ReviewCurrent drug delivery2024
Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.
Review in Current drug delivery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Artificial intelligence and multiomics integration for Parkinson's disease drug development.Molecules and cells · 2026Review
- Targeting mutant p53 in cancer: from mechanistic insights to therapeutic strategies.Cellular & molecular biology letters · 2026Review
- Article
- Quantum Computing in Drug Discovery Techniques, Challenges, and Emerging Opportunities.Current drug discovery technologies · 2026Review
- Data-driven precision: artificial intelligence redefining immunoradiotherapy in advanced pancreatic cancer.Frontiers in pharmacology · 2026Review
- Transformer graph variational autoencoder for generative molecular design.Biophysical journal · 2025Article
- Unlocking the potential: multimodal AI in biotechnology and digital medicine-economic impact and ethical challenges.NPJ digital medicine · 2025Review
- The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries.AAPS PharmSciTech · 2025Review
- Convergent Mechanisms in Virus-Induced Cancers: A Perspective on Classical Viruses, SARS-CoV-2, and AI-Driven Solutions.Infectious disease reports · 2025Article
- Article
- The Impact of Artificial Intelligence on Cancer Diagnosis and Treatment: A Review.Cancer informatics · 2025Review
- Cancer pharmacoinformatics: Databases and analytical tools.Functional & integrative genomics · 2024Review
- Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.Current oncology (Toronto, Ont.) · 2024Review
- The impact of generative artificial intelligence (AI) on the development of personalized pharmaceuticals and the future of precision medicine.EXCLI journal · 2024Article
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
Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.
Indexed as
Identifiers
37670704What 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.