ArticlemicroPublication biology2026
gPPIpred: A User-Friendly PPI Predictor Based on Protein Molecular Graphs.
Article in microPublication biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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Authors and funding
3 authors.
Funding
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Abstract
Protein-protein interactions (PPIs) govern essential cellular processes but remain challenging to characterize experimentally due to high cost and labor intensity. We present gPPIpred, a scalable computational framework leveraging graph neural networks (GNNs) and attention mechanisms to predict PPIs at residue-level resolution. Proteins are encoded as spatially informed molecular graphs integrating physicochemical features. Using curated structural datasets for training and validation, gPPIpred was fine-tuned to reliably predict positive interactions and actual interacting sites. Attention scores highlight key residues mediating interactions, offering interpretable insights to guide experimental design. gPPIpred combines high predictive performance with explainability, providing a user-friendly pipeline for large-scale PPI discovery.
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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.