ArticleDrug design, development and therapy2023
The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives.
Article in Drug design, development and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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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
49 citing papers in PubMed.
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- Artificial intelligence-driven digital twins in Pharma 4.0: transforming smart manufacturing, predictive quality assurance, and personalized drug delivery.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2026Review
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- Application of Artificial Intelligence in MedDRA Coding: A Practical Exploration from Clinical Data Management Perspective.Therapeutic innovation & regulatory science · 2026Article
- Digital Twin Technology In Radiology.Journal of imaging informatics in medicine · 2026Review
- Dynamic Personalized Optimization: An AI Functionality Framework for Digital Therapeutics.JMIR medical informatics · 2026Article
- AI for scientific discovery is a social problem.Patterns (New York, N.Y.) · 2026Review
- Specification Limit Normalization for Nonparametric Process Capability Analysis and Its Application in Cleaning Validation with AI-Enabled Monitoring Models.AAPS PharmSciTech · 2026Article
- High-throughput yeast engineering in biofoundries: towards autonomous and scalable synthetic biology.FEMS yeast research · 2026Review
- Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.Frontiers in pharmacology · 2026Review
- Quantum Computing in Drug Discovery Techniques, Challenges, and Emerging Opportunities.Current drug discovery technologies · 2026Review
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- Drug Discovery from Gene Expression/Gene Regulation.Methods in molecular biology (Clifton, N.J.) · 2026Review
- An overview of CRISPR-artificial intelligence theranostics: Current and emerging applications.Biomaterials translational · 2026Review
- Artificial intelligence-assisted neuropharmacology: reshaping drug discovery and translational strategies for neurological diseases.Turkish journal of medical sciences · 2026Review
- The Human Omnibus of Targetable Pockets.Journal of cheminformatics · 2025Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- A New Era of Artificial Intelligence (AI): Transforming Drug Discovery and Development.Journal of medicinal chemistry · 2025Article
- The covalent docking software landscape: features and applications in drug design.Briefings in bioinformatics · 2025Review
- Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes.Pharmaceuticals (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
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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.