ArticleACS medicinal chemistry letters2024
Supervised Screening of EGFR Inhibitors Validated through Computational Structural Biology Approaches.
Article in ACS medicinal chemistry letters, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Computational theranostics strategy for pancreatic ductal adenocarcinoma.Molecular diversity · 2026Article
- Advances in genomic and pharmacokinetic profiling for clinical stratification of metastatic breast cancer.Discover oncology · 2025Article
- A deep learning model for epidermal growth factor receptor prediction using ensemble residual convolutional neural network.Scientific reports · 2025Article
- Network toxicology reveals glyphosate mechanisms in kidney injury and cancer.Scientific reports · 2025Article
- Integrative systems biology and in-vitro analysis of cryptolepine's therapeutic role in breast cancer.Discover oncology · 2025Article
- Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions.Bioelectronic medicine · 2025Review
- Theoretical investigation of AKT1 mutations in breast cancer: a computational approach to structural and functional insights.Journal of computer-aided molecular design · 2025Article
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
One of the prominent challenges in breast cancer (BC) treatment is human epidermal growth factor receptor (EGFR) overexpression, which facilitates tumor proliferation and presents a viable target for anticancer therapies. This study integrates multiomics data to pinpoint promising therapeutic compounds and employs a machine learning (ML)-based similarity search to identify effective alternatives. We used BC cell line data from the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases and single-cell RNA sequencing (scRNA-seq) information that established afatinib as an efficacious candidate demonstrating superior IC
Identifiers
What Socratic 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 Socratic graph.