ReviewBriefings in bioinformatics2023
Attention is all you need: utilizing attention in AI-enabled drug discovery.
Review in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 54 papers.
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
54 citing papers in PubMed.
- PhoSARte: identification of SARS-CoV-2 phosphorylation sites using contrastive learning and protein language models.Briefings in bioinformatics · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Graph-based drug-target interaction modeling: from representation learning to output-driven drug discovery.Briefings in bioinformatics · 2026Review
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- Large language models in implant dentistry: a scoping review of applications, performance, and limitations.BMC oral health · 2026Article
- ac4C modification sites prediction in human mRNA: a complete review.Briefings in bioinformatics · 2026Review
- A multicenter-validated interpretable transformer model for pituitary microadenoma detection on non-contrast multiparametric MRI.BMC medical imaging · 2026Article
- Machine Learning-Guided Engineering of Protein Phase Separation Properties in Immune Regulation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Celebrating 25 years of briefings in bioinformatics: a comprehensive bibliometric analysis.Briefings in bioinformatics · 2026Article
- Data-efficient learning for accurate identification of MAPK1 inhibitors using an active meta-deep learning framework.Journal of cheminformatics · 2026Article
- AirSeg: Learnable Interconnected Attention Framework for Robust Airway Segmentation.Journal of imaging informatics in medicine · 2026Article
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Neurosenescence, inflammaging and neuroinflammation in neurodegenerative disorders.Frontiers in aging · 2026Review
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- Research progress of artificial intelligence in high-throughput drug screening.Frontiers in pharmacology · 2026Review
- AI-based neoadjuvant immunotherapy response prediction across pan-cancer: a comprehensive review.Cancer cell international · 2025Review
- Deep learning to predict extrapancreatic perineural invasion at CT images.Annals of medicine · 2025Article
- Artificial intelligence-driven anticancer peptide discovery.iMetaOmics · 2025Review
- Active Stacking-Deep Learning with Strategic Sampling for Small and Imbalanced Chemical Toxicity Prediction.ACS omega · 2025Article
- Artificial intelligence guided Raman spectroscopy in biomedicine: Applications and prospects.Journal of pharmaceutical analysis · 2025Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.
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.