ReviewDrug design, development and therapy2025
Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design.
Review in Drug design, development and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Combining cutting edge computational and experimental methods for targeting KRAS mutations in non-small cell lung cancer.Expert opinion on drug discovery · 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
- Review
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Recent applications of artificial intelligence in cancer radiotherapy and immunotherapy: current status and future directions.Frontiers in immunology · 2026Review
- Recent Advances and Emerging Directions in Machine Learning-Based Breast Cancer Drug Discovery: A Comprehensive Review.Breast cancer (Dove Medical Press) · 2026Review
- Integrating New Approach Methodologies (NAMs) into Preclinical Regulatory Evaluation of Oncology Drugs.Biomimetics (Basel, Switzerland) · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of artificial intelligence (AI) into oncology drug discovery is redefining the traditional pipeline by accelerating discovery, optimizing drug efficacy, and minimizing toxicity. AI has enabled groundbreaking advancements in molecular modeling, simulation techniques, and the identification of novel compounds, including anti-tumor and antibodies, while elucidating mechanisms of drug toxicity. Additionally, AI has emerged as a critical tool in precision medicine, driving the formulation and release of targeted therapies and improving the development of treatments for oncology and central nervous system diseases. Furthermore, AI-assisted clinical trial designs have further optimized the recruitment and stratification of patients, reducing the time and cost of trials. Despite these advancements, challenges such as data integration, transparency, and ethical considerations persist. By synthesizing current innovations, this manuscript provides a comprehensive analysis of AI-driven approaches in drug discovery and their potential to advance oncology therapeutics and precision medicine. It examines the transformative role of AI across the drug development continuum, with a focus on its applications in computer-aided drug design (CADD), generative artificial intelligence (GAI), and high-throughput screening (HTS).
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.