ReviewFrontiers in pharmacology2026
Research progress of artificial intelligence in high-throughput drug screening.
Review in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- m6A-Targeted Cancer Therapy: Molecular Targets, Inhibitors, and Nanodelivery Strategies.International journal of nanomedicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-throughput screening (HTS) is widely used in modern drug discovery. It enables batch activity testing of compounds and provides important support for the identification of active compounds. However, its screening efficiency and accuracy need to be improved. To address this issue, artificial intelligence (AI) has been gradually integrated into the HTS workflow. Leveraging the advantages of machine learning (ML) and deep learning (DL), AI optimizes applications in structure-based and ligand-based virtual screening, combination drug screening, image analysis, and post-screening data analysis and interpretation, driving the intelligent development of drug discovery. This paper reviews recent research progress in the application of AI in HTS, discusses the implementation of machine learning models, and summarizes key AI applications in HTS-related compound screening, image recognition, and hit identification from complex screening data, aiming to accelerate the development of innovative drugs.
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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.