ArticleBioinformatics (Oxford, England)2024
Revisiting drug-protein interaction prediction: a novel global-local perspective.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
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- ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.BMC bioinformatics · 2025Article
- Denoising self-supervised learning for disease-gene association prediction.BMC bioinformatics · 2025Article
- Cancer detection via one-shot learning: integrating gene expression and genomic mutation analysis.BMC bioinformatics · 2025Article
- Drug repurposing for Alzheimer's disease using a graph-of-thoughts based large language model to infer drug-disease relationships in a comprehensive knowledge graph.BioData mining · 2025Article
- QKDTI A quantum kernel based machine learning model for drug target interaction prediction.Scientific reports · 2025Article
- SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.ACS omega · 2025Article
- Geometry-enhanced graph neural networks accelerate circRNA therapeutic target discovery.Frontiers in genetics · 2025Article
- Deciphering metabolic disease mechanisms for natural medicine discovery via graph autoencoders.Frontiers in pharmacology · 2025Article
- DMAGCL: A dual-masked adaptive graph contrastive learning framework for predicting circRNA-drug sensitivity.Frontiers in genetics · 2025Article
- Computational discovery of natural medicines targeting adenosine receptors for metabolic diseases.Frontiers in pharmacology · 2025Article
- IRGL-RRI: interpretable graph representation learning for plant RNA-RNA interaction discovery.Frontiers in plant science · 2025Article
- Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding.BMC genomics · 2024Article
- Multimodal multiobjective optimization with structural network control principles to optimize personalized drug targets for drug discovery of individual patients.Briefings in bioinformatics · 2024Article
- MKAN-MMI: empowering traditional medicine-microbe interaction prediction with masked graph autoencoders and KANs.Frontiers in pharmacology · 2024Article
- Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines.Frontiers in pharmacology · 2024Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
motivationAccurate inference of potential drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance.
resultsWe propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multilayer perceptrons to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach is expected to offer valuable insights for furthering drug repurposing and personalized medicine research. AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.
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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.