ArticleScientific reports2025
TCoCPIn reveals topological characteristics of chemical protein interaction networks for novel feature discovery.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Support field neural representation learner framework for learning stability landscapes in molecular geometry.npj biomedical innovations · 2025Article
- Multi-Modal Topology-Aware Graph Neural Network for Robust Chemical-Protein Interaction Prediction.International journal of molecular sciences · 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
2 authors.
Funding
Abstract
Understanding chemical-protein interactions (CPIs) is crucial for drug discovery and biological research, yet their complexity often challenges traditional methods. We propose TCoCPIn, a novel framework integrating graph neural networks (GNN) with the comprehensive topological characteristics index (CTC), which combines multiple topological metrics to enhance predictive accuracy. TCoCPIn significantly outperforms traditional and embedding-based methods, achieving higher accuracy, precision, and recall in CPI prediction. A case study highlights its ability to predict potential interactions, such as between ibuprofen and TNF-alpha, demonstrating its utility in identifying novel therapeutic targets. These findings illustrate TCoCPIn's potential to uncover hidden associations and key nodes in CPI networks, providing new opportunities for drug discovery and disease mechanism exploration. Future work will focus on experimental validation and expanding the application of TCoCPIn to other biological systems.
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.