ReviewThe AAPS journal2022
Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review.
Review in The AAPS journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 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
76 citing papers in PubMed.
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- Multi-task learning for forensic age and sex estimation using large foundation models on pelvic radiographs: a multi-center study.International journal of legal medicine · 2026Article
- From API to Action: A Multi-Model Comparison of OpenAI, Anthropic, Google, and Meta LLMs for Clinical Trial Data Extraction.Bioengineering (Basel, Switzerland) · 2026Article
- From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- AI in Drug Discovery: Clinical Failures, Regulatory Reality, and the Validation Crisis Behind the Hype.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Harnessing Machine Learning for Accelerated Drug Discovery: Opportunities and Unmet Challenges.Pharmaceuticals (Basel, Switzerland) · 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
- Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.Acta pharmaceutica Sinica. B · 2026Review
- The landscape of nanomedical clinical trials.Nano today · 2026Article
- The Use of Digital Neurocognitive Assessments to Assess Traumatic Brain Injury and Dementia in Older Trauma Patients: An Emergency Department Feasibility Study.Diagnostics (Basel, Switzerland) · 2026Article
- Specification Limit Normalization for Nonparametric Process Capability Analysis and Its Application in Cleaning Validation with AI-Enabled Monitoring Models.AAPS PharmSciTech · 2026Article
- Impact of drug repurposing between 1985 and 2024 on pharmaceutical innovation.Communications medicine · 2026Article
- Designing novel FAK inhibitors targeting gastric cancer: a combined approach using machine learning, docking analysis, molecular dynamics simulations, and experimental validation.Journal of computer-aided molecular design · 2026Article
- Development and internal validation of machine learning models for gouty arthritis classification using routine clinical variables: a retrospective study.Frontiers in medicine · 2026Article
- Medication adherence trajectories and association with risk factors and clinical outcomes in type 2 diabetes treatment.PloS one · 2026Article
- Computational Innovations in Cancer Research and How Computing is Transforming Drug Discovery and Development: A Review.Mini reviews in medicinal chemistry · 2026Review
- Artificial Intelligence Revolution in Pharmaceutical Sciences : Advancements, Clinical Impacts, and Applications.Current pharmaceutical biotechnology · 2026Review
- Explainable AI-driven precision clinical trial enrichment: demonstration of the NetraAI platform with a phase II depression trial.NPJ digital medicine · 2025Article
- Review
- Key Stakeholders' Knowledge, Opinions, and Interests on Real-World Evidence in the Regulatory Process-Results of an EU-Wide Survey.Clinical and translational science · 2025Article
16 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development (R&D). This is partially driven by revolutionary advances in computational technology and the parallel dissipation of previous constraints to the collection/processing of large volumes of data. Meanwhile, the cost of bringing new drugs to market and to patients has become prohibitively expensive. Recognizing these headwinds, AI/ML techniques are appealing to the pharmaceutical industry due to their automated nature, predictive capabilities, and the consequent expected increase in efficiency. ML approaches have been used in drug discovery over the past 15-20 years with increasing sophistication. The most recent aspect of drug development where positive disruption from AI/ML is starting to occur, is in clinical trial design, conduct, and analysis. The COVID-19 pandemic may further accelerate utilization of AI/ML in clinical trials due to an increased reliance on digital technology in clinical trial conduct. As we move towards a world where there is a growing integration of AI/ML into R&D, it is critical to get past the related buzz-words and noise. It is equally important to recognize that the scientific method is not obsolete when making inferences about data. Doing so will help in separating hope from hype and lead to informed decision-making on the optimal use of AI/ML in drug development. This manuscript aims to demystify key concepts, present use-cases and finally offer insights and a balanced view on the optimal use of AI/ML methods in R&D.
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.