ArticleProteins2025
PCANN Program for Structure-Based Prediction of Protein-Protein Binding Affinity: Comparison With Other Neural-Network Predictors.
Article in Proteins, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Hybrid Approach to Protein-Protein Complex Affinity Prediction Based on Language Models and Molecular Dynamics.International journal of molecular sciences · 2026Article
- BindPred: a framework for predicting protein-protein binding affinity from language model embeddings.Bioinformatics (Oxford, England) · 2026Article
- PCANN Program for Structure-Based Prediction of Protein-Protein Binding Affinity: Comparison With Other Neural-Network Predictors.Proteins · 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
4 authors.
Funding
Abstract
In this communication, we introduce a new structure-based affinity predictor for protein-protein complexes. This predictor, dubbed PCANN (Protein Complex Affinity by Neural Network), uses the ESM-2 language model to encode the information about protein binding interfaces and graph attention network (GAT) to parlay this information into
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.