ReviewJournal of pharmaceutical analysis2025
A review of transformer models in drug discovery and beyond.
Review in Journal of pharmaceutical analysis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of ethical considerations of large language models in healthcare and medicine.Frontiers in digital health · 2025Pooled it
- Advances in Sensing Techniques and Deep Learning for Food Quality Detection: Opportunities, Challenges, and Perspectives from Image Recognition to Multimodal Data Fusion.Foods (Basel, Switzerland) · 2026Review
- End-to-End Intelligent Drug Discovery via a Scalable and Explainable Graph-Transformer Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Stereoselective Biotransformation: Transfer of Learning to Advance Drug Metabolism and Biocatalysis.Angewandte Chemie (International ed. in English) · 2026Review
- AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices.Current issues in molecular biology · 2026Review
- From mechanistic modeling to AI-driven design: computational strategies for targeting the γ-secretase complex.Briefings in bioinformatics · 2026Review
- A unified multi-scale deep learning framework for molecular property prediction that bridges molecular structures and fingerprinting.Communications chemistry · 2026Article
- Nature meets machine: the AI renaissance in natural product drug discovery.Natural products and bioprospecting · 2026Review
- AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip.Acta pharmaceutica Sinica. B · 2026Review
- Harnessing artificial intelligence to advance CRISPR-based genome editing technologies.Nature reviews. Genetics · 2026Review
- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- AI-driven drug discovery using transformer-based molecular representation learning.Frontiers in artificial intelligence · 2026Article
- Scaling Biomedical Text-Mining: Transformers, GenAI, and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Natural products against Babesiosis: active compounds, molecular mechanisms, application limitations and translational development.Frontiers in cellular and infection microbiology · 2026Review
- Assessment of transformer-based AI in clinical oncology.BMC medical informatics and decision making · 2025Article
- ToxBERT: an explainable AI framework for enhancing prediction of adverse drug reactions and structural insights.Journal of pharmaceutical analysis · 2025Article
- Large Language Models in Healthcare and Medical Applications: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets.Communications biology · 2025Article
- Simple controls exceed best deep learning algorithms and reveal foundation model effectiveness for predicting genetic perturbations.Bioinformatics (Oxford, England) · 2025Article
- Fingerprint-enhanced hierarchical molecular graph neural networks for property prediction.Journal of pharmaceutical analysis · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.
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.