ArticleArtificial intelligence review2022
Machine Learning in Drug Discovery: A Review.
Article in Artificial intelligence review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 237 papers, 3 of them syntheses that pooled it.
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
237 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects.Drug design, development and therapy · 2025Pooled it
- Application of machine learning in measurement of ageing and geriatric diseases: a systematic review.BMC geriatrics · 2023Pooled it
- Drug repurposing on Alzheimer's disease through modulation of NRF2 neighborhood.Redox biology · 2023Pooled it
- Preclinical models of COPD: a state of the art review.The European respiratory journal · 2026Review
- Reimagining computational macromolecular modeling: AI-driven approaches.Biophysical journal · 2026Review
- The innovation disconnect: Can artificial intelligence finally align academic discovery with industry needs?Indian journal of pharmacology · 2026Article
- From Executor to Orchestrator: The Pharmacology Scientist in the Age of Agentic AI.Clinical pharmacology and therapeutics · 2026Review
- MMP2Mol: a matched molecular pairs-based framework for ligand-based de novo drug design.Briefings in bioinformatics · 2026Article
- Identification of novel serotonin transporter (SERT) inhibitors via large-scale machine learning-based virtual screening and molecular dynamics simulations.Molecular diversity · 2026Article
- Cloud-based ligand-guided virtual screening with deep-learning-enhanced docking identifies a micromolar CXCR4 antagonist.Molecular diversity · 2026Article
- Computationally Generated Plant-Derived Berberine-Based Hybrid Compounds as Potential Dual Binders toInternational journal of molecular sciences · 2026Article
- Machine learning-based prediction of SARS-CoV-2 bioactivity: integrating ICBMC bioinformatics · 2026Article
- ASCENT: an active transfer learning paradigm for efficient drug-target interaction prediction.Science China. Life sciences · 2026Article
- Artificial Intelligence in Pharmaceutical Care:Saudi medical journal · 2026Review
- LeGenD: High-throughput N-glycan profiling using explainable AI and lectin profiling.The Journal of biological chemistry · 2026Article
- Intelligence on the graph: Graph neural networks for mechanistic drug target discovery.Journal of pharmaceutical analysis · 2026Review
- AI-guided identification of natural CTSL inhibitors with therapeutic potential for renal injury.PLoS computational biology · 2026Article
- Overrepresentation Bias Leads to Performance Overestimation in Blood-Brain Barrier Permeability Prediction Models: Characterization and Mitigation.Journal of chemical information and modeling · 2026Article
- Methods for Detecting Suspicious Information From Individual Transactions of Pharmaceutical Products via Twitter (now X): Retrospective Observational Study.Journal of medical Internet research · 2026Observational
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
177 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
This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.
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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.