ReviewPhysiological research2021
The potential applications of artificial intelligence in drug discovery and development.
Review in Physiological research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Machine Learning and Virtual Screening Methods to Discover Potential Cyclin-Dependent Kinase 2 (CDK2) Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- From Molecular Networks to Medicines: Targeting Complexity in Alzheimer's Disease (AD) Therapy.Molecular neurobiology · 2026Review
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Towards advanced regenerative therapeutics to tackle cardio-cerebrovascular diseases.American heart journal plus : cardiology research and practice · 2025Review
- Genetic and Cheminformatic Characterization ofACS infectious diseases · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- A machine learning method for predicting molecular antimicrobial activity.Scientific reports · 2025Article
- Advanced Technologies for Large Scale Supply of Marine Drugs.Marine drugs · 2025Review
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
- AI in fungal drug development: opportunities, challenges, and future outlook.Frontiers in cellular and infection microbiology · 2025Review
- The impact of artificial intelligence on drug discovery for neuropsychiatric disorders.EXCLI journal · 2025Review
- A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases.Biomolecules · 2024Review
- Review
- Tribulations and future opportunities for artificial intelligence in precision medicine.Journal of translational medicine · 2024Review
- Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery.Life (Basel, Switzerland) · 2024Review
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological studies include a multiple of in silico, in vitro, in vivo experimentations that traditionally last several years. In the present review, we briefly report some examples that demonstrate the power of the computer-assisted drug discovery process with some examples that are published and revealing the successful applications of artificial intelligence (AI) technology on this vivid area. Besides, we address the situation of drug repositioning (repurposing) in clinical applications. Yet few success stories in this regard that provide us with a clear evidence that AI will reveal its great potential in accelerating effective new drug finding. AI accelerates drug repurposing and AI approaches are altogether necessary and inevitable tools in new medicine development. In spite of the fact that AI in drug development is still in its infancy, the advancements in AI and machine-learning (ML) algorithms have an unprecedented potential. The AI/ML solutions driven by pharmaceutical scientists, computer scientists, statisticians, physicians and others are increasingly working together in the processes of drug development and are adopting AI-based technologies for the rapid discovery of medicines. AI approaches, coupled with big data, are expected to substantially improve the effectiveness of drug repurposing and finding new drugs for various complex human diseases.
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.