ArticlePharmaceuticals (Basel, Switzerland)2022
Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda.
Article in Pharmaceuticals (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- An advanced wide-and-deep learning framework for soybean price forecasting using market, weather, trade, and supply data.MethodsX · 2026Article
- AI-driven drug design: a comprehensive review.Journal of computer-aided molecular design · 2026Review
- Pharmacology through the lens of bibliometrics: global trends, research hotspots, and collaborations (2015-2026).Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction.BMC biology · 2025Article
- Deep Learning-Based Drug Compounds Discovery for Gynecomastia.Biomedicines · 2025Article
- GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph.BMC biology · 2024Article
- Artificial Intelligence in Drug Discovery: A Bibliometric Analysis and Literature Review.Mini reviews in medicinal chemistry · 2024Review
- Artificial Intelligence for Cancer Detection-A Bibliometric Analysis and Avenues for Future Research.Current oncology (Toronto, Ont.) · 2023Review
- Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.Exploration of targeted anti-tumor therapy · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.
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.