ArticleCancers2024
Artificial Intelligence-Driven Computational Approaches in the Development of Anticancer Drugs.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- Progress on quinoline‑based antitumour agents: Mechanistic insights and optimization strategies (Review).Molecular medicine reports · 2026Review
- The potential of harmaline on triple negative breast cancer: harmaline-DNA interaction related apoptosis, anti-metastasis, and in silico analysis.Histochemistry and cell biology · 2026Article
- Comprehensive overview of AI methodologies in nano-drug delivery Optimization and Design.NPJ precision oncology · 2026Review
- Dihydroergotamine alleviates circadian rhythm disorders through predicted interaction with to CRY1.Molecular diversity · 2026Article
- Article
- From Molecular Networks to Medicines: Targeting Complexity in Alzheimer's Disease (AD) Therapy.Molecular neurobiology · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Integrated Computer-Aided Drug Design: Advances in GPCR Natural Ligand Discovery.Cell biochemistry and biophysics · 2026Review
- AI accelerate the identification of druggable targets by 3D structures of proteins and compounds.NPJ precision oncology · 2026Review
- mRNA vaccines in cancer immunotherapy: current progress and perspectives in solid tumors and hematologic malignancies.MedScience · 2026Review
- Molecular docking approaches in mycetoma: Toward improved patient management.PLoS neglected tropical diseases · 2026Review
- The Molecular Architecture of Neurodegeneration: An Integrative Overview of Convergent Mechanisms.NeuroSci · 2026Review
- AI-driven multi-omics integration in precision oncology: bridging the data deluge to clinical decisions.Clinical and experimental medicine · 2025Review
- Computer-Aided Drug Design Across Breast Cancer Subtypes: Methods, Applications and Translational Outlook.International journal of molecular sciences · 2025Review
- Review
- Breaking Barriers in Breast Cancer: Multi-Targeted Therapeutic Insights.Mini reviews in medicinal chemistry · 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
6 authors.
Funding
Abstract
The integration of AI has revolutionized cancer drug development, transforming the landscape of drug discovery through sophisticated computational techniques. AI-powered models and algorithms have enhanced computer-aided drug design (CADD), offering unprecedented precision in identifying potential anticancer compounds. Traditionally, cancer drug design has been a complex, resource-intensive process, but AI introduces new opportunities to accelerate discovery, reduce costs, and optimize efficiency. This manuscript delves into the transformative applications of AI-driven methodologies in predicting and developing anticancer drugs, critically evaluating their potential to reshape the future of cancer therapeutics while addressing their challenges and limitations.
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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.