ArticleFrontiers in chemistry2019
Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods.
Article in Frontiers in chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 34 citations in OpenAlex.
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- Integrated Transcriptomic Analysis of S100A8/A9 as a Key Biomarker and Therapeutic Target in Sepsis Pathogenesis and AI Drug Repurposing.International journal of molecular sciences · 2025Article
- Unraveling the Mysteries of Alzheimer's Disease Using Artificial Intelligence.Reviews on recent clinical trials · 2025Review
- Structure-Based QSAR Modeling of RET Kinase Inhibitors from 49 Different 5,6-Fused Bicyclic Heteroaromatic Cores to Patent-Driven Validation.ACS omega · 2024Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Prediction of chemical warfare agents based on cholinergic array type meta-predictors.Scientific reports · 2022Article
- Review
- Small Molecular Drug Screening Based on Clinical Therapeutic Effect.Molecules (Basel, Switzerland) · 2022Article
- Review
- The Road to Personalized Medicine in Alzheimer's Disease: The Use of Artificial Intelligence.Biomedicines · 2022Review
- Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors.International journal of molecular sciences · 2021Article
- High-throughput screening assays for SARS-CoV-2 drug development: Current status and future directions.Drug discovery today · 2021Review
- Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents.Molecular diversity · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era.Current oncology (Toronto, Ont.) · 2021Review
- Thermodynamics-Based Model Construction for the Accurate Prediction of Molecular Properties From Partition Coefficients.Frontiers in chemistry · 2021Article
- Application of artificial intelligence to the diagnosis and therapy of colorectal cancer.American journal of cancer research · 2020Review
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
S100A9 is a potential therapeutic target for various disease including prostate cancer, colorectal cancer, and Alzheimer's disease. However, the sparsity of atomic level data, such as protein-protein interaction of S100A9 with RAGE, TLR4/MD2, or CD147 (EMMPRIN) hinders the rational drug design of S100A9 inhibitors. Herein we first report predictive models of S100A9 inhibitory effect by applying machine learning classifiers on 2D-molecular descriptors. The models were optimized through feature selectors as well as classifiers to produce the top eight random forest models with robust predictability and high cost-effectiveness. Notably, optimal feature sets were obtained after the reduction of 2,798 features into dozens of features with the chopping of fingerprint bits. Moreover, the high efficiency of compact feature sets allowed us to further screen a large-scale dataset (over 6,000,000 compounds) within a week. Through a consensus vote of the top models, 46 hits (hit rate = 0.000713%) were identified as potential S100A9 inhibitors. We expect that our models will facilitate the drug discovery process by providing high predictive power as well as cost-reduction ability and give insights into designing novel drugs targeting S100A9.
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.