ReviewDrug design, development and therapy2025
Explainable Artificial Intelligence in the Field of Drug Research.
Review in Drug design, development and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Machine-Learning-Driven Molecular Design and Structure-Property-Performance Relationships in Pharmaceutical Chemistry.Molecules (Basel, Switzerland) · 2026Review
- Sequence-based prediction of drug-target binding using machine learning, deep learning and ensemble models without 3D structural information.Scientific reports · 2026Article
- INBAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Framework for evaluating explainable AI in antimicrobial drug discovery.Journal of cheminformatics · 2026Article
- Ginger Bioactives as Multi-Target Therapeutics: Mechanisms, Delivery Innovation, and Human Health Impact.Nutrients · 2026Review
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Exploring CHO cell stability during prolonged passaging via eXplainable AI driven flux balance analysis.NPJ systems biology and applications · 2026Article
- Artificial intelligence-driven drug discovery: a deep learning paradigm shift in pharmaceutical research and development.Frontiers in pharmacology · 2026Review
- Review
- AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential.NPJ digital medicine · 2025Review
- Advances and Challenges in Drug Screening for Cancer Therapy: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2025Review
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- Construction and validation of a machine learning-based model predicting early readmission in patients with decompensated cirrhosis: a prospective two-center cohort study.BioData mining · 2025Article
- Explainable Artificial Intelligence: A Perspective on Drug Discovery.Pharmaceutics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the widespread use of artificial intelligence (AI) and big data technologies in drug research has significantly accelerated the drug development process. However, their black-box nature makes it challenging to evaluate their effectiveness and safety. The interpretability of models has become a key issue in the application of AI in the drug development. In this paper, a bibliometric approach has been adopted to systematically analyze the application of Explainable Artificial Intelligence (XAI) techniques in drug research, with an in-depth discussion of the developmental trends, geographical distribution, journal preferences, major contributors, and research hotspots. In addition, the research results of XAI are summarized in the three directions of chemical, biological, and traditional Chinese medicine, and the future research directions and development trends are envisioned in order to promote the in-depth application of XAI technology in drug discovery and continuous innovation.
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.