ReviewMolecular diversity2024
Construction of IRAK4 inhibitor activity prediction model based on machine learning.
Review in Molecular diversity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed.
- Machine learning-driven prioritization and experimental validation of traditional Chinese medicine-derived STING-inhibitory candidates.Molecular diversity · 2026Article
- XGBPred-ACSM: A Hybrid Descriptor-Driven XGBoost Framework for Anticancer Small Molecule Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- An integrated approach for novel PTP1B inhibitor screening: combining machine learning models, molecular docking, molecular and dynamics simulations.Molecular diversity · 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
- Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.Molecular diversity · 2025Article
- Development and validation of a new diagnostic prediction model for NAFLD based on machine learning algorithms in NHANES 2017-2020.3.Hormones (Athens, Greece) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Interleukin-1 receptor-associated kinase 4 (IRAK4) is a crucial serine/threonine protein kinase that belongs to the IRAK family and plays a pivotal role in Toll-like receptor (TLR) and Interleukin-1 receptor (IL-1R) signaling pathways. Due to IRAK4's significant role in immunity, inflammation, and malignancies, it has become an intriguing target for discovering and developing potent small-molecule inhibitors. Consequently, there is a pressing need for rapid and accurate prediction of IRAK4 inhibitor activity. Leveraging a comprehensive dataset encompassing activity data for 1628 IRAK4 inhibitors, we constructed a prediction model using the LightGBM algorithm and molecular fingerprints. This model achieved an R
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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.