Scientific reports · 2026Article
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- 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
- CAMF-DTI: Enhancing Drug-Target Interaction Prediction via Coordinate Attention and Multi-Scale Feature Fusion.Current issues in molecular biology · 2025Article
- BiMA-DTI: a bidirectional Mamba-Attention hybrid framework for enhanced drug-target interaction prediction.BMC biology · 2025Article
- Article
- GiGs: graph-based integrated Gaussian kernel similarity for virus-drug association prediction.Briefings in bioinformatics · 2025Article
- Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review.npj biomedical innovations · 2025Review
- Attention-based approach to predict drug-target interactions across seven target superfamilies.Bioinformatics (Oxford, England) · 2024Article
- Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction.iScience · 2024Article
- PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning.International journal of molecular sciences · 2023Article
- EFMSDTI: Drug-target interaction prediction based on an efficient fusion of multi-source data.Frontiers in pharmacology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction on drug-target interaction has always been a crucial link for drug discovery and repositioning, which have witnessed tremendous progress in recent years. Despite many efforts made, the existing representation learning or feature generation approaches of both drugs and proteins remain complicated as well as in high dimension. In addition, it is difficult for current methods to extract local important residues from sequence information while remaining focused on global structure. At the same time, massive data is not always easily accessible, which makes model learning from small datasets imminent. As a result, we propose an end-to-end learning model with SUPD and SUDD methods to encode drugs and proteins, which not only leave out the complicated feature extraction process but also greatly reduce the dimension of the embedding matrix. Meanwhile, we use a multi-view strategy with a transformer to extract local important residues of proteins for better representation learning. Finally, we evaluate our model on the BindingDB dataset in comparisons with different state-of-the-art models from comprehensive indicators. In results of 100% BindingDB, our AUC, AUPR, ACC, and F1-score reached 90.9%, 89.8%, 84.2%, and 84.3% respectively, which successively exceed the average values of other models by 2.2%, 2.3%, 2.6%, and 2.6%. Moreover, our model also generally surpasses their performance on 30% and 50% BindingDB datasets.
Indexed as
Identifiers
What OpenQuestion holds
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.