ReviewDrug development research2025
Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.
Review in Drug development research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 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
10 authors.
Funding
Abstract
Cancer affects one in three to four people globally, with over 20 million new cases and 10 million deaths annually, projected to rise to 35 million cases by 2050. Developing effective cancer treatments is crucial, but the drug discovery process is a highly complex and expensive endeavor, with success rates sitting well below 10% for oncologic therapies. More recently, there has been a growing interest in Artificial intelligence (AI) due to its potential to significantly enhance the success rates by processing large data sets, identifying patterns, and making autonomous decisions. The primary aim of this literature review is to examine the potential that state-of-the-art AI tech-nologies have to enhance and complement well-established research methods used in cancer drug development, such as QSAR, interactions prediction, and ADMET prediction, among others. The basic technical aspects of computational technologies are clarified, and key terms commonly asso-ciated with AI are defined. Current applications and case studies from academia and industry are presented to highlight AI's potential to accelerate progress in cancer drug research. Challenges and disadvantages of AI are also acknowledged, and it is discussed that future research should focus on overcoming its limitations to maximize its impact in cancer treatment.
Indexed as
Identifiers
What Socratic 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 Socratic graph.