ReviewLife (Basel, Switzerland)2024
Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery.
Review in Life (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 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
88 citing papers in PubMed.
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- Emerging diagnostic biomarkers and therapeutic targets in Alzheimer's disease.Inflammopharmacology · 2026Review
- Closing the Translational Gap: Closed-Loop AI Discovery Frameworks for Experimental Validation and Clinical Implementation in Cancer Therapeutics.Cancer medicine · 2026Review
- Pharmaceutical Compounding as a Pillar of Personalized Oncology: Current Applications, Emerging Technologies, and Future Perspectives.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Recent Advances in Organofluoroprobes for Brain Imaging: Progress, Challenges, and Future Directions.ACS pharmacology & translational science · 2026Review
- SMASH: Screening Molecules Accurately on Small Hardware. Fast, user-friendly, enhanced with a machine learning virtual screening tool.Journal of molecular modeling · 2026Article
- Machine Learning and Virtual Screening Methods to Discover Potential Cyclin-Dependent Kinase 2 (CDK2) Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Generative AI in healthcare: redefining clinical practice through digital transformation.Health economics review · 2026Article
- Clinical combination therapy for IBD and promising co-delivery strategies.Inflammopharmacology · 2026Review
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial intelligence in drug research and development: a review of methods and applications in drug repurposing.Briefings in bioinformatics · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery.International journal of molecular sciences · 2026Review
- AI-Driven Plant-Derived Anti-Infectives: Integrating Traditional Wisdom into Precision Medicine Against AMR.Life (Basel, Switzerland) · 2026Review
- A Data-Driven Approach for Interpretable and Efficient Predictive Modeling: A Case Study in SARS-CoV-2 Protease Inhibitor Discovery Through Feature Selection.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Rethinking Nature's Pharmacy: AI Era and Natural Product Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Breaking resistance: strategies for novel antibacterial therapeutic interventions.Molecular biology reports · 2026Review
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- Integrating traditional and modern approaches for comprehensive pharmacophore map validation in drug discovery.Journal of computer-aided molecular design · 2026Review
28 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.
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.