ReviewPharmaceuticals (Basel, Switzerland)2023
Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.
Review in Pharmaceuticals (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed.
- Targeting GLUTs in Cancer: Mechanisms, Combination Strategies, and Translational Challenges.Current medical science · 2026Review
- Artificial Intelligence in Computer-Aided Drug Design (CADD) Tools for the Finding of Potent Biologically Active Small Molecules: Traditional to Modern Approach.Combinatorial chemistry & high throughput screening · 2026Review
- Next Generation DNA Damage Response Inhibitors: Harnessing Nanocarriers and Tumor Microenvironment for Precision Cancer Therapy.Oncology research · 2026Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Bridging technology and medicine: artificial intelligence in targeted anticancer drug delivery.RSC advances · 2025Review
- Recent progress on nanotechnologies for enhancing blood-brain barrier permeability.Smart molecules : open access · 2025Review
- Harnessing Artificial Intelligence in Pediatric Oncology Diagnosis and Treatment: A Review.Cancers · 2025Review
- Integrating AI into Cancer Immunotherapy-A Narrative Review of Current Applications and Future Directions.Diseases (Basel, Switzerland) · 2025Review
- Advancements in pathology: Digital transformation, precision medicine, and beyond.Journal of pathology informatics · 2025Review
- Strategies for the drug development of cancer therapeutics.Frontiers in pharmacology · 2025Review
- Quantum Dots in Cancer Theranostics: A Thorough Review of Recent Advancements in Bioimaging, Tracking, and Therapy Across Various Cancer Types.Current pharmaceutical biotechnology · 2025Review
- Drug Repurposing: A Conduit to Unravelling Metabolic Reprogramming for Cancer Treatment.Mini reviews in medicinal chemistry · 2025Review
- Unraveling the Anti-Cancer Mechanisms of Antibiotics: Current Insights, Controversies, and Future Perspectives.Antibiotics (Basel, Switzerland) · 2024Review
- Insight into the structural and dynamic properties of novel HSP90 inhibitors through DFT calculations and molecular dynamics simulations.Journal of molecular modeling · 2024Article
- Artificial Intelligence-Driven Computational Approaches in the Development of Anticancer Drugs.Cancers · 2024Article
- Article
- 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
- Advances and applications of biomimetic biomaterials for endogenous skin regeneration.Bioactive materials · 2024Review
- Green Extraction of Plant Materials Using Supercritical COPlants (Basel, Switzerland) · 2024Review
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
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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.